MAExplorer - Microarray Exploratory Data Analysis

Reference Manual++ - MAExplorer Microarray
Exploratory Data Analysis

The MicroArray Explorer Java tool for data mining microarrays **Legend
(Click on figures to show higher resolution versions) 

 


*** DRAFT ***

 $Date: 2003/11/23 10:29 $ 
 MicroArray Explorer-V0.96.34.01
 "Millenium" edition


 
Peter F. Lemkin

Laboratory of Experimental and Computational Biology

Center for Cancer Research, National Cancer Institute - Frederick,
Frederick, MD 21702






















MAExplorer home |
Table of Contents |
 Overview |
Introduction |
Menu summary |
PDF documents | 

Newsletters | 


Plugins |
Quick start |
Short tutorial |
Advanced Tutorial |
Glossary |
Figures |
Tables |
Index |
Help desk





















SourceForge    

http://maexplorer.sourceforge.net/ 

    or

 LECB/NCI        

http://www.lecb.ncifcrf.gov/MAExplorer























++ Note: This hypertext manual is divided into chapters and
appendices Web pages.  These may be printed individually from your Web
browser by (1) clicking in the text window to be printed, and (2)
using the "Print Frame" in Netscape or "Print" in Internet
Explorer. Some of the chapters (eg. 2) have many images.  The  entire manual may be downloaded at one
time with low resolution figures and is suitable for printing in the
Web browser.  You may also download a an Adobe acrobate PDF file version of the entire
manual with the lower resolution figures (~5Mb). The Unix script for
creating the full reference manual from the individual HTML pages is

CreateMaeFullRefManual.do.









The MAExplorer is a Java-based bioinformatics exploratory
data-analysis and data-mining program for analyzing sets of
quantitative spotted cDNA or oligonucleotide microarray data (Lemkin et al., 2000) - (see (Schulze, 2001) for a review of
microarray technology).  


Prior to its release on SourceForge, MAExplorer was developed by
Dr. Peter Lemkin (LECB/NCI-Frederick) with help from Gregory Thornwall
(SAIC) and Jai Evans (DECA/CIT, NIH). It was initially created for
analyzing 33P labeled membrane array data from the mouse
mammary tissue from Mammary Genome Anatomy Project (MGAP) 
http://mammary.nih.gov/ with the help of many researchers in the
Laboratory of Genetics and Physiology, NIDDK under Dr. Lothar
Hennighausen.  Since the early work with MGAP it was extended to work
with other types of cDNA and oligo arrays and various nucleotide
labeling methods.  These include spotted Cy3/Cy5 glass slides, spotted
membranes, non-geometric chip data, and other chip supports with
different geometries and numbers of duplicate spots/gene, clones as
well as oligo chip data such as Affymetrix.  A wizard tool called Cvt2Mae was developed to make
it easier for other researchers to convert their data to the format
required by MAExplorer. Cvt2Mae was developed by Peter Lemkin, Greg
Thornwall and Bob Stephens (ABCC/SAIC). You may extend the set of
builtin analysis methods by writing Java plugins called  MAEPlugins.



This document describes the MAExplorer's functionality, provides
tutorials and contains documentation for using it with various types
arrays.


With this program, you may: 1) analyze expression of individual 
genes; 2) analyze expression of gene families and clusters; 3) 
compare expression patterns for multiple hybridized samples. 



MAExplorer is written in Java and runs as a stand-alone application
that you download to your
computer.  Although MAExplorer began out as a Java applet for use with
with Web browsers for the MGAP Web database (
http://www.lecb.ncifcrf.gov/mae ), we have depricated its use as
an applet because of many problems with running large Java applets in
 some Web browsers.  Instead,
we recommend downloading MAExplorer which includes the public MGAP array data as a demonstration
data set. Then run MAExplorer on this data after you have installed it
on your computer. 









Notation: MAExplorer uses the notation that the sample
probe total mRNA is labeled and then hybridized against the
known cDNA targets tethered to the microarray.  Because of this
notation, we refer to a hybridized sample as a HP.  An alternative
notation that reverses these terms is also commonly used (see 
"Chipping Forecast", Nature Genetics supplement, Jan, 1999,
pg 1). Also, because arrays may be constructed from either spotted
clones or oligonucleotides, we refer to hybridized chip DNA from any
of these sources genericlly as "genes".






 


Throughout this document we use the  abbreviations HP for
hybridized sample, GC for gene class. These and other terms are
explained in the Glossary
and Index . There are a number of figures and tables illustrating various
features of MAExplorer throughout this manual. Figures are presented at
low-resolution. By clicking on the lower-resolution figure, the
high-resolution versions can be viewed.









NOTES: because MAExplorer is under development, there may be
occasional problems with some of its functionality. There may also be
some problems (mostly bad HTML links) with migrating from LECB/NCI to
the SourceForge Web site.  Some operations that are under development
are labeled with "[Future]" in this manual.  We welcome your
suggestions for improvements as well as letting us know about problems
that you encounter.  Occasionally the manual or the figures in the
manual may not be quite in phase with the software.  Please notify us
of problems or suggestions by 
E-mail so we can try to fix or implement them. If you are a
bioinformatics developer and would be interested on working with the
MAExplorer project, consider joining the

MAExplorer development team on SourceForge.net.











**Icon Legend


Data from a 38 sample subset of hybridized samples from the MGAP mouse microarray
database. This screen illustrates a synthetic pseudoarray image
showing the ratios of duplicated grids of genes comparing day 13
pregnancy in C57B6 mouse (sample HP-X 'set') with Lactation day 1
(sample HP-Y 'set').  The color scale of the spots is indicated on the
left as is the current data normalization mode (median).  Genes with
white circles are named genes and were selected by the data filter. A
scatter plot of this data is shown on the right with genes passing the
data filter indicated as red + and those not passing the filter
(i.e. ESTs, calibration DNA, user's genes) shown as gray + symbols. A
single gene was selected by clicking on it in the array image and has
a yellow circle (grid 1-D) and a corresponding green circle in the
scatter plot. Information on that gene is indicated above the array
and at the top of the scatter plot. MAExplorer can also be used to
view mean data from sets of samples e.g. Day 13 pregnancies
from C57B6 (3 HP-X samples) vs. Day 1 Lactation (4 HP-Y samples) at low or high resolution.










Table of Contents





Overview

Menu summary

Quick start



1. Introduction

1.1 Microarrays and notation used with MAExplorer


1.2 Microarray image quantification

   1.2.1 
Ratio and Zscore comparison of data from different hybridized samples


1.3 Microarray image and plot display


1.4 Exploratory data analysis - overview

   1.4.1 
Saving the state of a data-mining session in stand-alone mode 

   1.4.2 
Logging messages and command history 


1.5 Quick start - demonstration of MAExplorer

1.6 Tutorials for using MAExplorer







2. MAExplorer menus

2.1 File menu

   2.1.1 
 Databases menu

   2.1.2 
 Exploratory state menu

   2.1.3 
 Groupware facility for sharing user states menu


2.2 Samples menu

   2.2.1 
 Selecting sample HP with chooser or menu sample lists

   2.2.2 
 Swapping selected samples's (Cy3,Cy5) channels in ratio data 
dye-swap experiments

   2.2.3 
Viewing sample HP-X, HP-Y, and HP-E partitions

   2.2.4 
Defining sample condition 'class' names

   2.2.5 
Toggling between single HP-X (-Y) samples and HP-X (-Y) sets


2.3 Edit menu

   2.3.1 
User edited gene list - the 'Edited Gene List' menu

   2.3.2 
Sets of genes menu

   2.3.3 
Sets of Sample Conditions menu

   2.3.4 
Setting user preferences menu


2.4 Analysis

   2.4.1 GeneClass menu

      2.4.1.1 
GeneClass ontology subsets

      2.4.1.2 
Simulating Gene Class ontologies using Gene Set operations


   2.4.2 Normalization menu

      2.4.2.1 
Intensity background correction

      2.4.2.2 
Normalization between microarrays to allow comparison

      2.4.2.3 
Using different normalizations to 'see' different data views


   2.4.3 Filter menu

      2.4.3.1 
  Data filtering using multiple gene data filters


   2.4.4 Plot menu

      2.4.4.1 
  Show microarray pseudoarray images menu

      2.4.4.2 
 Scatter plots menu

      2.4.4.3 
 Histogram plots menu

      2.4.4.4 
 Expression profile plots menu


   2.4.5 
  Cluster menu

      2.4.5.1 
  Cluster genes with expression profiles similar to current gene

      2.4.5.2 
  Cluster counts of similar filtered genes by expression profiles

      2.4.5.3  
  K-means clustering' gene expression profiles for filtered genes

      2.4.5.4 
  Hierarchical clustering of expression profiles


   2.4.6 Report menu

      2.4.6.1 
  Array report menu - hybridized samples global data

      2.4.6.2 
  Gene reports menu

      2.4.6.3 
  Table format menu

      2.4.6.4 
  Table font size menu


2.5 View menu

   2.5.1 
Logging MAExplorer messages

   2.5.2 
Logging command history


2.6 Plugins menu

2.7 Help menu





3. 
  Exploratory Data Analysis - Data Mining

3.1 Analysis objectives

   3.1.1 
  Some experimental design issues of microarray experiments

   3.1.2 
  Design philosophy of MAExplorer methodology

   3.1.3 
  Evolution of MAExplorer from earlier proteomic data mining systems

   3.1.4 
  Concepts used in data mining with MAExplorer


3.2 
  Steps in an analysis

   3.2.1 
  Definition of expression profile

   3.2.2 
  Clustering Methods

      3.2.2.1 
  Clustering similar genes

      3.2.2.2 
  K-means clustering

      3.2.2.3 Hierarchical clustering


3.3 
  Display gene intensity and identification data measurements

3.4 
  Selecting subsets of genes using the data Filter

3.5 
  Selecting subsets of hybridized sample conditions

3.6 
  Setting threshold values using the state-scroller sliders

3.7 
  Exporting report and plot data




4.  Status and Bugs of MAExplorer

4.1 Known Bugs in MAExplorer

   4.1.1 Browser Applet Bugs

   4.1.2 Downloading and Installer Bugs

   4.1.3 Computation speed and display Bugs

   4.1.4 User state and login Status

   4.1.5 Data file names Bug

   4.1.6 Gene Sets Bugs

   4.1.7 Clustering Bugs

   4.1.8 Expression profile Bugs

   4.1.9 Data conversion problems

   4.1.10 Java Plugins bugs


4.2 Revision notes

4.3 
  Web Browser problems when running MAExplorer as an applet

4.4 
  Handling fatal error reporting (i.e. DRYROT errors)




Release archive


Acknowedgments


References to related exploratory data analysis methods

R.1 Nucleic Acids Res. paper (PDF)

R.2 Overview (PDF)

R.3 Examples (PDF)

R.4 Using mAdb data with MAExplorer (PDF)

R.5  Introduction to Data Mining with MAExplorer(PDF) or
(PPT)

R.6  Using Cvt2Mae to convert array data for use with MAExplorer.(PDF)

R.7 Statistics in Functional Genomics workshop paper (PDF)


R.8 Software design of the MAExplorer data mining tool
(PDF) or
(PPT)




Newsletters





Appendices

A. Short tutorial for MAExplorer

A.1 Demonstration data

A.2 General instructions

A.3 Self-guided tutorial of MAExplorer - notation and examples


B. Advanced tutorial


C. Use of MAExplorer with user's microarray data

C.1 
  Creating quantified spot data files from hybridized sample arrays

C.2 
  Table of samples that can be loaded into MAExplorer

C.3 
  Quantified spot data file format

C.4 
  GIPO table database file format

C.5 
  Configuring MAExplorer for use with other arrays

C.6 
  Using the Cvt2Mae 'wizard' tool to convert array data for use with 
  MAExplorer


  

D. 
  Use of MAExplorer as a stand-alone application

D.1 
  Installing MAExplorer as stand-alone application

D.2 
  Downloading MAExplorer for stand-alone use with other arrays

D.3 
  Starting MAExplorer by clicking on a .mae file

D.4  
  The data file format for .mae files

D.5  
  Using MAExplorer as an Applet on your computer
 
D.6 
  List of startup .mae files included in the download installation 
 




E. Design issues

E.1 
  Internal data structures design to facilitate direct manipulation

E.2  
  Approaches to data mining: client-centric and server-centric models

E.3 
  Conversion of microarray data files to MAExplorer format using Cvt2Mae

E.4  
  Extending MAExplorer functionality using Java Plugins

E.5  
  Web database server design





Download Installers

Installer information


 MAExplorer Plugins

 Cvt2Mae wizard


MAExplorer Open Source


Download source

javadocs for source

 MPL1.1 Public License

 Legal


List of Figures

List of Tables

Glossary of terms used in MAExplorer

Index




 Help
desk







 




MAExplorer - Overview


MAExplorer is a bioinformatics microarray data mining Java application
that may help in the discovery of genes regulated in cancer and other
diseases.  MAExplorer is generally run as as a  stand-alone application on a
local computer. By running as a local application, it is able to
access your local disk to save the state of your data mining session
as well as plots and reports.  Using the previously saved data mining
state, you can continue a data-mining session at a later date after
exiting the program.







Recommended Hardware


Because data mining is a computationally and graphically intensive
activity, a reasonable level of computation resources are required for
adequate response. The same Java program runs on a variety of
operating systems including Windows 95/98/Me/NT/2000, Macintosh
OS8/9/X, Solaris, Linux, etc. so the choice of computer is not that
critical. We recommend the following hardware:







Addition of user defined analysis methods using Java Plugins


We have provided the ability for users to add their own Java Plugin
Extensions to MAExplorer. These extend the capabilities of the core
MAExplorer program to other more sophisticated analysis methods
created by users and allow interaction with specialized genomic
servers.  This is described in  Appendix E,  Section 2.6, and in the  MAExplorer Plugins Web page.











 MAExplorer - Microarray Exploratory Data Analysis



 



1. Introduction


This hyperlinked manual provides a detailed description of the
MAExplorer conventions (Section 1) and operation (Section 2). The latter contains
many figures of computer screens showing the operations described in
the Section. Section 3
discusses typical scenarios in using MAExplorer for data-mining
microarrays and contains a brief introduction to the process of data
mining.  Section 4 lists currently
known bugs and the revision history.  Appendix A is a short
tutorial. Appendix B is a more
advanced tutorial. Appendix
C describes the files required by MAExplorer and how they may be
created for using MAExplorer with other array data. It also describes
the data conversion tool Cvt2Mae.  The Appendix D covers downloading,
installing and running MAExplorer as a stand-alone Java application on
a local computer.  Appendix E
discusses design issues for the MAExplorer Java program and supporting
Web servers.  Users may create new analytic methods and add them as  MAExplorer Plugins as Java
extensions.  There is a 
glossary of terms used in MAExplorer. There is also a List of Figures, a List of Tables, and an Index to help find material of interest.





MAExplorer is normally used as stand-alone program 


 Figure 1 gives an overview of the
system. Note that MAExplorer does not perform spot
quantification from raw scanned images - it is used for the
subsequent data mining analysis of quantified spot data. Figures 1.1.1 through
1.1.3 describe this in more detail.













Overview of MAExplorer showing it use as either a stand-alone application of Web applet



 


Figure 1. Overview of MAExplorer exploratory data analysis
system. Initial data preparation steps are performed prior
to analysis by MAExplorer and are indicated by cyan
italics at the top of the figure. The primary data consists of
quantified microarray image data as well as corresponding qualitative
clone ID, gene-in-plate-order (GIPO or print-table, etc.), gene name,
hypertext base references and related information.  After the
microarrays are hybridized, they are scanned and spots quantified
using image spot quantification programs. These lists are then saved
for each array in a tab-delimited file. Microarray image
quantification may be performed by various software such as Axon's
GenePix(TM), Scanalyze, Molecular Dynamics
ImageQuant(TM), Research Genetics' Pathways(TM),
etc.  When used as a stand-alone application, data may be saved on the
local computer for local off-line use, and direct access to other
Internet genomic databases may be made without using a proxy server.



[DEPRICATED: When used as an applet, this auxiliary
databases and the MAExplorer Jar files are copied to the Web server or
local file system (in the case of the stand-alone version) where they
are then available to be downloaded by users. When a user invokes a
Web page containing the Java applet, it first downloads the applet
that then downloads auxiliary databases including a configuration file
that describes the array data. It then downloads the subset of
quantified microarray spot data files requested for the set of
hybridized samples being investigated. Additional samples may be
downloaded at any time. When the user selects an operation that
requires access to Web databases not residing on the MAExplorer Web
server, implicit Java security restrictions prevent the applet from
going directly to these other Web servers. Instead, it requests the
MAExplorer proxy server request the data from the foreign Web server,
and then returns it back to the user's Web browser. ]



















Overview of MAExplorer exploratory data analysis system



 


Figure 1.1.1 Overview of MAExplorer exploratory data analysis
system. MAExplorer is used as a stand-alone application on local
data.  [Its use as a Web browser applet has been DEPRICATED. In
the case of the applet, it may only access quantified array data from
the Web server that launched the applet.]


















Overview of data preparation for quantified spot data used by MAExplorer



 


Figure 1.1.2 Overview of data preparation for quantified spot data
used by MAExplorer. MAExplorer handles quantified spot data as
shown in this figure. Arrays are hybridized against labeled samples
are scanned and spots are quantified into spot data files.  Quantified
spot data is represented as tab-delimited data with data for one
spot/row.  Each spot is identified in this file by its grid
coordinates (grid, grid row, grid column) with image (X,Y) coordinates
being optional. Quantified spot data includes the raw spot intensity
for each channel (in the case of multiple channels such as Cy3, Cy5,
etc.). If the original data has background spot intensity values, then
that may be included as well - otherwise no background data will be
available for background correction. The spot data is discussed in
more detail in Section 1.1 and Appendix C.1, and Appendix C.3.



















Overview of running MAExplorer as a stand-alone application



 


Figure 1.1.3 Overview of running MAExplorer as a stand-alone
application. The preferred way of running MAExplorer is as a
stand-alone application. There are distinct advantages in running
MAExplorer as an application in that data and the exploration state
may be saved on the users local computer, direct access to genomic
servers is easier (no proxy server required - see Figure 1.4).  MAExplorer plugin
extensions (MAEPlugins) may only be used with the stand-alone
version. Since MAExplorer is packaged for 
download for a variety of operating systems, using this method is
not difficult to set up and the MAEPlugins should run on a variety of
operating systems.

















Overview of running MAExplorer as a Web browser applet



 

Figure 1.1.4 [DEPRICATED] Overview of running MAExplorer as a Web
browser applet. An alternative way of running MAExplorer on
existing databases is as a Web-browser applet. There advantage of this
method is that no software installation is required on the user's
computer.  However, the user may not save data and the exploration
state on their local computer. Furthermore, direct access to genomic
servers requires a proxy server. MAExplorer plugin extensions
(MAEPlugins) may not be used with the the applet version.  The  Mammary Genome Anatomy Program
(MGAP) originally used the MAExplorer applet.










Example of a MAExplorer database - 

http://www.lecb.ncifcrf.gov/mae - the public MGAP DB



The Mammary Genome Anatomy Program (MGAP) microarrays of cDNA clones
from mouse mammary tissue (collaboration with Research Genetics) were
hybridized with 33P radio-labeled samples.  These were then
used to charge fluorescing plates. See the  MGAP site for more
documentation on the database and preparation procedures.  The
hybridized arrays are scanned on a phospho-imager scanner at high
resolution.  Spot data was quantified from these images using the
Research Genetics' "Pathways 2.01" program which generated
tab-delimited data files. This data also includes the microarray grid
point locations (field, grid, grid row, grid col) from the associated
microarray description data files (grid-in-plate-order data). When you
download MAExplorer, you will also
download the public MGAP dataset.






1.1 Microarrays and notation used with MAExplorer


In general, microarrays are hybridized using cDNA samples derived from
mRNA labeled with either radio-label, biotin, fluorescent dyes, or
other methods (see  Schulze,
2001) for review of the technology). MAExplorer may be used to
construct databases using single-labeled sample intensity (e.g.,
Affymetrix, 33P radio-labeled, etc.) and double-labeled
ratio fluorescent (i.e. Cy3/Cy5) data arrays with different GIPO
geometries. 






Definition of "Condition list of samples"


Samples are organized into Condition Lists of samples (generally
replicate samples). These may be used in various statistical and
clustering tests.  There are three built-in lists of samples called
the HP-X 'set', the HP-Y 'set' and the HP-E list. The X and Y sets are
used in various 2 condition tests such as the  t-Test between the X and Y
sets (Section 2.4.3). The HP-E list is an ordered expression list
of samples used in clustering and in displaying expression
profiles. You may interactively  define new or edit
named condition lists using a graphical wizard (Section 2.6),  manipulate and assign them to the HP-X
'set', HP-Y 'set' and HP-E list.  Some examples of condition lists
might be (assuming you have the data available in your database):


  Virgin     = ( V.1, V.2, V.3 )
  Pregnacy   = ( P13.1, P13.2, P13.3 )
  Lactation  = ( L3.1, L3.2, L3.3 )
  Involution = ( I4.1, I4.2, I4.3 )








Definition of "Ordered Condition list" of multiple condition lists


We further extend this paradigm by defining a meta-data structure
called the "Ordered Condition List" or OCL. This is an list of
multiple conditions that you have previously defined. The OCL
may be sorted if you want and the data lends itself to
sorting. E.g., a time series of conditions lends itself to sorting -
different types of diagnoses may not. The OCL may be used in various
statistical tests (e.g., the  F-test applied to the current OCL - see Section 2.4.3)).  You may interactively
 define new or
edit named Ordered Condition Lists using a graphical wizard
(Section 2.7).An example of an ordered condition list might be:


  Partuition= ( Virgin, Pregnacy, Lactation, Involution )









Definition of "intensity" for single-labeled samples


MAExplorer uses the term "intensity" in slightly different ways
dependent on whether you are using the single-labeled or fluorescent
double-labeled data. For single-labeled data, "intensity" is the raw
quantified data value as measured by the image scanner. Raw data must
be normalized between samples in order to compare it between
samples. Therefore, to compare N samples, you must first normalize the data and then
compare them.






Definition of "intensity" for fluorescent double-labeled samples


For fluorescent double-labeled data, the Cy3 and Cy5 dye-labeled (for
example) measurements are the raw quantified data values as measured
by the image scanner. In this case, "intensity" is defined as the
ratio of Cy3 to Cy5 (i.e. Cy3/Cy5). If you wish to look at the ratio
as Cy5/Cy3, you may flip the two channels on a per-sample basis (see
Section 2.2.2 for more
details).




Issues of experimental design of microarray experiments


Some of the issues involved in  experimental design
(setting up experiments) based on the types of arrays are discussed in
Section 3.1.1 for (Cy3/Cy5)-labeled as well as 33P-labeled
samples. Poorly designed experiments will not yield significant
statistical results, so attention should be paid to developing an
adequate and robust design for your data given costs of doing
experiments as well as statistical constraints on analyzing the data.







Actual and "Pseudoarray" image geometry


The main MAExplorer windows contains a pseudoarray image for
visualization purposes. It may or may not correspond the spot
positions on the actual array. This array geometry is defined by the
number of replicate Fields (normally 1) each of which contains a
number of grids (also called "blocks") containing a number of
rows/grid and columns/grid of spots.  If there is no explicit array
geometry or spot (X,Y) coordinate data available but simply gene
identifiers and intensity data, then an arbitrary pseudoarray geometry
is generated. If there is an explicit array geometry, then it waill
draw the pseudoarray using this geometry. The database configuration
determines which method will be used and is discussed in  Appendix C.5.  If there is no
explicit grid geometry, the number of spot Locations (e.g., IncyteID,
Affymetrix probe_set) may be used to synthesize a set of grids of a
size that is reasonable for viewing with MAExplorer. This is done in
the Cvt2Mae array data
conversion program when the  array
geometry (#grids, #rows/grid, #columns/grid) is not known.  This
conversion is not done in MAExplorer itself. 

In Cvt2Mae we generate a visually appealing pseudoarray image geometry
if no array geometry is specified with the data (e.g. Affymetrix data,
etc). It maps the number of N spot data entries to a
(#grids,#grid-rows,#grid-columns). The algorithm is given in Appendix C.6 as well as
a suggestion for handling
non-standard geometries using Cvt2Mae.








Gene coordinate numbering on the microarray


A gene coordinate
numbering is a mapping of gene identifiers to locations on the
array for a particular array geometry.  These are described by
grids (or blocks), each consisting of grid rows by
grid columns of spots. The grids may be repeated on the array
and constitute duplicate fields. Some arrays group subsets of
grids into meta-grids which are specified by meta-grid
rows by meta-grid columns of grids. MAExplorer can handle
grids but not meta-grids.  In the case where there is no
array grid geometry specified or meta-grids are used, an arbitrary
pseudoarray geometry can be constructed to serve as a basis to display
the microarray pseudoimage (see the Algorithm for constructing the
pseudo array from a list of spots in Appendix C.6).


In MAExplorer we refer to grids by letter names (A,B,C,...) and fields
by F1 and F2.  If you are using Cy3/Cy5 ratio data and the Cy3 and Cy5
data is available as independent channels for each HP sample, then
operations that use F1 and F2 will use the Cy3 and Cy5 data for
various operations such as scatter plots (Cy3 vs Cy5), etc.  If there
is only one field in an array (i.e. no duplicate grids), then when
MAExplorer is run, operations and menus describing F1 and F2
operations will not be available.


Using duplicate (F1 and F2) spots allows us to get an estimate of the
hybridization variance within an array and is used to compute the
(F1,F2)  gene coefficient
of variation (CV) used in the gene data Filter to remove noisy
data before looking for additional differences. Note that if Cy3/Cy5
data is used, then F1 and F2 duplicates are not allowed as MAExplorer
uses the (F1,F2) data to hold the(Cy3,Cy5) data for a hybridized
sample.








Example: special array spot coordinate numbering for the MGAP array


As an example of this coordinate system, the following describes the
array geometry for the array used in the NIDDK MGAP database. The general
principal with different sizes and numbers of fields is the same for
other arrays. The MGAP array was spotted by Research Genetics for
MGAP. Clones in the array are laid down in grids consists of 8 rows
and 24 columns per grid. There are 8 grids (named A through H or 1 to
8) to a field with a space between grids.  Finally, there are two
fields (left and right named 1 and 2 or F1 and F2) that are
duplicates.


Note: we currently present the MGAP arrays with grids A through H
oriented from top to bottom - whereas Research Genetics orients them
rotated +90 degrees with grid H to the left and grid A to the
right. This occurred when the images were scanned with a -90 degree
change in the orientation. Therefore, we have swapped rows and columns
in our relative orientations so it meets with users normal
expectations of row-column orientation.  This could be easily changed
to the Research Genetics convention using a parameter in the
configuration file. Since the actual plate coordinates are tracked
with each clone and reported when it is accessed in MAExplorer, the
image coordinate system is not that critical - although the
verisimilitude of actual array layout and the data-mining layout can be
useful.









 

 Setting the "current gene" to a specific gene by "Master gene ID" 


The MAExplorer uses the concept of the "current gene" to indicate a
particular gene to be analyzed. You may interrogate the microarray
database or Internet databases for data on the current gene or to use
it in one of the operations. For example, you might cluster genes by
expression profiles to find other genes with profiles similar
to the current gene.


Various gene identifiers may be present in the GIPO data file
associated with the array. One of these is selected to as a unique
identifier to represent genes in the MAExplorer database.  Normally,
the Master gene ID is defined as the Clone ID. However if the
Clone ID is not present, but the GenBank ID is, it will use the latter
as the identifier.  If neither GenBank nor Clone ID is present, it
will use GenBank5' then GenBank3' if present. If that is not present,
it will use the UniGene ID if is present. If that is not present, it
will use dbEST5' then dbEST3' if present. If that is not present, it
will use LocusLink LocusID if present.  Finally, if none of those
identifiers are present, you can specify a 'Generic ID' that is
related to some other database gene identifier such as a 'Location'
identifier.


The current gene may be specified by clicking on a spot in the
microarray image or on a point in the popup scatter plot, or a gene
ID cell in a report. 




 

 Setting the "current gene" by Gene Name Guesser 


In addition, the user may type a specific gene name or clone ID into a
popup Gene Name Guesser dialog text window. This is invoked by
clicking on the blue button "Enter gene name or clone ID" at the top
right in the control panel.  When the "guesser" window pops up, start
typing the gene name or clone ID in the blue text entry field. You
select either the Gene Names, Clone ID, UniGene ID, GenBank, GenBank
3' or GenBank 5',dbEST 3', dbEST 5', or LocusID identifier.  Then you
may start typing letters and it will match all names or identifiers
which are prefixed with the sub-string you have typed so far.  As you
type more characters, it will limit the list of possible completions
of what you are typing. After selecting the gene you want, you then
press the "Done" button to use this entry to set the current gene and
remove the guesser popup window. You may press the "Clear" button to
clear what you have typed and the "Cancel" button to cancel the
current gene selection process.







 Setting the "Edited Gene List" subset of genes using wildcard
names 


You may also define a set of genes from the guesser window using
wildcard names where the character '*' matches zero or more
characters. First you specify a sub-string common to gene names.  Then
press the "Set E.G.L." (set 'Edited Gene List') button. For    example (see Figure
2.3.1), you could find all oncogenes and proto-oncogenes by typing
"*ONCO*" in the guesser.  It automatically enables the View 'Edited
Gene List' in the array that shows genes in the E.G.L. enclosed in
magenta boxes.





 

 The current gene cluster 


Some operations involving clustering will automatically assign the
gene cluster to the E.G.L. This includes clustering of genes similar
to a selected (i.e. current) gene and K-means clustering. In the case
of K-means clustering, the cluster you select by picking a gene
belonging to that cluster will cause it to be defined as the current
cluster and also assigned to the E.G.L. This will be discussed in more
detail in the section on clustering.


 

 The current Condition List of samples 


The current condition list of samples
is the last condition edited with the interactive  graphical wizard
(Section 2.6) used to define new or edit condition lists.




 


 The current Ordered Condition List (OCL) of multiple conditions


The current ordered condition
list (is a possibly ordered list of Multiple Condition
Lists) is the last condition edited with the interactive  graphical
wizard (Section 2.7) used to define new or edit ordered condition
lists.



Saving full resolution plots as GIF files in stand-alone mode


The various plots may be saved as full resolution GIF files when
running MAExplorer in stand-alone mode. The various plots have
"SaveAs" buttons which appear in stand-alone mode. Saving your
intermediate results may be useful for documenting your data mining
session or for subsequent publication. (Here is an example of a full
resolution 
clustergram of 38 MGAP hybridized samples for 1076 named and EST
genes).








Saving Text windows as .txt files in stand-alone mode


The various text windows may be saved as .txt files when running
MAExpplorer in stand-alone mode. The various text windows have
"SaveAs" buttons which appear in stand-alone mode. Saving your
intermediate results may be useful for documenting your data mining
session or for subsequent publication.







1.2 Microarray image quantification


Quantification data for all genes in a hybridized sample (x and y
coordinates, intensity, background density) is obtained by reading
data from a quantification file for that hybridized sample. The
quantification file for each hybridized sample resides on the local
file system (for stand-alone) or MAExplorer Web server (for applet
use) and is derived from image quantification programs such as Axon's
GenePix(TM) program, Scanalyze, Molecular Dynamics'
ImageQuant(TM) program, Research Genetics'
Pathways(TM) program, etc.   These programs are
independent of MAExplorer and are not part of our downloadable
software distribution.  
Normalization between hybridized samples must be performed to
allow comparison between different hybridized array samples. File
formats are discussed in Appendix
C).








1.2.1 Ratio and Zscore comparison of data from different
hybridized samples


Because of variation between hybridized samples, data is normalized.
Methods that are pure scaling transformations (such as Median, Scale to 65K, By Calibration DNA, By Use Gene Set,
etc.) allow you to compare data using the ratio between two normalized
sets of data. We define the ratio for two samples as follows:


    ratio(x,y,c) = Ixc / Iyc
where: 
    samples x,y have values Ixc and Iyc for the same 
    gene c in samples HP-X and HP-Y



The Zscore method transforms the data such that it can not be used
with the ratio comparison. Instead we use the Zdiff(x,y) method for
comparing Zscore developed by Mark Vawter (Vawter, 2000). Zscores typically
cover the range of -3.0 to +3.0 (standard deviations) with a
transformed mean of 0.0. Therefore the Zdiff will typically cover the
range of -6.0 to +6.0.


Let
    Zscore(p,c) = (Ipc - meanp)/stdDevp
where:
    Ipc is the intensity of gene c for sample p. Sample p has meanp 
    and stdDevp

Then,
    Zdiff(x,y,c) = Zscore(x,c) - Zscore(y,c),
where: 
    samples x,y have Zscore(x,c) and Zscore(y,c) normalized values for the 
    same gene c in samples HP-X and HP-Y, or HP-X 'sets' and HP-Y 'sets'.






 

Table 1.2.1 Displays affected by the normalization mode. When
comparing two hybridized samples or sets of hybridized samples, the
metric used is either ratio or Zdiff depending on whether the Zscore
normalization was selected in the Normalization menu. This will affect
a variety of data displays and some of the data Filter methods listed
here. In addition, all of the other graphics (EP plots, intensity
histogram plot, cluster plots including clustergrams and dendrograms)
are also affected by the normalizations.








    

  1.  Pseudocolor X/Y ratio, X-Y Z-diff, and other pseudoarray images

  2.  the 3-line gene data displayed in the main MAExplorer window 

  3.  the gene data display at the top of the scatter plot when clicking
on a point in the scatter plot

  4.  report tables of genes with the highest/lowest X/Y (Cy3/Cy5) (F1/F2) ratio
or X-Y (F1-F2) Zdiff

  5.  Ratio histogram plot of X/Y (Cy3/Cy5) (F1/F2) ratios or X-Y (Cy3-Cy5) (F1-F2) Zdiff data

  6.  data Filter: Spot Intensity [SI1:SI2] range or Zdiff [Z1:Z2] range for HP data

  7.  data Filter: Intensity [I1:I2] range or Zdiff [Z1:Z2] range for (HP-X/HP-Y) or (HP-X - HP-Y) data

  8.  data Filter: Ratio [R1:R2] range or Zdiff [Z1:Z2] range for (HP-X/HP-Y) or (HP-X - HP-Y) data

  9.  data Filter: Ratio [CR1:CR2] range or Zdiff [CZ1:CZ2] range for (Cy3/Cy5) or (Cy3-Cy5) data of a single sample

  10.  Range and scale of data in EP plots, cluster plots, clustergrams, 
dendrograms, etc.

  11.  Statistical tests t-tests (X and Y sets), Kolmogorov-Smirnov test (X and Y sets), 
     ANOVA F-test (OCL), etc.
















1.3 Microarray image and plot display


The MAExplorer displays one microarray pseudoarray image of the
hybridized samples. This is either for a single sample, the ratio of
two samples, the average of replicate samples or the ratio of two sets
of replicate samples, the ratio Cy3/Cy5 or Cy5/Cy3, or other
mappings. Section  2.4.4.1
Show microarray pseudoarray images menu describes these options and
shows some examples. 


The Filter menu is used to select a set of data filters that
determines which genes are selected. These are highlighted in the
array image in different ways - with a red (white) circle in the
intensity (ratio) pseudoarray image each spot meeting the range threshold
criteria. How these are highlighted depends on which Plot menu
Show Microarray method and View menu modes were
selected. If the Show 'Edited Gene List' (EGL) option is set in
the View menu, genes in the EGL will appear as  magenta squares. The "Filter mode" is always
present and shows genes meeting various Filter criteria (to be
discussed). The user may interactively define a list of genes by
clicking on them when the Click to add gene to edited gene list
option is set in the Edit menu. Alternatively, you can click on
a gene with the Control key pressed to add a gene to the EGL or with
the Shift key pressed to delete a gene from the EGL.






Types of pseudoarray image displays


There are several differnt types of pseudoarray images that may be
displayed.  The current type is set in the Show Microarray
submenu in the Plot menu selections including
Pseudograyscale intensity that approximates the intensity of a
single sample or average of samples. The Pseudocolor
Red(X)-Yellow-Green(Y) HP-X/HP-Y ratio or Zdiff and Pseudocolor
Red(Cy5)-Yellow-Green(Cy3) Cy3/Cy5 (or F1/F2) ratio or Zdiff add
the two samples or channels together as separate Red+Green channels to
give a color spectrum. The Pseudocolor HP-X/HP-Y ratio or Zdiff
Pseudocolor Cy3/Cy5 (or F1/F2) ratio or Zdiff gives a color
spectrum from a low ratio (zdiff) value (Green) to a high value (Red)
with a value of 1.0 (0.0) of Black.  The Pseudocolor (HP-X,HP-Y)
'sets' p-value shows the p-Value between two X and Y sets in a
color spectrum.. If the Original image is set and the image
file is in the database, it will pop up a separate Web browser window
to display it. The Pseudograyscale display is a grayscale image, with
higher concentration genes appearing darker, on a light blue
background. The pseudocolor HP-X/HP-Y ratio of spots image is
constructed using a color scale going from bright green (<1) to
black (=0) to bright red (>1) on a black background. For the
pseudocolor Zdiff of (X-Y), the color scale goes from bright green
(<0) to black (=0) to bright red (>0). If the dichromasy
switch is set in the View menu, that a different set of colors is
selected that may be easier for some people to differentiate.  If the
Use dual HP-X & HP-Y 'sets' else single samples toggle in
the Samples menu is set, it displays the mean HP-X data in the left
and HP-Y in the right for doing a side by side comparison. 


In all of the pseudoarray images, the grids in the image are labeled
field#-GridLetter (e.g. 1-C, 2-B, etc). This allows them to be
clearly identified as the user scrolls over the image that is larger
than the visible computer window.







Popup windows


MAExplorer starts with the main pseudoarray image windows. This window
contains the pull-down menus where you may issue commands. As you
perform various operations, new windows may popup for some of these
commands. For most of these windows, you may click on the "Close"
button or click on the close window icon associated with your
operating system (generally one of the buttons at the top of the popup
window). However, some windows were designed to not close when you do
this. In particular the "State sliders" are not able to be
closed unless the associated data filtering or clustering operation is
closed. When you close the associated operation will automatically
close the state slider window.


There is also a popup alert message window for bettering informing
users of conditions that prevent them from doing the operation they
requrested. You must press the Close button to pop-down the message,
although you may do press the SaveAs butto to save the message to a
file. For complex problems, some of the messages may suggest what you
need to do to correct the problem.





The current sample sample, HP-X, and HP-Y


In MAExplorer, a hybridized array sample is abbreviated HP. The
underlying data comparison model assumes, as a minimum, the comparison
of two different experimental conditions represented by samples HP-X
and HP-Y.  A good way to think about this is that these variables are
the two axes of a scatter plot (one of the displays you may
generate). The HP-X and HP-Y may be thought of as containing data from
either single hybridized samples or containing mean data from multiple
replicate sets of sample.  The HP-X and HP-Y are assigned using the
Set current HP-X and Set current HP-Y in the
Samples menu (hybridized sample is abbreviated HP in
MAExplorer. The sets are most easily changed using Choose HP-X,
HP-Y and HP-E to select the currently active samples.  The
contents of the of multiple sample HP-X and HP-Y 'sets' may
alternatively be changed using the Edit HP-X & HP-Y 'sets' of
samples by source submenu, and the HP-E list of samples using the
Edit HP-E list of samples by source. Assigning single samples
to either HP-X or HP-Y may be done from the Samples menu. However, it
is easier to do it by clicking on the pseudoarray image. First click
on the magenta "[X]" or "[Y]" Current Sample box at the top of the
list of switch between HP-Y and HP-Y. Whichever is visible ([X] or
[Y]) is the one that will be the HP sample assigned. Then simply click
on the magenta "*" to the left of the sample name for the sample you
wish to assign.



Hybridized samples are selected from a list of all of the sample
samples in the database. To make it easier to select a HP, they may
be selected from submenus by their developmental stage (if supported
by your particular database) or from a list of all samples in the
database located on the left side of the pseudoarray image. If a sample
has never been loaded during a session, it will be loaded when you
request it. 


The last sample selected is called the current sample or
current HP. That is the sample that is displayed in the pseudoarray
image in the primary MAExplorer window when using display modes
requiring a single sample.

 



Using 'sets' of HP-X and sets of HP-Y


Multiple samples may be assigned to the to the HP-X or HP-Y
sets. These are assigned using the Edit HP-X and HP-Y 'sets'
of microarrys in the Samples menu. The multiple sets are
enabled by setting the Use HP-X and HP-Y 'sets' else single
samples checkbox in the Samples menu.  Then, when
statistical calculations are performed on that data, it will use the
means, std-deviations, etc. from each of these sets rather than
individual samples.






The HP-E sample list for computing expression profiles


You may cluster sets of genes with similar expression profiles across
a set of hybridized samples. The set of HP samples used in doing these
profiles is specified by Edit expression profile 'list (HP-E)
in the Samples menu. The Choose HP-X, HP-Y, and HP-E
command may also be used for defining the members and order of the
samples in the HP-E 'list'.  Then, gene intensity expression profiles
may be created in a popup window for hybridized samples in the HP-E
set by using the Expression profile plot commands in the Plot menu.
Several of these plots may be created on the screen at the same
time. Clicking on a vertical data line in the plot will show the name
of the HP, its intensity and coefficient of variation (CV) of the
(F1,F2) data for this gene. Note that you can order the hybridized
samples in the HP-E set by the order in which they are added.






Data 'Filters' - the intersection of one or more data tests


A set of genes may be computed by taking the intersection selected
gene sets.  These sets are determined by various logical, data range
and statistical tests.  Genes passing each test are assigned to a
gene subset which in turn are used in the gene intersection
computation.  The final gene subset is used in array, plots, and
reports, and subsequent data filtering. Changing any test parameters
causes the data filter to be re-computed.





List of data filters for MAExplorer - any number of filters may be used simultaneously





Figure 1.3 Data Filter Venn diagram. This illustrates some of
the logical, data range and statistical tests criteria available using
the MAExplorer data Filter paradigm. Note that multiple criteria
may be selected from each of these categories. The extreme case,
probably never used, could use all tests.









1.4 Exploratory data analysis - overview


MAExplorer may be used to perform various data explorations by looking
for patterns correlated with different sets of hybridized samples or
with expression profiles of genes. This is discussed in more detail
throughout this manual and later in Section 3 on Exploratory Data
Analysis.  Detailed descriptions of all commands are given in Section 2 Menus. 


A first-approximation approach to data-mining might be to sequentially
constrain the data of interest to find some changes and then to report
on those changes. We have arranged these commonly performed first-pass
operations as submenu entries in the Analysis Menu. The submenus are:


    

  1.  GeneClass Menu

  2.  Normalization Menu

  3.  Filter Menu

  4.  Plot Menu

  5.  Cluster Menu

  6.  Report Menu










The primary 'Analysis' menu of MAExplorer - most operations are found in this menu





Figure 1.4 Screen view of MAExplorer main window with Analysis
Menu. The menu structure of MAExplorer was designed to allow users
to quickly perform commonly used data-mining operations. Other menus
are used for modifying the data (File, Samples, Edit, and View menus)
or accessing on-line Help menu information in a separate Web browser
popup window. MAExplorer menus are similar to most Windows PC
applications where pull-down menu selections are used to invoke
operations.  The current hybridized array sample is displayed as a
pseudocolor ratio image of median normalized spot intensities.
Clicking on a spot assigns it as the current gene with data being
reported in the top most message area. The names of the current HP-X
and HP-Y samples are listed above that area.  In general, clicking on
spots, points in plots or cells in spreadsheet reports will assign the
it as the current gene and access Web genomic databases if enabled.








In addition to displaying the hybridized sample pseudoarray images,
derived data may be viewed in various types of plots. These include
scatter plots, histograms, ratio-histograms, expression profiles, gene
clustering, etc. Data may be presented as table reports presented as
either active spreadsheets that can access genomic databases by
clicking on cells or as tab-delimited Excel-compatible tables that may
be cut (if your windowing system supports this) and pasted into an
Excel spreadsheet.



The selected HP-X and HP-Y samples are used when generating scatter
plots, ratio histograms and other graphics.  Scatter plots and ratio
histograms may also be performed on the left and right sides of the
currently displayed HP array (fields F1 and F2 respectively if array
data has duplicate spots for the same genes).



A MAExplorer database contains a table identifying genes, so data is
accessible by gene name as well or by sub-strings identifying a set of
genes (e.g. "onco" that could be used to find any oncogene or
proto-onco gene in the database).



When the program starts, it displays the microarray image of the first
hybridized sample in the HP-X set of samples initially specified. If
you specify a new HP-X or HP-Y sample, then it changes the pseudoarray
image to correspond to that array. You may change the current HP-X or
HP-Y sample from either the Samples pull-down menu or by clicking on a
sample in the Active Sample list in the left of the pseudoarray
image.  If you click the mouse on or near a spot, it will latch
onto that spot and define it as the current gene.



Note: In Figure 1.4,
genes that pass the MAExplorer data Filters are indicated by red
(white) circles around spots in the pseudograyscale (pseudocolor)
intensity (ratio) image. The pseudoarray image shows the gene data as
replicate grids of spots if there are two fields Field 1 (left set of
grided spots) and Field 2 (right set of grided spots). If there is no
duplicate spot data, then only Field 1 is shown. 


If background correction is enabled in the Normalization menu, then
intensity is reported in the message displays as intensity'
otherwise as intensity.  
Normalization should also be used between hybridized samples -
whether the data is ratio data (i.e. Cy3/Cy5) or  single sample
intensity arrays.






1.4.1 Saving the state of a data-mining session in stand-alone mode
 

If you are running MAExplorer in stand-alone mode, you may save the
state of your session for later use using the "Save DB" or "SaveAs DB"
commands. Then, the checkpointed database could be accessed using the
"Open file DB command". It currently saves: the gene sets, condition
(HP) lists, current HP-X, HP-Y and HP-E lists, data Filter options and
slider value settings, display options, clustering options,
normalization options, etc. We recommend using the "SaveAs ... DB" so
you can save the state under a different name rather than overriding
the original state. This way you could backup to the original state if
you wanted to. The "SaveAs DB" and "Open file DB" commands are
described in the File menu.





1.4.2 Logging messages and command history
 

Often a user would like to review measurements of particular genes and
to review the list of commands they issued (also called the command
history).  Various data measurements as well as many other types of
information in the three text lines in the status area of the main
window may optionally be recorded in a  popup message log (Section
2.5.1) and the command history may also be reviewed in a separate
 popup message log (Section
2.5.2). If you are running the stand-alone version, the logs may
be saved. Otherwise, you could cut and paste the log data into
other word processing applications.






1.5 Quick start - demonstration of MAExplorer
 

MAExplorer is used as a stand-alone application. You may download the stand-alone
application (see Appendix D). This download also include a demo data set
of 50 hybridized samples from the public MGAP database. In any case, you can
explicitly download the data at any time at 
http://www.lecb.ncifcrf.gov/mae/MGAP-Array-database.zip or
HREF="http://prdownloads.sourceforge.net/maexplorer/MGAP-Array-database.tar.gz?download">
http://prdownloads.sourceforge.net/maexplorer/MGAP-Array-database.tar.gz?download


Setting up MAExplorer to work with  user-specific data is
discussed later in this manual in Appendix C.







MAExplorer Home page




Figure 1.5.1 The MicroArray Explorer home page at 
http://maexplorer.sourceforge.net/. The table of contents in
the left panel lists an introduction and short tutorial, several
demonstration databases. Below that are links to documentation
including this reference manual, glossary and index. The Export
version discusses running MAExplorer with other arrays and as a
stand-alone version. The  Download
application is a Web page for downloading and installing the
stand-alone Java application on your computer.










You may start MAExplorer in your Web browser from the MGAP

Startup DB. This offers several preset public databases consisting
of sets of hybridized samples as well as the empty database. After
you have clicked on a particular startup database, it will begin
loading MAExplorer - indicated by a red box with a 
"Loading..." message in the top window of your browser. After
MAExplorer starts, this message changes to a white box with "Reading DB" while it downloads the data files
required. Finally, when it is ready for your interaction, it displays
a white box with a green "Ready".


NOTE: for Web browser invocation, the MAExplorer applet works with
Netscape 4.7, Internet Explorer 5.0, and HotJava on a Windows
(95/98/NT/2000/XP) system or a Solaris Unix system. Macintosh and SGI
systems seem to hang at times because of Web browser
problems. However, it works on all other systems as a stand-alone Java
application that you may  download and
install on your computer.  You might want to  review these Web browser restrictions.



After the MAExplorer is started and the menus become active, you may
switch the preset hybridized samples to other samples using the
Samples pull-down menu. The last hybridized sample loaded
becomes the "current hybridized sample" and its image is the one
displayed. 







Exiting MAExplorer


If you are in MAExplorer and want close the program and exit, you may
use the Quit command in the File menu or click on the
"close application" button (found in the upper right hand corners of
MAExplorer windows put there by your operating system). 






1.6 Tutorials for using MAExplorer


There are a number of things you may do in this data mining facility. 


    

  1. Analyze expression of individual genes

  2. Analyze expression of gene families and clusters

  3. Compare expression patterns in multiple hybridizations



We wrote two tutorials to help you understand its capabilities.  We
recommend you first try the 
short tutorial before attempting the  advanced tutorial. The latter
demonstrates some of the more advanced capabilities.












 MAExplorer - Microarray Exploratory Data Analysis



 





 2. MAExplorer menus


A MAExplorer analysis is performed using various interactive
controls. These commands are selected from pull-down menus located on
the "menu bar" at the top of the main MAExplorer window. The primary
menus are:








Menu notation


In the following menus, selections that are sub-menus are
indicated by a 'Indicates that the
menu has a submenu'.  Selections prefaced with a '' and indicate '' indicate that the command is a checkbox
that is enabled and disabled respectively.  Checkbox menu items
have a "[CB]" at the end of the command. Selections prefaced with
a '' and indicate '' indicate that the command is a
multiple choice "radio button" that is enabled and disabled
respectively, and that only one member of the group is allowed to be
on at a time. Radio button menu items have a "[RB]" at the end of
the command. The default values set for an initial database are
shown in the menus.  Selections prefaced with a '#' indicate
that the commands are available only when MAExplorer is run in the
stand-alone mode. Selections prefaced with a '*' commands
requires access to the backend Web server [Future]. Selections that
are not currently available will be grayed out in the menus of the
running program.



The following Sections 2.1 through 2.7 describe the pull-down menus in
detail.










 MAExplorer - Microarray Exploratory Data Analysis



 




2.1 File menu


The File menu operations includes options and submenus
providing access to database data from disk and Web servers, state
saving, and groupware to share states between collaborators. 





In stand-alone mode, the user may select the database subset to be
loaded from either a Web server or a local file system. When used as
an applet, this is pre-determined by the Web page where MAExplorer is
started.  Opening a disk DB, 'Open disk DB', also restores any  user defined gene sets
and other parts of the  exploratory
state that were present when the 'Save ... disk DB' was
invoked. 


In the following menus, selections that are sub-menus are
indicated by a 'Indicates the menu
entry has a submenu'.  Selections prefaced with a '' and indicate '' indicate that the command is a checkbox
that is enabled and disabled respectively.  Checkbox menu items
have a "[CB]" at the end of the command. Selections prefaced with
a '' and indicate '' indicate that the command is a
multiple choice "radio button" that is enabled and disabled
respectively, and that only one member of the group is allowed to be
on at a time.  Radio button menu items have a "[RB]" at the end of
the command. Selections prefaced with a '#' indicate that
the commands are available only when MAExplorer is run in the
stand-alone mode. Selections prefaced with a '*' commands
requires access to the backend Web server [Future]. Selections that
are not currently available will be grayed out in the menus of the
running program.


When used as an applet connected to a Web database server, databases
may be divided into public and collaborator projects.  Users accessing
protected collaborator projects will be required to log-in to the
server and a popup login request will appear.


 






2.1.1 Databases menu


The Databases submenu is currently available only in stand-alone
mode and contains the following selections for opening and saving
databases. 


[In the future], each user will be able to save the state of their
exploration into a password protected directory of named states on a
Web server (e.g. doing a 'Save ... Web DB' command. Later, they could
restore that state from the Web server by doing an 'Open Web DB'
command).  Users would be required to register with that server to set
up a unique state-saving area.  Once this facility was setup, users
may selectively allow other user's to view selected data implementing
a groupware environment for improving collaboration.




  
  

  

  






Starting an existing MAExplorer database using the file browser with 'Open file DB'




Figure 2.1.1 Example of the "Open file DB" command. The file
browser is opened in the current project directory with the name of
the currently opened file. You may select another .mae startup
database file to load in the current project. You may also "cruise"
the file system and load an .mae file from a different project directory.
The "Set project" command makes this easier since it gives you a list
of available projects that you may change directly. The projects must have
been setup on your computer previously. The "New project" command can
be used for setting up new projects or projects.




  






Saving the current session in a new MAExplorer startup database file using the 'SaveAs file DB'




Figure 2.1.2 Example of saving a user session in a new startup file
using the "SaveAs DB" command. The file browser is opened in the
current project directory with the name of the currently opened
file. You may enter another .mae file name to save your current
session. Then when you restart MAExplorer using this new file, it will
restore the data mining state to where you left off (except that no
popup windows are opened).








2.1.2 Exploratory state menu


The Exploratory 'state' submenu contains the following
selections for saving and later using the user's state of the
exploration. If MAExplorer is being run on a local computer, no login
is required.


  
  
  

  
 


2.1.3 Groupware facility for sharing user states menu [Future]

 
The groupware facility allows users to share their state data with
other users. However, If MAExplorer is being run on a local computer,
then groupware may not be available since it depends on using specific
Web servers with MAExplorer-specific groupware services available.
[We first developed these concepts in the context of 2D protein
gels.  They include: WebGel (Lemkin
et al., 1999b) for Internet exploration of 2DE databases, Xconf (Lemkin et al., 1993) an early
X-windows based image conferencing similar to CU-See-Me or ALO Instant
messenger for sharing images in a conference over the net,  Flicker (Lemkin, 1997) an image
comparison over the internet using a a Java applet, and  GELLAB-II (Lipkin and Lemkin, 1981)
a system for data mining - see 
GELLAB-II Poster on the Web) that embody many of the concepts used
in MAExplorer.]

 The Groupware submenu contains the following selections:
  
  
  

  



  



 

Saving the state and databases on the local file system


The current state of an exploratory data analysis session may be saved
on the local file system. The state consists of: gene sets;
HP-X and HP-Y hybridized sample sets; HP-E hybridized sample
lists; thresholds and switch setting preferences; etc. The user may
save their current state and restore a previous state at any
time. Restoring the state will overide the current database.






Groupware sharing of intermediate exploratory results [FUTURE]


Each registered user will be able to save the current state of their
exploration of the data in named User State files on the
back-end Web server using the Save user's state command.  The
user may keep multiple named states on the back-end secure server
where they be accessed to restore the state at a future time using the
Open user's state command. The user can request a list of their
states with the Directory of user's states command. They may
remove a particular state with Delete user's state. 


A registered user may allow another registered user to access their
state or states (using the Open another user's state command)
if the user owning the data had granted them permission. The Share
user state and Unshare user state commands control these
permissions. There are two special share-users defined: public
to allow unlimited read-only access to the state they specify, and
private to disallow all access to a user state.










 MAExplorer - cDNA Microarray Exploratory Data Analysis



 




2.2 Samples menu


Each experimental condition sample is represented by a hybridized
sample (abbreviated HP in MAExplorer).  The Samples menu
operations include operations to select the current hybridized sample
or samples. The simplest model of a MAExplorer analysis assumes (at
least) two microarray hybridized samples variables HP-X, HP-Y whose
data may be plotted against one another or compared.  The default is a
single HP-X sample and a single HP-Y sample.


The first menu command, "Choose HP-X, HP-Y and HP-E samples", entries
lets you change the current working HP-X 'set', HP-Y 'set', and
HP-E 'list' hybridized samples.



The second menu command, "Choose named condition lists of samples",
lets you define or edit new named lists of hybridized samples. This is
useful for defining sets of replicate samples. These may be further
manipulated using the (Edit menu | Sets of Conditions (samples))
commands.




The third menu command, "Choose ordered lists of conditions", lets you
define or edit new named Ordered Condition Lists (OCL) of named
condition lists. This is useful for defining a sub-experiment
consisting of N conditions each with replicate samples. The last OCL
manipulated is defined as the "current OCL". The current OCL is used
in the OCL F-test Filter.



The fourth menu command, "Set Samples from lists", lets you change the
current HP-X and HP-Y, HP-Y samples as well as the HP-X 'set',
HP-Y 'set', and HP-E 'list' samples. This is similar to using the
"Choose HP-X, HP-Y and HP-E samples" command, but is more dificult to
use.  You may change the current HP-X or HP-Y sample by clicking on
the sample name directly in the list of sample names on the left side
of the pseudoarray image (see Figure 2.2.3 legend).






The fifth menu entry, "Edit use (Cy5/Cy3) else (Cy3/Cy5) for each HP",
lets you swap data channels for Cy3/Cy5 data for individual samples.



Other menu commands list the status of the current HP-X 'set', HP-Y
'set', or HP-E 'list', and define condition class names that are
associated with the HP-X 'set' and HP-Y 'set'.  The last menu entry,
"Use HP-X & HP-Y 'sets' else single samples", lets you switch
between using HP-X and HP-Y as single samples of sets of multiple
samples. For example, if you are using a scatter plot of X and Y, it
will switch the data being plotted from a comparison of single samples
to a comparison of means of sets of samples depending on the status of
the switch. Sets of samples are used extensively in data explorations.

 



Figure 2.2.1 shows setting the HP-X, HP-Y, HP-E lists of samples using
the "Chooser" - the preferred method.  Figure 2.2.2 shows setting the
HP-X sample from the menus.  Figure 2.2.3 shows changing the current
HP-X sample by clicking on a sample name in the microarray image.







Choosing the X and Y sets of samples and the E ordered list of samples





Figure 2.2.1 Samples menu - selecting lists of samples by using the
"chooser". The hybridized samples assigned to the current HP-X,
current HP-Y, set of HP-X, set of HP-Y and expression profile list
HP-E may be changed from the Samples pull down menu using the
Choose HP-X, HP-Y and HP-E option lets you graphically change
the currently active sample HP-X, HP-Y sets and E-list.












Selecting HP X, Y, or E samples using the pull-down menus





Figure 2.2.2 Samples menu - selecting samples by source
characteristics. The hybridized samples assigned to the current
HP-X, current HP-Y, set of HP-X, set of HP-Y and expression profile
list HP-E may be changed from the Samples pull down menu. The
specific "By Source" menus shown here are from the MGAP database.
This figure shows the user changing the current X sample from the
developmental stages submenu that is part of the "By Source" submenu.
Alternatively, samples containing a keyword or part of a keyword can
be found using a "guesser" popup window that allows the use of wild
cards. This is invoked using the "From list of all H.P.s" submenu. For
example, you could specify "*pregnancy*" to find all samples of
containing that word.











Switching current X or Y sample by clicking on desired sample 
in list at left edge of pseudoarray image. Switch between 
X and Y by toggling '[X]' and '[Y]' of the Current Sample box.





 Figure 2.2.3 Changing the current sample to either the HP-X or
HP-Y sample by clicking on a sample name at the left edge in the
microarray pseudoarray image. The current sample is indicated in
magenta. Click on the magenta "*" adjacent to the new name you want to
select and it will change the HP-X sample. To switch between setting
HP-X and HP-Y, click on the [X] Current
Sample box to change the sample to HP-Y.  You can click on
[Y] Current Sample box to change it
back to HP-X. Then clicking on a sample name will set it to the
current HP-X or HP-Y that was selected. This figure shows that the
user had selected [Y] and C57B6-L10-29hrs for the new HP-Y sample.












2.2.1 Selecting  hybridized samples with Chooser or pull-down menu 
sample lists


The Set Samples from lists submenu lets you define HP-X, HP-Y,
HP-X 'set', HP-Y 'set', or HP-E 'list' from lists of samples. It
contains four submenus:

 



The Set current HP-X sample and Set current HP-Y sample
commands offer another way to set the single current X and Y sample
(see  Figure 2.2.3 above for
the preferred way using the "Chooser").



The Edit HP-X & HP-Y 'sets' of samples by source menu allows
the user to define HP-X and HP-Y as sets having multiple
hybridized samples.  Then, the mean values of the genes are used
when comparing HP-X with HP-Y.

 


The Edit HP-E expr. profile 'list' by source menu allows the user to
define an ordered list of samples for use in expression profile
statistics.  Then, an expression vector of normalized quantification
values (one for each sample in the HP-E list) is computed for each
gene. Note: to place the samples in a particular order, start with an
empty HP-E set and then add them in the order you desire.

 




The Convention for pull-down menu sample selection lists


We use a common sample selection scheme when selecting a sample from a
pull-down menu list.  This sub-sample "By Source" option is only
available if your database was set up to allow sub-sample source names
in the Samples database.

 


For example, the By Source database-specific entries for the
MGAP database includes the following submenus.








The From list of all samples selection pops up a hybridized
sample guesser dialog window. As with the gene name guesser, you can
start typing in the name of a sample and it will give you a list of HPs
that match that initial string. You then click on the sample you want
and then press the Done button.







2.2.2 Swapping selected samples' (Cy3,Cy5) channels in ratio data
dye-swap experiments


The Edit use (Cy5/Cy3) else (Cy3/Cy5) for each HP command may
be used to selectively swap (Cy3,Cy5) data entries so the user may use
the samples (carefully, since gene labeling efficiency is not always
symmetric!) dye-swap data for replicates. This is only available for
ratio data. It swaps the data contained in MAExplorer (memory only) so
that the Cy3 data is swapped for the Cy5 data. For example, consider
the case where there are two materials A and B hybridized in two
experiments and labeled as follows: E1 (A=Cy3,B=Cy5) and E2
(A=Cy5,B=Cy3).  Then, assuming uniform symmetric labeling (which is
generally not the case - although it might be true for a subset
of genes), then one might average data from E1 and E2 if the data from
E1 (or E2) were swapped.  This is shown in the following figure.







Selectively swapping (Cy3,Cy5) for particular samples





Figure 2.2.4 Samples menu - selectively swapping (Cy3,Cy5) data
channels for particular samples. This is only operative if your
database contains Cy3/Cy5 ratio labeling data. This is useful in
databases containing subsets of dye-swap experiments mixed in with
other samples that are not dye-swapped.











2.2.3 Viewing sample HP-X, HP-Y, and HP-E partitions


You setup sets of HP samples for the HP-X and HP-Y sample sets and
HP-E expression list of samples using the Chooser (above). The current
contents of these lists may be viewed using the List HP-X &
HP-Y sample 'sets' to list the samples in the HP-X and HP-Y
'sets'. The List HP-E sample 'list' may be used to list the
samples in the ordered HP-E 'list'.




2.2.4 Defining sample condition 'class' names


When using sets of conditions, the HP-X and HP-Y 'sets', you will
probably want to assign meaningful names to these sets. The commands
Define HP-X class name and 
  •  Define HP-Y class name
assign a free text line name for the sets.



    

    2.2.5 Toggling between single HP-X (-Y) samples and HP-X (-Y) sets
    

When MAExplorer first starts up, it assumes that you wish to treat the
data as single samples so that HP-X and HP-Y are assigned to single
samples. However, if you want to work with sets of multiple samples
then you must toggle the state using the Use HP-X & HP-Y 'sets'
else single samples [CB] check box command. This toggles the state
between treating the data as multiple samples (HP-X and HP-Y 'sets')
or as single HP-X and HP-Y sample samples.
    



    

    2.2.6 Create and edit named condition lists of samples
    

The command Choose named condition lists of samples lets you
define new or edit existing named lists of hybridized samples called
"Condition lists". Associated with each condition list is a set of
annotation parameters to document the condition.  The condition lists
may be used in the (Edit | Sets of conditions) operations. Among other
operations, you may assign any condition list to the working HP-X
'set', HP-Y 'set', or HP-E list of samples used through
MAExplorer. The last condition list that was edited with the (Sample
menu | Choose named
condition lists of samples) is called the "current condition" that
could be used in various operations.  Figure 2.2.5 shows a screen
illustrating a popup condition chooser session where the legend
describes the options.



    


    

Editing named condition lists of samples


    


Figure 2.2.5 shows a screen illustrating a popup condition chooser
session. The set of all samples in the database is in the
scrollable "Remainder Samples" window in the upper left. The samples
you have selected for the condition list being edited is shown in the
upper right "Selected Samples in current condition" window. The list
of all conditions in the database is in the lower left "List of
Conditions" window. The current condition list that is selected is
highlighted and its contents displayed in the "Selected Samples"
window. User defined annotation associated with the current condition
are displayed in the right "Current Conditioned Annotation" window.
To add a new condition, click on the Add Cond button to define
the new condition name.  The Remove Cond button is used to
delete a named condition list. The List Cond button pops up a
report listing the samples and annotation for the current
condition. The List All button pops up a report listing the the
names of all of the conditions and the annotation names. You may add
or remove new annotation names for all of the conditions. The Add
Ann button will add the new annotation you enter into all
conditions - you must enter the data for each condition that requires
it. You may The Save the current status of all of the
conditions into your working database. If you have pressed
Cancel before saving, then you will not have saved your
edits. Pressing the Done button will save the changes and
pop-down the window.




    

    2.2.7 Create and edit named ordered condition lists (OCL) of conditions

    

The command Choose ordered condition lists of conditions lets
you define new or edit existing named ordered lists of conditions
called "Ordered Condition Lists" (OCL). Associated with each ordered
condition list is a set of annotation (name, value) pairs to document
the condition.  The last condition list that was edited with the
(Sample menu |  Choose
ordered lists of conditions) is called the "current OCL". The
current OCL is used by the (Filter menu |  Filter by current
Ordered Condition List (OCL) F-Test [p-Value] slider [RB])
test. Figure 2.2.6 shows a screen illustrating a popup
ordered condition list chooser session.


    


    

Editing named ordered condition lists (OCL) of samples


    


Figure 2.2.6 shows a screen illustrating a popup ordered condition
list (OCL) chooser session. The set of all conditions in the
database is in the scrollable "Remainder Conditions" window in the
upper left. The conditions you have selected for the OCL being edited
is shown in the upper right "Selected Conditions in current OCL"
window. The list of all conditions in the database is in the lower
left "List of Conditions" window. The current OCL list that is
selected is highlighted and its contents displayed in the "Selected
Conditions" window. User defined annotation associated with the
current OCL are displayed in the right "Current OCL Annotation"
window.  To add a new OCL, click on the Add OCL button to
define the new condition name.  The Remove OCL button is used
to delete a named condition list. The List OCL button pops up a
report listing the conditions and annotation for the current OCL. The
List All button pops up a report listing the the names of all
of the OCLs and the annotation names. You may add or remove new
annotation names for all of the OCLs. The Add Ann button will
add the new annotation you enter into all conditions - you must enter
the data for each condition that requires it. You may The Save
the current status of all of the OCLs into your working database. If
you have pressed Cancel before saving, then you will not have
saved your edits. Pressing the Done button will save the
changes and pop-down the window.


    


    







 MAExplorer - Microarray Exploratory Data Analysis



 
 

    

    2.3 Edit menu
    

The Edit menu operations include operations to modify the
'edited gene list' that is set from a variety of Filters as well as
manually this menu. The user may perform set operations (union,
intersection, and difference) on named sets of gene and sets of sample
experiments (conditions). User preferences are also set in this menu.
    

Sets of genes or HP condition lists are very useful for tracking
complex data-mining sequences of analysis. For example, derived named
gene sets may be used in successive data filters and for reports. For
example, one could do the following experiment given four different
types of HPs for (e.g. virgin, pregnancy, lactation, and
involution)
    
 

    

    

        
 First compare two HPs using a statistical test such as a
 t-test. Then save the resulting set of genes under the name "virgin
 vs. pregnancy". Then compare the next two HPs and save the resulting
 genes under the name "lactation vs. involution". Finally, compute
 the difference of genes found in "virgin vs. pregnancy" that are not
 found in "lactation vs. involution". This resulting gene set could
 then be saved (e.g. with a name "Genes found in virgin
 vs. pregnancy, but not in lactation vs. involution").  Similarly,
 taking the intersection of these two named sets shows genes that are
 common between the two sets. Taking the union shows genes found in
 either of the two named sets.


 
    
 
    

The Edit menu contains the following main selections. All of these entities
and preferences are saved as part of the startup state when you
do a (File | Databases | SaveAs ... DB).
    

  
     
    



    

    

    2.3.1 User edited gene list - the 'Edited Gene List' menu
    

You may define and edit arbitrary sets of genes using the User
edited gene list submenu to modify the 'Edited Gene List'
(EGL). This has sub-modes of operation for adding or removing genes
from the image by clicking on spots. If the Show 'Edited Gene
List' mode is set, you may see exactly which genes you have
defined by the magenta squares drawn around each gene in the EGL. Many
of the clustering operations will leave the current cluster in the
EGL. The commands include:
    


 
    
 

This gives you the functionality of adding and deleting genes from a
user defined list of genes to be analyzed.  The EGL may be used with
the gene-set operations discussed in     Section
2.3.2. You may also define genes in the EGL using the "Gene Name
Guesser" shown in Figure 2.3.1.
    



    


    

Gene 'Guesser' used to find a particular gene or subset of genes by key phrase


    

Figure 2.3.1 Edited Gene List defined from the Gene Name Guesser
using wildcards. The Edited Gene List was defined as the set of
genes containing the sub-string "onco" in it. The sub-string was
specified to the popup guesser window as "*onco*" using '*' characters
as wildcard symbols indicating that it should match any or no
characters.  The button Gene Name may be toggled through a set
of other identifiers including Clone ID, UniGene ID, dbEST 3', dbEST
5', GenBank 3', and GenBank 5', LocusID, etc. depending on what
identifiers are available in your database.  The user then pressed the
Set E.G.L. button on the guesser window that sets the E.G.L. to
those genes. If you have enabled the View menu "Show 'edited gene
list', then the genes in the EGL. are viewed as magenta squares seen
in the pseudoarray image. You many to do additional editing to
manually add or remove genes that you want to change in the set. If a
2D scatter plot was being used, EGL labeled genes would appear there
as well. To select a particular gene as the current gene, click on the
gene you want in the list, then press the Done button.


    

    



    

    2.3.2 Sets of genes menu 
    

These commands let you do comparisons of sets of genes generated under
different criteria. In addition, you may compute derived gene sets
from existing gene sets using set operations (OR, AND,
DIFFERENCE). You may also normalize the data by a gene subset. The
user may save the genes defined by: 1) by the Filter, or 2) the
manually defined 'Edited Gene List'.  The gene set resulting
from a binary gene set operation OR (union), AND (intersection), or
DIFFERENCE are saved in a new named gene set.  The set difference (A-B) is
defined as the gets in set A that are not in set B. Genes in set B
that are not in set A are ignored. The 'User Filter Gene Set' may be
set to any gene set and may then be used as part of the gene Filter
cascade.  The 'User Normalization Gene Set' may be set to any gene set
and may then be used to normalize gene intensity values across
hybridized samples. (See  normalization
algorithm for more information on this method.)
    


     
If you are running MAExplorer in stand-alone mode, the current named
gene sets are saved when you save the DB using the Save disk DB
or Save as disk DB selections in the Databases submenu of the
File menu. The gene sets are saved in a State sub directory as
".cbs" files and are used to restore the gene sets when restarting
MAExplorer on a .mae startup file. The .mae startup file saves the
names of the .cbs files that are shared among the various startup
files for a given project. The implication then is that if you change
and save a gene set in one startup database, it will change in other
startup databases when they load that gene set. The advantage is that
different startup databases may view a gene set produced by another
database.


The Sets of genes operations in the Edit menu include:

   
  
    


    

The following is an example of List saved gene sets state
listing the catalog of named gene subsets in some of the MGAP
data. Note that sets #1 to #11 are fixed by the data in the GIPO file
and may not be changed by the user. Sets #12 to #14 are assignable
from other sets or in the case of the E.G.L, by various MAExplorer
operations. Sets #1 through #14 may not be removed whereas #15 and
higher may be removed.
    

   

       User Gene Sets
   Set# |#genes| title
   =======================
    #1 |1727| ALL GENES
    #2 |394|  ALL NAMED GENES
    #3 |246|  ESTs similar to genes
    #4 |456|  ESTs
    #5 |1096| All genes and ESTs
    #6 |1681| Good genes
    #7 |40|   Replicate genes
    #8 |0|    HousekeepingGenes
    #9 |96|   Calibration DNA
    #10 |77|  Your plates
    #11 |46|  Empty wells
   --------- User Assignable ----------
    #12 |0|   User Filter Gene Set
    #13 |60|  Edited Gene List
    #14 |0|   Normalization Gene Set
   --------- User definable------------
    #15 |60|  The 60 genes closest to Carbonic Anhydrase-III
    #16 |30|  Named genes in the 60 genes closest to CA-III
    #17 |4|   Replicate genes in the 60 genes closes to CA-III

    



    
The following figure illustrates selecting sets by name for gene set
operations.


    


    

Gene subsets may be computed from other gene subsets using Boolean operations


    

Figure 2.3.2 Selection of gene sets for binary gene set
operations. This example computes the Boolean AND of two sets "ALL
NAMED GENES" and "60 genes closest to CA-III from Named and Ests", and
then the AND of the "Replicates" with the previous result. The first
result is save in the set called "The 60 genes closest to Carbonic
Anhydrase-III".  The second result is saved in the called set "Named
genes in the 60 genes closest to CA-III".  Finally, the third result
is saved in the set named "Replicate genes in the 60 genes closes to
CA-III".


    

    




    

    2.3.3 Sets of sample conditions menu
    

In addition, MAExplorer can operate on sets of hybridized samples. For
example, a sample set might be replicate hybridized samples from the
same biological experiment sample, or it could be repeated experiments
of different but the same types of samples. (One must be careful in
mixing data between the two cases because of the different expected
sources of variance). This means you can treat multiple replicate
samples as a distribution and compare the mean values for each gene in
one set of samples with the mean values for another set of samples. We
call these sets of hybridized samples conditions lists or HP
lists.  You may then put one or more HP samples into a condition
set.  These sets in turn can be used for computing statistics on
clonal differences between different condition sets. Note each
condition set may have multiple (i.e. different) samples.  These
condition sets are saved with the user state when doing a
(File | Databases | SaveAs DB).  As with sets of genes, there are a
number of operations to manipulate HP condition set in the Sets of
Conditions menu that includes:
    
 

 
    
   

    

The following is an example of List saved HP condition lists
state listing the catalog of named HP condition lists.

   

       Condition Lists
   ===============
   Condition[1] #HPs 2, [Initial HP-X: C57B6 pregnancy day 13]
   Condition[2] #HPs 2, [Initial HP-Y: Stat5a (-,-) pregnancy day 13]
   Condition[3] #HPs 4, [Initial HP-E expression list]


    


    

The following is an example of List contents of saved HP condition
list state.

   

       Condition List #1 [Initial HP-X: C57B6 pregnancy day 13]
   ====================================
   HP[1] Pregnancy 13  (1 hr) [C57B6-p13-totalRNA5ug]
   HP[2] Pregnancy 13  (1 hr) [C57B6-p13.2poly-A]


    


    

 

    

    2.3.4 Setting user preferences menu
    

The Preferences submenu is used to set various data labels,
statistical limits and other parameters. These include: 
    
 

 
    
 
    


     The Font Family submenu is used to set
the text font family. This may be useful if your computer is missing
some fonts or some fonts are easier to read than others. Note: some
fonts may not work well on your computer. If this is the case, try
another font. When you save the data mining session with the "SaveAs
file DB", it also saves the font you have set. For some plots or popup
text-windows, you may have to regenerate the popup window to see the
font changes.
    
 

    

 
    
 
      

    

    



    

    

    

Popup window allowing you to adjust all threshold slider values


    


Figure 2.3.4.1 Popup window allowing you to adjust all threshold
slider values">. The Adjust all Filter threshold scrollers
command allows you to pre-adjust all threshold slider values used in
data filtering and in clustering. It may be easier to set the
approximate range before invoking the clustering operation because
changing a parameter will recluster your data.



    

    


    

    



    

    

    

Dialog query to change HP-X Condition set name


    


The Define HP-X (HP-Y) class name command may be used to change
the names of the HP-X (HP-Y) experimental condition sets. These names
are used in various labels in the main window, popup plots and
reports, etc. The commands to change various names of database
components are in the Preferences submenu in the Edit menu.


    

    


    

    


    







 MAExplorer - Microarray Exploratory Data Analysis



 
 

    

    2.4 Analysis menu
    

The Analysis menu (see 
Figure 1.4) contains an ordered list of six primary menus that
may be used, in that order, to perform an initial analysis. In more
complex analyses, the sequence of operations will vary and include
commands selected from other menus or will use these menus in
different order. The Analysis submenus are as follows:
    


    
  
     2.4.1 GeneClass Menu
  
     2.4.2 Normalization Menu
  
     2.4.3 Filter Menu
  
     2.4.4 Plot Menu
  
     2.4.5 Cluster Menu
  
     2.4.6 Report Menu



    


    

    

The 'Analysis' submenus are used for most data-mining operations


    


Figure 2.4 MAExplorer main window with Analysis Menu. The menu
structure of MAExplorer was designed to allow users to quickly perform
commonly used data-mining operations as a first approximation
analysis.



    


    







 MAExplorer - Microarray Exploratory Data Analysis



 



    

    2.4.1 GeneClass menu
    

A gene class (e.g. all named genes, ESTs, oncogenes, etc.) is a set of
genes that belongs to the class of genes in the universe of genes in
the particular microarray database.  MAExplorer may restrict the set
of genes by "Gene Class" membership (currently includes All Genes, All
named Genes, ESTs similar to genes, Unknown ESTs, All genes and ESTs,
Good genes, Replicate
genes (i.e. with more than one copy of the gene in the array),
Calibration DNA, genes from user's plates). The additional Gene Class
list of names depends on its availability in a specific database.


    


    

GeneClass menu used for selecting a particular subset of genes by gene class membership


    

Figure 2.4.1.1 Gene Class menu. The user may select a subset of
genes that belong to one of the classes of genes. This shows the
user selecting the set of "All named genes" that are indicated with
red (white) circle over the spots in the array intensity (ratio)
pseudoarray image.


    




    


    

The GeneClass 'Replicate genes' showing all genes occurring more than 
once in the array


    


Figure 2.4.1.2 Example of all replicated genes occurring more than
once in the array. This was selected by using the GeneClass
'Replicate genes'. You may use the data Filter "Filter by genes with
replicates" instead of the GeneClass. This has the advantage that you
may use other GeneClasses (e.g. ESTs, or All named genes, etc.).
Alternatively, you can find all of the replicates for a particular
gene by 1) use the Gene Guesser to find the particular gene you want;
2) press "Set E.G.L." to save it as an Edited Gene List; 3) enable the
Filter "Filter by E.G.L." at the same. This will show all occurrences
of that gene.



    


    

The set of all genes constitutes a number of different gene
classes. It is possible to restrict the subsequent analysis to a
particular subset of these genes called a gene class.  The
GeneClass menu operations include operations to select the
current set of genes to analyze from the set of all genes by their
membership in a gene class.


 
    


Some of the above gene classes are deduced from the gene name supplied
with the Gene In Plate Order (GIPO) file for the array. We use the
following automatic classification rules shown in Table 2.4.1.
    

    


    
Table 2.4.1 Rules for the automatic classification of gene names
into the default Gene Class sets. The gene name is analyzed
alphabetic-case independently.

    


    


    

    
Gene class 
 Rule for class membership

    


     
All genes 
 all genes on the array

    


     
All named 
 genes not starting with "EST" 

    


     
ESTs similar to genes 
 genes starting with "EST,"

    


    
ESTs 
 genes with the name "EST"

    


    
Replicate genes 
 genes with multiple copies

    


     
Calibration DNA
 genes using the configuration file name "calibDNAname" (optional - see

Appendix Table C.4.1 )

    


     
Your plates 
 clones using the configuration file name "yourPlates" (optional - see

Appendix Table C.5.1-C))


     Empty Wells  empty wells where no spot exists
on the array indicated by keywords "empty", "empty well" or
"EmptyWell" (optional - see

Appendix Table C.5.1-C) )


     
Good Genes 
 spots on the array where the GIPO QualCheck data was used and was 
valid. If it was not used, then it assumes all spots are good. (optional - see

Appendix Table C.4.1 )

    

    
    

    

    

    



    

    2.4.1.1 GeneClass ontology subsets [Future]
    

If the Set Gene Class subset were activated, it might
include categories such as the following. If the categories exist and
the data is made available to MAExplorer, then it is possible to
specify gene subsets by Gene Class name. It is the responsibility of
the database creator to define a mapping table supporting these named
subsets of named genes. 
    




      

    • Apoptosis

    • Bcl-2 family

    • Cdk inhibibition

    • Cell adhesion

    • Cell cycle

    • Cell surface

    • Chemokines

    • Cyclins

    • Cytokines

    • Cytoskeleton

    • DNA binding

    • DNA recomb

    • DNA repair

    • DNA synthesis

    • G-proteins

    • Growth factors

    • Heat shock

    • Interferons

    • Interleukins

    • Ion channel

    • Milk Genes

    • Motility

    • Oncogenes

    • Proteases

    • Protein turnover

    • Receptors

    • Receptors

    • Signal transduction

    • Stress response

    • Transcription

    • Transport

    • Tumor suppressors

    

    

    

    


     

    2.4.1.2 Simulating Gene Class ontologies using Gene Set
operations
    

You can effectively implement finding ontology subsets for Gene Class
subsets using the following procedure. The trick is to repeatedly
define an E.G.L. gene subset using the gene name guesser to find the
genes of interest and save it as a named gene subset. Edit out genes
you don't want. Then you would repeatedly do the OR of gene sets of
interest, saving the result as a new named set. Then doing the OR of
another gene set with the set you just created, etc.


    Procedure
    


    

    

    


    

      

    1.  Use the Gene
Name Guesser (see Figure 2.3.1) to collect genes belong to a particular
ontology. Pressing "Set E.G.L" puts the genes from the guesser into
the EGL set. You can then delete any genes that don't belong by
clicking on the gene in the pseudoarray image with the SHIFT key
pressed.


    2.  Save the EGL as a name gene set with a meaningful name
representing the ontology construct using the (Edit | Save 'Edited
Gene List' as named gene set) (see Section 2.3.2 for gene set
editing commands).


    3.   Repeat steps [1] and [2] to gather additional sets.


    4.   Merge appropriate gene sets to get the complete ontology
constructs using the (Edit | OR (Union) of 2 gene sets) as required.


    5.   Finally, set the (Edit | Assign 'User Filter Gene Set') to
select a particular gene class to use, and then


    6.  Enable the data Filter by setting (Filter | Filter by 'User
Filter Gene Set' membership) (Section 2.4.3).


    
    

    


    

    

    


    







 MAExplorer - Microarray Exploratory Data Analysis




 

    

    2.4.2 Normalization menu
    

The Normalization menu operations include operations to
normalize gene intensity data between hybridized samples. This is
critical in being able to compare samples because of differences in
amount of sample, labeling efficiency and variations in scanner
operation including gain and baseline settings. There are several
methods available including normalizing by Zscore, median, log mean,
Zscore of logs, calibration DNA, housekeeping genes, etc. The specific
microarray image 
quantification is determined the image analysis program being used
to pre-process the arrays.
    


    
Note: although this set of normalization methods is limited, it is
adequate for some analyses of the data. We are in the process of
adding more normalization methods through MAEPlugin methods.

    

    

  
     
    





    2.4.2.1 Intensity background correction
    

The background intensity data from the spot quantification programs
may be used to correct spot intensity.  Background may be specified as
either a global value or on a per-spot basis. If the array images have
low background, then this may not be too much of a problem if no
background values are available.
    

Some software quantification software (e.g. Research Genetics'
Pathways 2.01) measures background globally as: BGLow (low
background), BGAvg (Average background), BGRms (root mean square
background). For MGAP, MAExplorer uses the BGLow value when you
request background subtraction.  These values are read from the
MAExplorer Samples DB file (see  Appendix
Table C.2.1.1 For other quantification programs, background may be
available on a per-spot basis in the quantification files. It the
latter is available in your data, it will be used if background
correction is enabled (see 
Appendix C.3).
    

The background corrected intensity I'ij is
computed from the raw intensity Iij and background
intensity bkgrdHPi for H.P. i and spot j as
follows:


          I'ij = Ij - bkgrdHPi

    

    


    

    Ratio computation for Cy3 and Cy5 data
    

For most MAExplorer operations, the intensity of a gene is generally
computed as the mean intensity of the spots (background corrected or
not) which duplicate that gene on the microarray. When working with
dual hybridized samples using Cye-3 and Cye-5-dUTP labeling that results
in green and red fluorescence, this can be used in self-normalizing
intensity for each hybridized clone array using the Cy3/Cy5 ratio. If
local background is available, then the ratio can be computed for HP h
and spot j as

      (Cy3hj - BkgrdCy3hj) / (Cy5hj - BkgrdCy5hj) 
  
    



    

    2.4.2.2 Normalization between microarrays to allow comparison
    

The normalization of quantitative data is crucial when comparing data
between different microarray samples. There are a      number of different schemes
possible. One is to normalize by the sum of known calibration,
housekeeping genes or other "constant expression" genes in the
microarray.  Another is to sum the background corrected integrated
density for all spots in an array and to normalize individual gene
measurements by that sum. These methods are now described in more
detail.  As the  MAEPlugins
facility becomes available, we will be adding a number of more
sophisticated gene-specific normalization methods that take many of
the problems specific to microarrays into account.




    Normalizing by scaled Zscore of intensity
    

The "normalized Zscore of intensity" method normalizes each hybridized
sample by the mean and standard deviation of the raw intensities for
all of the spots in that sample. The mean intensity 
mnIi and the standard deviation 
sdIi are computed for the raw intensity of 'Good
genes'. It is useful for standardizing the mean (to 0.0) and the range
of data between hybridized samples to about -4.0 to +4.0. When using the
Zscore, you compute Zdiff(erences) not ratios. The Zscore intensity
Zscoreij for intensity  Iij
for HP i and spot j is computed as


         Zscoreij = (Iij - mnIi)/sdIi,
and
     Zdiffj(x,y) = Zscorexj - Zscoreyj.

    



    

    Normalizing by the median of intensity
    

The "Median intensity" method normalizes each hybridized sample by
the median of the raw intensities of 'Good genes' for all of the
spots in that sample. It is a useful normalization to use when you want
to compute X/Y ratios between hybridized samples.

         Imij = (Iij/ medianIi)

    


    

    Normalizing by the log of median of intensity
    

The "Log median intensity" method normalizes each hybridized sample
by the log of median scaled raw intensities of 'Good genes' for all
of the spots in that sample. The value 1.0 is added to the intensity
value to avoid taking the log(0.0) when intensity has zero value. This
is a useful normalization to use when you want to compute X/Y ratios
between hybridized samples and compress the scale. Because we are computing
a log, we report the difference between HP-X and HP-Y as (X-Y) instead
of a ratio (X/Y).


         Imij = log(1.0 + (Iij/ medianIi))

    


    

    Normalizing by scaled Zscore of log intensity, standard deviation
    

The "Normalize by Zscore of log intensity, stdDev" method normalizes
each hybridized sample by the mean and standard deviation of the
logs of the raw intensities for all of the spots in that sample. The
mean log intensity mnLIi and the standard
deviation log intensity sdLIi are computed for the
log of raw intensity of 'Good genes'. Then the Zscore intensity
ZlogSij for HP i and spot j is


         ZlogSij = (log(Iij) - mnLIi)/sdLIi

    


    


    Normalizing by scaled Zscore mean absolute deviation of log intensity
    

The "Normalize by Zscore of log intensity, mean absolute deviation"
method normalizes each hybridized sample by the mean and mean
absolute deviation of the logs of the raw intensities for all of the
spots in that sample. The mean log intensity mnLIi
and the mean absolute deviation log intensity
madLIi are computed for the log of raw intensity
of 'Good genes'. Then the Zscore intensity 
ZlogAij for HP i and spot j is


         ZlogAij = (log(Iij) - mnLIi)/madLIi

    



    

    By 'User Normalization Gene Set'
    

This method is useful a subset of genes have been determined to have
relatively constant expression across the set of samples.  It
normalizes by the sum of intensities for a subset of genes defined by
the user in the     'User
Normalization Gene Set' (Section 2.3.2)using the gene set editing
commands.  Normalizing by the sum of genes uses the 
Igsij that is computed for microarray HPi
with intensities Iij for all genes j in
the gene subset.


         Igsi = Sum (Iij)
        	   genes j
        	   i in HPi

    

 Then, the normalized intensity I'ij is computed as:


          I'ij =  Iij/Igsi

    


    




    By 'Calibration DNA' set
    

If a predefined set of calibration DNA genes are available on the
array, they may be used to normalize density values between the
samples. The calibration DNA genes are defined by special gene names
that are declared in the Configuration file using the 'calibDNAname'
parameter (see Appendix C Table C.5.1(C)). If
there is no calibration DNA, this entry is not used.  The algorithm is
the same as "User Normalization Gene Set" (above), but the set is
predefined as the genes flagged as calibration DNA.  For example, in
the MGAP database, these spots are the "mouse genomic DNA" spots so
the Configuration file entry would be calibDNAname="m.g. DNA".

    




    Scaling intensity data to 65K
    

Another method "Scale intensity data to 65K" scales the maximum
intensity of each sample to 65K (the maximum intensity). Since the
raw scanned data is often 16-bits, it can have a maximum value of
65535 (216-1) and so this does minimum scaling. This method
may make it easier to view the data initially using the 
pseudoarray image. However, it may not properly scale the data between
arrays and should probably not be used in quantitative comparisons.   

    



    No normalization
    

You may also want to look at the raw intensity (or Cy3 and Cy5
channel) data. Turning off normalization gives you the raw data read
into MAExplorer.

    




    2.4.2.3 Using different normalizations to 'see' different data views
    

Changing the normalization method will sometimes make differences
between data sets more apparent. The following figure shows the same
data in two different scatter plots but with two different
normalizations.



    


    
A) 
Scatter plot using the Median normalization


    

    
B) 
Scatter plot using the Log Zscore normalization


    



Figure 2.4.2.3 Scatter plot of HP-X and HP-Y 'sets' data. HP-X
is C57B6 pregnancy day 13 and HP-Y is Stat5a (-,-) pregnancy day 13
filtered by "All named genes and ESTs".  A) A scatter plot
using the Median normalization.  B) A scatter plot using the
Zscore of the logs normalization. Notice how the Casein alpha outlier
is more apparent in the case of the Zscore log normalization. The
skewed plot is characteristic of much microarray data. Some
normalization methods (not currently included in MAExplorer) can
compensate for these some of these artifacts (Dutoit, 2000) and are planned for
future MAEPlugins.



    

    

    

    



    







 MAExplorer - Microarray Exploratory Data Analysis



 


    

    2.4.3 Filter menu 
    

 
The final set of genes presented for display, plotting, reports, etc.
is determined by a cascade of gene "data filters" that generate a
restricted gene set. The cascade is computed in real-time using the intersection of individual
criteria and tests selected by the user.  Examples of Filter
criteria include: membership in a particular gene set, ratio
(HP-X/HP-Y) within a range, passing statistical tests such as t-tests
or F-test, etc.


    


    

Filter menu and Ratio filter submenu showing options and 
Preference State sliders of active filters


    


Figure 2.4.3 Filter menu. The Filter menu is a cascade of data
filters that restrict the set of genes passing all filters that
have been enabled and whatever the criteria was that was set for those
filters. This figure shows the GeneClass filter set to "All genes and
ESTs", the spot CV filter and Ratio (X/Y) range filters being set
interactively by the scroll bars on the right. The genes that pass
the filter are indicated with a red (white) circle in the array
intensity (ratio) pseudoarray image.


    


    

The Filter menu options are used to restrict the set of genes
by pre-filtering the data with a series of cascaded filter criteria
and tests. The resulting subset of genes passing the filter are then
used in the plots, reports and other data analysis methods. Some of
the filters require additional parameters that are set by the State
scrollers. The user will automatically be prompted for changes to
these scollers (a threshold scrollers window will pop up) when the
filter is activated or change. These values may also be set from the
Adjust all Filter threshold scrollers entry in the
Preferences submenu in the Edit menu. The filters are
broken up into subgroups in the following menu with the grouping
haveing more to do with the criteria (i.e. gene set membership, data
range, or statistical tests).

 
    


     The Filter by positive intensity
data submenu filter contains options that specify which spot
intensity values are to be considered when excluding negative
quantified spot data. Note: this filter only makes sense if your data
might have negative values (e.g. Affymetrix chip "Avg Diff" data) or a
background corrected value that is less than 0.0. The filter is
enabled by setting the "Filter by spots with positive intensity"
checkbox.  Negative intensity values may occur with some types of
arrays quantification programs. In the "Check spots for positive
values mode" submenu, you may set the samples where the test may be
applied to spots from the current HP, the single (HP-X,HP-Y) samples,
(HP-X,HP-Y) 'sets' (replicated spots), or samples in the HP-E list
selected to be used in the filter. If there are (F1,F2) or (Cy3/Cy5)
data, then each spot must meet the threshold criteria.


 
    



    
The Filter by Good Spot data submenu filter contains options
that specify spots based on their quality. It filters out genes that
have that do not have "Good Spot" values defined by the optional
QualCheck spot data. (See the list of codes in Appendix C.4). If there is no
such spot quality data, then all spots are considered "good".  The
filter is enabled by setting the "Filter by spots with Good Spot
values" checkbox.  All spots for the specified samples must meet the
criteria. In the "Check spots for Good Spot mode" submenu, you may set
the samples where the test may be applied to spots from the current
HP, the single (HP-X,HP-Y) samples, (HP-X,HP-Y) 'sets' (replicated
spots), or samples in the HP-E list selected to be used in the filter.


 
    





     The Filter by Spot Detection Value data submenu filter
contains options that specify spots based on their spot detection
value quality metric over the range of [0.0 : 1.0]. The filter is
available only if the data exists for your database and is ignored
otherwise.  If active, it pops up a "Spot Detection Value" slider in
the range of [0.0 : 1.0]. Only spots greater than the slider value
pass the filter.  This data could be the Affymetrix MAS5.0 "Detection
p-value" or some other metric correlated with spot detection quality.
The filter is enabled by setting the "Filter by per-sample Spot
Detection Value" checkbox.  All spots for the specified samples must
meet the criteria. In the "Check spots for Spot Detection Value mode"
submenu, you may set the samples where the test may be applied to
spots from the current HP, the single (HP-X,HP-Y) samples, (HP-X,HP-Y)
'sets' (replicated spots), or samples in the HP-E list selected to be
used in the filter.


 
    





    

     

The Filter by spot intensity [SI1:SI2] sliders submenu contains
options that determines how individual spot intensity thresholding is
to be applied in the Filter.
 
 
    


The Use data mode submenu filter contains options that specify
which spot intensity values are to be considered of the single sample
(F1 and F2 replicated spot intensity data, or Cy3/Cy5 for ratio data),
or the (HP-X,HP-Y) 'sets' of replicated samples is to be used in the
filter. If there are single sample (F1,F2) or (Cy3/Cy5) data, then
each spot must meet the threshold criteria.

 
    



The Compare channels meeting range submenu specifies which
additional constraints are to be used. If required by the (AT MOST
channels, AT LEAST channels, PRODUCT OF channels,
SUM OF channels) commands, the Percent SI OK scroll
bar will appear which covers the range of 0% to 100%.


 
    




    

The Filter by [I1:I2] sliders submenu contains options that
determines how spot expression (intensity or (Cy3/Cy5) ratio value)
thresholding is to be applied in the Filter:


 
    



    

The Filter by ratio or Zdiff sliders submenu contains options
that determines how spot-ratio thresholding is to be applied in the
Filter. The spot ratio is mean HP-X / mean HP-Y for sets of
samples. The spot Zdiff is used if one of the Zscore normalization
methods is active and is computed as (mean HP-X - mean HP-Y) for sets
of samples.
 
 
    




    
The Filter by Cy3/Cy5 HP-X ratio or Zdiff sliders submenu
contains options that determines how spot Cy3/Cy5 HP-X ratio
thresholding is to be applied in the Filter. The spot ratio is Cy3/Cy5
for normalized data unless one of the Zscore methods is used. In that
case, the Zdiff is used and is computed as (Cy3 - Cy5) for sets of
samples.  If HP-X 'sets' is used, then it computes the mean Cy3 value
and the mean Cy5 value and uses those values in the above
computations.

 
 
    



     
The Filter by spot CV  submenu filter contains options that
specify how the Coefficient Of Variation of the (F1,F2) or (HP-X,HP-Y)
'sets' (replicated spots) is to be used in the filter. The (F1,F2) CV
is available only if there are duplicate spots on the HPs.

 
    




    

    Filtering using statistical test by your selecting a p-value
    

These tests will filter genes meeting
the test criteria if the resulting p-value of that test is <= the
value specified by the p-Value state slider. Only one test may be
active at a time. If you switch to a new p-value test, it will disable
the previous p-value test.  If any of these tests are selected, it
will pop up the p-Value state slider window for you to set the
p-Value. There are two t-tests: one operating on duplicate (F1,F2)
data if available, and the HP-X,HP-Y 'sets' if they are defined. The
Kolmogorov-Smirnov test operates on HP-X,HP-Y 'sets' if they are
defined.  The F-test operates on the  current Ordered
Condition List (OCL) consisting of any number of  condition lists each
containing at least 2 (replicate) samples/condition.

 
  •   Filter by current Ordered
      Condition List (OCL) F-Test [p-Value] slider [RB] - only
      include genes that meet the F-test criteria on the current
      OCL. This only works if there are at least 2 (replicate)
      samples/condition for each of the condition sets in the OCL.
      See info on defining
      the OCL and 
      using the OCL data.


    

    Filtering out genes with high replicate spot variation
    

The Spot CV filter mode submenu contains options to select how
the spot CV filter is to be applied. It computes the maximum value of
CV for all of the samples in the particular sample set specified. That
maximum value is then used for the spot CV filter test.  Genes may be
filtered out having a large difference between spot quantification
values of corresponding duplicate spots. You may compute the
coefficient of variation CVj for the two values
(f1j and f2j for a particular
gene j. 


        CVj = 2|f1j-f2j|/(f1j+f2j)

    

If the database only has one field but replicate HPs, then you may use
the HP-X & HP-Y 'sets' CVj to filter the
genes. Then CVj values are tested against a CV
threshold slider value to eliminate genes with a high coefficient of
variation.

    



 
      
 
    •  
      Current HP [RB] - CV of (F1,F2) for each gene in current
      sample [if duplicate spots are available on each sample]
 
    •   HP-X or HP-Y [RB] -
      CV of (F1,F2) for HP-X and HP-Y single samples [if duplicate
      spots are available on each sample]
 
    •  
      HP-X 'set' [RB] - CV of spots in HP-X set
 
    •  
      HP-Y 'set' [RB] - CV of spots in HP-Y set 
 
    •  
      HP-X or HP-Y 'sets' [RB] - CV of spots in the HP-X set or HP-Y 
      set 
 
    •  
      HP-X and HP-Y 'sets' [RB] - CV of spots in both the HP-X
      set and HP-Y set
 
    •  
      HP-E [RB] - CV of HPs in expression profile list
 
    




    

    2.4.3.1 Data filtering using multiple gene data filters
    
 
Any or all of the data filters may be selected simultaneously. In
particular, if you select filters that use parameter threshold
scrollers, they will be added to a state scroller window (see  Figure
2.3.4.1 for details to allow adjustment of ALL sliders
simultaneously). You may change various thresholds and see the effect
in real time. Note: some of the scrollers are more sensitive to low
values. Therefore, we set them to respond non-linearly with a more
precise vernier at the low end.


    


    

Filtering using multiple filters (Spot Intensity and Ratios of samples) 
and multiple thresholds


    


Figure 2.4.3.1 Filtering using multiple scrollers. This example
is of Cy3/Cy5 time series data. It filters normalized spot intensity
of the Cy3 and Cy5 channels independently ([SI1:SI2] inside range)
where low intensity spots are eliminated. It then filters out genes
outside of the [R1:R2] ratio range.

    



    


    

Filtering using positive intensity data - ignoring negative data


    

Figure 2.4.3.2 Using the Positive Intensity data Filter. 
This allows removing negative data if the data contains negative 
intensity values (e.g. Some Affymetrix data has negative Average Difference
values which could be read as Intensity for MAExplorer).



    


    


    

    


    







 MAExplorer - Microarray Exploratory Data Analysis



 


    

    2.4.4 Plot menu
    

The Plot menu lets you display a pseudoarray image, scatter plots,
ratio and intensity histograms,and expression profile plots. The
pseudoarray image is displayed in the main MAExplorer window. All of
the other plots are displayed in popup windows. Depending on the
particular plot, multiple instances may be allowed.  The Plot
submenus are:

 
    

You may switch between different representations of the microarray
spot pseudoarray image. It may be viewed as several different types of
pseudo images including an intensity gray value and a pseudo-color
Red/Black/Green image for ratio (HP-X/HP-Y) and Zscore (HP-X - HP-Y)
data. The p-Value results of comparing a HP-X 'set' with a HP-Y 'set'
of samples, or the CV of the HP-EP 'list can be displayed as a color
spectrum pseudoarray image.
    

Depending on the origin of the array data, it may have the same
verisimilitude as the original arrays.  Otherwise, it is displayed in
a generic pseudoarray image containing grids that will fit the window
- these are not the same as the original array image (see . However, the
pseudoarrays are useful to getting a rough idea of the global changes
in the data between arrays and how may genes pass the data filter.
    

When enabled using one of the commands in the Section 2.4.5 Clustering menu, cluster data appears as
 blue circles or squares drawn as overlays on
the pseudoarray image. These options are discussed in the section on
clustering. If you are doing clustering K-means clustering, the
current cluster is displayed in the scatter plot if the latter is
active.
    

Scatter plots, ratio and intensity histograms of the mean (HP-X/HP-Y)
or (HP-X/HP-Y) 'set' data, or the  F1/F2 or Cy3/Cy5 data.  F1/F2
or Cy3/Cy5 plots are available if the data exists in your particular
database. That might be the case with replicate spots or with Cy3/Cy5
data. If the normalization is set to a Zscore or log mean mode, it
will compute Zscore scatter plots and histograms.
    



     
Clicking on spots in an array image or points in scatter plots sets
the current gene and will bring up data on the gene or (optionally)
access corresponding data from GenBank, UniGene,  mAdb Clone, etc. databases in a
popup Web browser.  Clicking on a bin in a ratio or intensity
histogram plot filters out all genes except for those in the range of
that bin.
    
  
Expression profiles plots of selected genes or subsets of genes for
all samples in the HP-E list. These are active plots with data reported
when the user clicks in the plot.
    



    

Clicking on a spot (i.e. gene) in the microarray pseudo image or on a
point (i.e. gene) in the scatter plot, it will define that gene as the
"current gene" that is used in other operations. The current gene is
indicated in both plots with a  green circle 
around it. Similarly, you may  modify
the 'Edited Gene List' from either the pseudoarray image or the
scatter plot. When viewing is enabled, it overlays those genes with
magenta squares.
 


    


    

Plot menu - selecting Ratio Pseudoarray image


    

Figure 2.4.4 Plot menu - selecting Ratio Pseudoarray
image. This displays a pseudocolor show in the scale on the left that
indicates the ratio of the value of the HP-X sample / HP-Y sample (or
'sets' if the option to use HP-X and HP-Y 'sets' is enabled.) If The
data is Cy3/Cy5 data, then this displays the ratio of the ratios using
the current normalization. Various other pseudoarray image
representations could be used.




    

    2.4.4.1 Show microarray pseudoarray images menu
    

You may show the pseudoarray
image of the current hybridized samples using several
modalities. The grayscale pseudoarray image is generated from the
quantified spot data. If the data contains the actual spot positions
of the genes (as generated by the various array image quantification
program), the spots may be drawn using a scaled version of those
coordinates. Otherwise, a generic set of grids (and fields in there
are multiple fields) is synthesized to represent the spot
positions. Pseudoarray images may also be useful as an alternative
modality for displaying X/Y ratio or X-Y Zdiff data.  If the
normalized intensities are the same, then the spot will appear as
black with the overall spot intensity depending on the spot
concentrations. High ratios and Zdiffs will be red and low values
green as shown in  Table
2.4.4.1.  The p-Value results of comparing a HP-X 'set' with a
HP-Y 'set' of samples can be displayed as a color spectrum pseudoarray
image. 
    


    

If the database that was loaded contains only one sample, the
pseudoarray image display defaults to the pseudograyscale spot
intensity mode. If there is at least one HP-X and one HP-Y sample,
then the Pseudocolor HP-X/Y ratio or Zdiff mode is the initial
default display.  If there are duplicate spots for each gene, you may
generate a Pseudocolor F1F2 ratio or Zdiff mode image. If you
are using Cy3/Cy5 ratio data and the data is available as independent
channels for each HP, then you may plot Cy3 vs Cy5 for individual
samples.
    

When available on the database server, the original image may be
displayed in a separate popup Web browser.



 
    




    

    

    

Table 2.4.4.1. Pseudocolors assigned to spots to represent data in
the X/Y ratios or X-Y Zdiffs pseudocolor array images. Each color
represents the normalized X/Y ratio or X-Y Zdiff depending on
Normalization mode. The 9 colors of the boxes represent the normalized
expression ranges.

    


    


     
.
.
.
.
.
.
.
.
.
Normalization
     mode - RBG    		

    



     
bright green
.
.
 dark green
Black
dark red
.
.
bright red

    


     
.
.
.
.
.
.
.
.
.
Normalization
     mode - dichromasy    		

    


     
bright blue
.
.
 dark blue
Black
dark orange
.
.
bright orange

    


     
<0.250X
0.307X
0.400X
0.571X
1.000X
1.75X
2.50X
3.25X
>4.00X 
Ratio data 

    


     
<-3.0
-2.25
-1.50
-0.75
0.00
0.75
1.50
2.75
>3.0 
Zscore data 

    


     
<-0.99
-0.742
-0.495
-0.247
0.000
0.247
0.495
0.742
>0.99 
Zscore Log data 

    


    



    

    

Clicking on a particular gene will report its specific quantification
and identification values (See     
Section 3.3 on gene quantification). If the Enable display current
gene in popup genomic DB Web Browser option is set in the
View menu, then it will also pop up a Web browser with the
corresponding to the particular genomic DB data for that
database if it exists.
    

The same data is shown in a variety of normalization and display formats.


    

    2.4.4.1.1 Examples of microarray intensity data pseudoarray image
    
 
The relative intensity may be displayed for the current sample (last
HP-X or HP-Y selected) or two samples (HP-X or HP-Y samples or HP-X or
HP-Y 'sets' of samples). To show two samples side by side, enable the
Use dual HP-X & HP-Y Pseudoimage in the Show Microarray
submenu. To show averaged set data in the dual mode, enable the "Use
HP-X & HP-Y 'sets' option in the Samples menu.  The grayscale
value reflects the current normalization mode.


    


    

Pseudoarray image show intensity plot for median normalized data for 
a single sample


    

Figure 2.4.4.1.1.1 Pseudoarray intensity image of median normalized
intensities of the current HP sample (C57B6 virgin 10 weeks from MGAP
database).  The graylevel scale on the left edge of the pseudoarray
image indicates the spot intensity. All pseudoarray images have scales
that vary depending on the type of pseudoarray being displayed.



    


    

Pseudoarray image show intensity plot for Zscore normalized data for 
single sample


    

Figure 2.4.4.1.1.2 Pseudoarray intensity image of Zscore normalized
intensities of the current HP (C57B6 virgin 10 weeks from MGAP
database). 



    


    

Pseudoarray image show intensity plot for Zscore Log normalized data 
for single sample


    

Figure 2.4.4.1.1.3 Pseudoarray intensity image of ZscoreLog normalized
intensities of the current HP (C57B6 virgin 10 weeks from MGAP database).



    


    

Pseudoarray image show intensity plot for Zscore Log normalized data 
for HP-X and HP-Y single samples


    

Figure 2.4.4.1.1.4 Pseudoarray intensity image of ZscoreLog
normalized intensities of the dual HP-X and HY-Y individual
samples. The Plot menu Show Microarray submenu toggle "Use dual
HP-X & HP-Y samples" option is set.  HP-X is a C57B6 pregnancy day
13 and HP-Y is a Stat5a (-,-) pregnancy day 13.




    


    

Pseudoarray image show intensity plot for Zscore normalized data 
for HP-X and HP-Y 'set' samples


    

Figure 2.4.4.1.1.5 Pseudoarray intensity image of ZscoreLog
normalized intensities of the dual HP-X and HY-Y sample 'sets'. The
Plot menu Show Microarray submenu toggle "Use dual HP-X & HP-Y
samples" option is set. The "Use HP-X & HP-Y 'sets' option in the
Samples menu.  HP-X is the mean of three 'C57B6 pregnancy day 13' and
HP-Y is the mean of three 'Stat5a (-,-) pregnancy day 13'.




    

    2.4.4.1.2 Example of microarray ratio or Zdiff data pseudocolor image
    
 
The ratio (HP-X/HP-Y) or Zdiff HP-X - HP-Y) normalized intensity data
may be displayed as a pseudoarray image for HP-X and HP-Y, or HP-X and
HP-Y 'sets' of samples. To show averaged set data in the dual mode,
enable the "Use HP-X & HP-Y 'sets' option in the Samples menu.
The colors of the 9 scale boxes represent the normalized expression
ranges and is assigned according to the current normalization mode
listed in the  table.


    


    

Pseudoarray image show color ratio plot for median normalized data
for HP-X and HP-Y single samples


    

Figure 2.4.4.1.2.1 Pseudocolor array image of median normalized X/Y
ratios. HP-X is C57B6 pregnancy day 13 and HP-Y is Stat5a (-,-)
pregnancy day 13.  Each spot's color represents the normalized X/Y
ratio depending on Normalization mode. The color of the box is one of
9 colors representing the normalized expression ranges and assigned
according to the  table "Ratio
mode".



    


    

Pseudoarray image show color ratio plot for median normalized data 
for HP-X and HP-Y 'set' samples


    

Figure 2.4.4.1.2.2 Pseudoarray color image of normalized X/Y 'set'
mean value ratios. Mean of three HP-X C57B5 pregnancy day 13
samples and mean of three HP-Y Stat5a (-,-) pregnancy day 13 samples.
Each spot's color represents the normalized X/Y 'set' ratios depending
on Normalization mode. The color of the box is one of 9 colors
representing the normalized expression ranges and assigned according
to the  table "Ratio mode".



    


    

Pseudoarray image show color ratio plot for |X-Y| Zdiffs normalized 
data for HP-X and HP-Y 'set' samples


    

Figure 2.4.4.1.2.3 Pseudoarray color image of X-Y Zdiffs. HP-X
is C57B6 pregnancy day 13 and HP-Y is Stat5a (-,-) pregnancy day 13.
Each spot's color represents the normalized X-Y Zdiff depending using
the Zdiff normalization mode. The color of the box is one of 9 colors
representing the normalized expression ranges and assigned according
to the  table "Zdiff mode".



    


    

Pseudoarray image show color ratio plot for |X-Y| Zdiff of Log 
normalized data for HP-X and HP-Y samples


    

Figure 2.4.4.1.2.4 Pseudoarray color image of X-Y Zdiff of log
data. HP-X C57B5 pregnancy day 13 sample and HP-Y Stat5a (-,-)
pregnancy day 13 sample.  Each spot's color represents the normalized
X/Y ratio depending on ZdiffLog with StdDev normalization mode. The
color of the box is one of 9 colors representing the normalized
expression ranges and assigned according to the  table "ZdiffLog mode".
 

    


    


    

Pseudoarray image showing color coded p-values for t-test comparison of 
HP-X and HP-Y 'set' samples


    

Figure 2.4.4.1.2.5 Pseudoarray image showing color-coded p-values
for t-test comparison of HP-X and HP-Y 'set' samples. The HP-X and
HP-Y sets both have 2 samples each (more is obviously much better).
The data was normalized using the Median and a spot intensity
[SI1:SI2] data filter was applied to eliminate some of the noisy data.
Each spot's color represents a p-value in the range indicated in the
scale in the left edge of the image. Note that although all spots are
assigned a p-Value, many may not be very significant because adequate
preprocessing of the data (such as normalization, and low intensity
spot removal, etc.). So use this display with care.


    


    

    2.4.4.2 Scatter plots menu
    

Scatter plots include HP-X vs HP-Y intensity for comparing data
between HP-X and HP-Y samples (or sets if the HP-X and -Y sample
'sets' mode is enabled in the Samples menu - as is shown in Figure 2.2.1). You may
zoom into any area of the scatter plot as is shown in Figure
2.4.4.4.2(C).  If there are duplicate spots for each gene, you may
plot F1 vs F2 intensity (or Cy3 vs Cy5 if using ratio data) for
comparing replicate data (or Cy3 and Cy5 ratio data channels) within
the same sample It will also compute the correlation coefficient for
the data and display it in the plot and in the message panel. The data
is the intensity values using the current normalization method. If you
are analyzing ratio Cy3/Cy5 data, you may compare Cy3 or Cy5 of the
HP-X sample against Cy3 or Cy5 of the HP-Y sample. If you are in
stand-alone mode, a SaveAs GIF button will also be
available. This saves the current plot as a full resolution GIF file
specified by the user in a popup file browser window.
      


      rSq=0.974, n=1728, X(mn+-sd)=(4.477+-7.845), Y(mn+-sd)=(12.379+-24.810)

    
 

The Scatter plots submenu includes:

 
      

 
    •  HP-X vs HP-Y intensity - display a scatter plot of HP-X
     vs HP-Y intensity data of H.P. selected (average of F1+F2
     fields). If HP-X/-Y 'sets' are enabled, then the plot is of the
     mean set values.

 
    •  HP F1 vs F2 intensity - display a scatter plot of F1 vs F2
     (or Cy3 vs Cy5) intensity data of the current selected sample

 
    •  --------------------

 
    •  HP-X Cy3 vs HP-Y Cy3 intensity - display a scatter plot of 
     HP-X(Cy3) vs HP-Y(Cy3) intensity data of the currently selected 
     HP-X and HP-Y samples (ratio data only).
 
    •  HP-X Cy5 vs HP-Y Cy5 intensity - display a scatter plot of 
     HP-X(Cy5) vs HP-Y(Cy5) intensity data of the currently selected 
     HP-X and HP-Y samples (ratio data only).
 
    •  HP-X Cy3 vs HP-Y Cy5 intensity - display a scatter plot of 
     HP-X(Cy3) vs HP-Y(Cy5) intensity data of the currently selected 
     HP-X and HP-Y samples (ratio data only).
 
    •  HP-X Cy5 vs HP-Y Cy3 intensity - display a scatter plot of 
     HP-X(Cy5) vs HP-Y(Cy3) intensity data of the currently selected 
     HP-X and HP-Y samples (ratio data only).

 
    


If Cy3/Cy5 ratio data is being analyzed, then the "HP F1 vs F2
intensity" menu entry becomes


 
      
 
    •  HP Cy3 vs Cy5 intensity - display a scatter plot of Cy3 vs 
      Cy5 intensity data of the current sample selected (i.e. being 
      displayed).
 
    


The following figure illustrates some of the scatter plots and zoomed
regions using the scroll bars on the horizontal and vertical axes.
    


    


    
A) 
Scatter plot of single HP-X vs HP-Y samples


    


    
B) 
Scatter plot of single HP-X vs HP-Y 'set's of samples


    


    
C) 
Zoomed scatter plot of single HP-X vs HP-Y 'set's of samples


    




Figure 2.4.4.2 Scatter plot of HP-X and HP-Y single sample
data. HP-X is C57B6 pregnancy day 13 and HP-Y is pactation day 1.
A) An active scatter plot may be generated for the current HP-X
and HP-Y samples filtered by "All named genes".  B) similar
plot for HP-X and HP-Y 'sets' of replicate samples (3 pregnancy and 4
lactation samples in the sets respectively).  Clicking on a point in
the plot sets the current gene. C) Zoomed up region (of
B) at the bottom of the plot showing more detail and filtered
by just "All named genes". Zooming is performed by adjusting the X or
Y axes limits scroll bars. Note the points enclosed in magenta boxes
indicate genes in the E.G.L. gene list.



    

    


    Scatter plots of data from multiple channels on the same sample
    
It is also possible to plot the separate channels within a single sample
against each other. For example F1 vs F2 in samples with replicate
data and Cy3 vs Cy5 in samples with separate ratio data channels.


    


    
A) 
Scatter plot of Field 1 (F1) vs Field 2 (F2) of current HP sample


    


    
B) 
Scatter plot of Cy3 vs Cy5 of current HP sample


    


    
C) 
Scatter plot of HP-X Cy3 vs HP-Y Cy3 channels of different samples


    


    
D) 
Scatter plot of HP-X Cy3 vs HP-Y Cy5 channels of different samples


    


Figure 2.4.4.2.1 Scatter plot of multiple channel data from a
single sample. A) F1 Vs F2 data for a C57B6 pregnancy day 13
sample.  B) Cy3 vs Cy5 data for a NCI mAdb mouse array sample.
C) Scatter plot of individual Cy3 channels from two different
ratio Cy3/Cy5 data hybridized samples.  C) Scatter plot of
individual Cy3 channel of HP-X compared with Cy3 channel of HP-Y for
ratio Cy3/Cy5 data hybridized samples. D) Scatter plot of
individual Cy3 channel of HP-X compared with Cy5 channel of HP-Y for
ratio Cy3/Cy5 data hybridized samples.



    

    




    

    2.4.4.3 Histogram plots menu
    

You may compare ratios or Zdiffs of data using the HP-XY ratios or
Zdiff command to display a ratio histogram of Filtered intensity
data from two samples selected from the Samples menu.  The HP-XY
'set' ratio or Zdiff is used if there are multiple samples in the
HP-X or HP-Y sets, then the mean values in each of the sets is used in
the calculations. If there are duplicate spots for each gene, you may
plot the F1F2 ratio or Zdiff histogram of the F1/F2 ratios or
F1-F2 Zdiff values for normalized data for each spot in the currently
displayed sample.  If you are in stand-alone mode, a SaveAs GIF
button will also be available to save the current plot as a full
resolution GIF file specified by the user in a popup file browser
window.
    

The Intensity selection plots a histogram of the gene intensity
data values for each Filtered spot (gene) in the current hybridized
sample.

    

The Histograms submenu includes:

 
      
 
    •  HP-XY ratio or Zdiff - display ratio or Zdiff histogram of data 
      from selected HP-X and HP-Y samples (mean F1+F2 data if duplicate
      spots). 
 
    •  HP-XY 'sets' ratio or Zdiff - display ratio or Zdiff histogram 
      of mean data from HP-X and HP-Y 'set's of samples (mean data). 
 
    •  F1F2 ratio or Zdiff - display ratio or Zdiff of histogram of 
      F1 F2 duplicate spot data from the current sample.
 
    •  HP Intensity - (or HP (Cy3/Cy5) if ratio data) display a 
      histogram of spot intensity (or Cy3/Cy3 ratio) data for the current 
      sample.
 
    

    

If Cy3/Cy5 ratio data is being analyzed, then the F1F2 histogram menu
entry becomes


 
      
 
    •  Cy3Cy5 ratio or Zdiff - display ratio or Zdiff of histogram of 
      Cy3 Cy5data from the current sample.
 
    


The following figures illustrates the histograms. You may use the
histogram to specify ranges of [I1:I2] or {R1:R2] for data filtering
in the histogram by specifying the corresponding histogram bins. This
is described in the figure legend.
    



    


    
A) 
Ratio histogram of (HP-X/HP-Y) samples data, median normalization


    


    
B) 
Zdiff  histogram of (HP-X - HP-Y) samples data, Zscore normalization


    


    
C) 
Intensity histogram of current sample data, median normalization


    


Figure 2.4.4.3 Histogram plots. A) Ratio histogram of
HP-X/HP-Y data with particular histogram bin selected with the
constraint set to filter all genes > that bin. HP-X is 13 day
pregnancy C57B6 and HP-Y is day 1 lactatation. The selected bin
thresholds are then used in the Filter with the resulting Filtered
genes shown in the array image.  B) Zdiff histogram of HP-X -
HP-Y 'sets' for same data as (A) but with the ><
threshold constraint set to find genes outside of the symmetric
histogram range.  C) Intensity histogram of HP-X data filtered
by [I1:I2] intensity range. As with ratio histograms, you can do
additional filtering by selecting a particular histogram bin that is
then used in the Filter. Filtering was disabled for the intensity
histogram. To apply the filter, the "Don't re-Filter" button would be
toggled to the "Re-Filter" state.  The threshold constraints include:
 =, >, <, >, <>, and ><. Note that each time
you click on the "Thr:" button, it cycles to the next option in the
threshold constraints list.



    

    



    

    2.4.4.4 Expression profile plots menu
    

You may generate an individual expression profile plot (EP plot) or a
scrollable list of EP plots.  The order list of hybridized samples to
plot are specified by the HP-E set.  In the latter case, the genes are
specified by the data Filter.
    


You many generate as many individual expression profile plots as you
want using the Display a gene's expr. profile for HP-E
command. However, only the last one will be active and will be updated
with different genes as you click on them in the microarray image
scatter plot. This could be used to compare the EP plots for several
different genes. First view the EP plot for one gene, then create a
new EP plot for the second gene, etc.
    

If you use the Display Filtered genes expr.  profiles
for HP-E command, it will generate a scrollable list of
expression profile plots for all of the genes passing the Filter. If
the number of genes is very large, it may take a while. 
    

You may interrogate a line corresponding to a particular HP sample in
a EP plot by moving the mouse over the line and then selecting the
line.  This will cause the name of the HP, its intensity and CV to
appear in the plot.  If the Err check box is set, then the mean
of the intensity is indicated by a short horizontal bar and the +- CV
by red vertical error bars above and below the mean.  If the plot
style Line button is pressed, then the plot style is cycled
between Line (vertical lines for each point), Circle (small circles at
each point), and Curve (circles are connected).  Pressing the button
repeatedly cycles through: Line (i.e. vertical vars),
Circle, or Curve (i.e. continuous curve of all
samples). In the case of  mean expression profiles
used in K-means clustering, the standard deviation is used in
place of the CV value. The various clustering methods have EP
plots buttons. When they are invoked, the scrollable list of EP
plots is sorted by the clustering method ordered list of genes. This
enables you to view the data in the same order as that produced by the
cluster analysis.  If the zoom nnX button is pressed,
then all of the plots are magnified by nn-fold to make low intensity
plots more visible.  Pressing the button repeatedly cycles through:
1X, 2X, 5X, 10X and 20X.  It does not change the data itself. The
Show HP names button pops up a numbered list of all HP entries
used in the expression profile.  If you are in stand-alone mode, a
SaveAs GIF button will also be available for the EP overlay
mode (Figure 2.4.4.4.1) or individual EP plot. This saves the current
plot as a full resolution GIF file specified by the user in a popup
file browser window.
    

The Expression profile plots submenu contains:

 
      
 
    •  Display a gene's expr. profile for HP-E - popup a window
       and display the expression profile of a gene when click on a 
       spot in the Image or a point in the scatter plot.
 
    •  Display Filtered genes expr. profiles for HP-E -
      popup a scrollable montage of expression profiles of the list
      of Filtered genes. 
 
    •  --------------------
 
    • 
     Use EP overlay else EP list [CB] - display Filtered genes 
     as an overlay plot of expression profiles, else as a scrollable
     list of EP plots.
 
    




    


    
A) 
Expression profile plot - shown with various formating options


    


    
B) 
List of expression profile plots (it is scrollable if more than 10 plots)


    


Figure 2.4.4.4 Expression profile plots. A) Individual
expression profile plots may be created by clicking on any
gene. Multiple instances may also be created. Here we show some of the
presentation options for the 38 sample MGAP database. Error bars are
computed for the standard error for that sample. There are three
different plotting options: line, circle and curve. #1 is the default
line plot with error bars. #2 is the line plot without the error bars
but clicking on line 7 to find out which sample it is and what the
intensity value is. #3 is the circle plot with error bars, and #4 is
the curve plot without error bars. Window #5 shows the list of samples
corresponding to the 38 points in the EP plots.  B) List of
EPplots of the oncogenes and proto-oncogenes in the database (set by
the guesser with "onco" and "Set E.G.L." and the Edited Gene List
Filter). The list would become scrollable if there were more than 10
profiles. Setting the current gene would scroll the list to the EPplot
for the current gene.


    

    





    


    
A) 
Scrollable list of expression profile plots


    


    
B) 
Overlay expression profile plots with current gene in green


    


    
C) 
Overlay expression profile plots of EGL filter genes with current 
gene in green

    


Figure 2.4.4.4.1 Expression profile plots. 
A) Scrollable list of EP plots of Filtered named genes centered at
Carbonic anhydrase III.
B) Overlay plot of all named Filtered genes.
C) Overlay plot of all ONCO or PROTO-ONCO genes with the
draw EGL option active so the graphs are drawn for these genes.


    

    



    

    



    







 MAExplorer - Microarray Exploratory Data Analysis



 



    

    2.4.5 Cluster menu
    

The Clustering menu lets perform various types of gene and condition
clustering operations. When you invoke a clustering operation it will
popup one or more windows and may modify the pseudoarray image. Some
of the popup windows include clustergram and dendrogram analysis plots
used with the hierarchical clustering.

    

When enabled, cluster data appears as  blue circles
or squares drawn as overlays on the pseudoarray image. These 
options are discussed in the section on clustering.
    

Cluster analysis plots include finding a subset of genes or subsets of
samples based on cluster analysis of expression profile similarity
measures. These show genes belonging to particular clusters, or genes
that cluster well with specified genes.  Cluster methods include:
finding genes similar to the current selected gene within a "distance"
threshold; K-means-like clustering where you specify a seed gene and
the number of clusters; and hierarchical clustering with clustergram
and dendrogram graphics.
    



    


    

Cluster menu options


    

Figure 2.4.5 Cluster Menu options. The hierarchical clustering
option is being selected.


    

    



    Use of clustering to find patterns of similar gene expression
    

Clustering is a way of possibly finding co-expressed genes that
exhibit similar expression changes in a set of samples. Genes may show
similar co-expression, but that does not prove they are co-regulated
at the same point in a pathway - merely that measurements of those
genes in a particular set of experiments show similar
expression. However, identifying genes with similar expression for
which some information is already known about some of the genes may be
useful as a starting point to help figure out gene function and
possibly aspects of its pathways in cell function using additional
experiments and analysis.
    

There are many methods for doing clustering - each with advantages and
disadvantages. We present three methods in MAExplorer and plan on
adding a variety of more powerful methods through the MAEPlugin
facility under development.

    

These methods may find genes belonging to particular clusters or genes
that cluster well with particular genes. Gene clusters are sets of
genes whose expression profiles are found to be similar according to a
particular metric. We now define what we mean by "similar". The order
list of hybridized samples used in computing the expression profiles
are those in the HP-E list. MAExplorer has two different dissimilarity
measures for Cij: Euclidean distance LSQdistij and Pearson correlation
coefficient rij. These are computed as
follows and are tested against the cluster distance threshold (set by
the slider in the preferences sliders).  Let n= |HP-E|, the number of
samples in the expression profile. We define similarity as (1.0 -
normalized dissimilarity).

    


    

    

Hint: when working with very large data sets with many samples, it
may be useful to pre-adjust the distance and/or number of clusters
threshold sliders to an approximate range using the (Edit Menu |
Preferences | Adjust all Filter threshold scrollers). This is because
once the clustering starts, it does not (currently) let you abort the
clustering to change the threshold value.


    

    

    



       LSQdistij = Sqrt( Sum ( D'hj - D'hi) **2 ) / n 
               h in HP-E
	       i,j in Filtered genes, i not j

    



    Let,
   sumij = Sum( D'hj * D'hi ),
   mni = (1/n)Sum( D'hi ),
   mnj = (1/n)Sum( D'hj ),
   sumSqi = Sum( D'hi *  D'hi ),
   sumSqj = Sum( D'hj *  D'hj ),
  
then,
         [sumij - n*(mni * mnj)] 
   rij = --------------------------------------------------------
         [Sqrt(sumSqi - n*n*mni*mni) * Sqrt(sumSqj - n*n*mnj*mnj)]
               h in HP-E
	       i,j in Filtered genes, i not j

    

    

The Cluster plots submenu contains a number of clustering
methods.  Pressing the Escape key during a long cluster operation will
abort the operation. If you are in stand-alone mode using the
ClusterGram, a SaveAs GIF button will also be available for
saving the current plot as a full resolution GIF file specified by the
user in a popup file browser window.:



      
 

    •   Cluster genes with
      expression profiles similar to current gene [RB] - click on
      gene in image to find other genes with similar HP-E expression
      profiles whose cluster distance is less than the cluster
      distance threshold. The larger the  blue
      box, the higher the similarity.
	
 
    •   Cluster counts of
      similar Filtered genes by expression profiles [RB] - draw
       blue circles  around filtered genes
      indicating the number of other genes whose cluster similarity is
      less than the cluster distance threshold. The larger the circle,
      the more similar genes were found. Clicking on a gene switches
      to the above mode.
      

    •   K-means clustering of
      gene expression profiles [RB] - draw 
      magenta circles around the N primary-node gene clusters
      representing the gene closest to representing the center of the
      cluster. Each of the nodes is a maximum distance from all other
      nodes in the recursive definition of nodes. N is determined by
      the State Scroller "# of Clusters".  Changing N will recompute
      the clusters. It then pops up a scrollable text window with the
      clusters and indicates which genes belong to it. If you select
      the EP plot button, it will draw the expression profiles
      for the clustered genes. The Mn-Cluster-Report button
      will generate report for all genes sorted by K-means
      cluster. Summaries can be generated using the Mean EP
      plot and Mn-Cluster-Report buttons.  The SaveAs
      GeneSets button saves all of the clusters as named Gene Sets
      ("Cluster #1", "Cluster #2", etc). If you change the filter or
      current gene, you should explicitly use the Recompute
      Clusters button to regenerate the new set of clustered
      genes.  When you recompute the K-means clusters, it uses the
      current gene as the initial node.

 
    •  Hierarchical clustering of expression profiles  - this computes the hierarchical
      clustering of the expression profiles (normalized by HP-X sample
      data for each gene) of Filtered genes.  The hierarchical
      clusters are displayed in an ordered gene clustergram and
      optional dendrogram.  Sub-regions of the clustergram may be
      explored in more detail using the EP-subset plot button,
      or a report of the ordered genes can be created using the
      ClustGram Report Note: you may add (remove) genes you
      select from the Clustergram to the E.G.L. by holding the
      Control(Shift) key while clicking on the gene name.
      	     
 
    •   S.O.M. gene clusters by
      expr profiles [RB] - [Future MAEPlugin]

 
    •   Multi-Dimensional
      Scaling of genes by expr profiles [RB] - [Future MAEPlugin]

 
    •   Multi-Dimensional
      Scaling of genes by exprprofiles [RB] - [Future MAEPlugin]
         
 
    •   Clusters of (HP-E)
      samples as fct of Filtered genes [RB] - [Future]


    •  --------------------


    •   Use correlation-coefficient
      else Euclidian-distance [CB] - use the (1.0 - correlation
      coefficient) as the distance metric instead of the default
      Euclidean distance.


    •   Scale EP vector by max
      magnitude prior to clustering [CB] - scale each sample in
      the EP by the max magnitude for all sample values in the EP.


    •  Normalize by HP-X sample else
      HP max intensities [CB] - normalize data by the
      corresponding HP-X sample data for each gene or the maximum raw
      intensity for each HP in the expression profile.


    •  Use median instead of mean
    for K-means clustering [CB] - use the clustering (see (Bickel, 2001)).

 
    
 
    


The Hierarchical Cluster plots submenu contains:


      
 
    •   Display ClusterGram of gene
      expr profiles [CB] - compute the hierarchical clustering of
      the expression profiles (normalized by HP-X sample data for each
      gene) of Filtered genes. Then display the hierarchical clusters
      in an ordered gene clustergram and optional dendrogram when the
      dendrogram checkbox is selected.  Expression profile
      plots of the clustergram may be explored in more detail using
      the EP plot button that generates a scrollable list of
      all EP plots ordered by the same order as the clustergram. A
      full report of the ordered genes expression profiles may be
      created using the ClustGram Report button.
      

    •  --------------------
           

    •   
      Use avg-arithmetic-linkage [RB] - set the hierarchical
      clustering linkage method to the average arithmetic linkage of
      sub-clusters.[ Future]


    •   Use avg-centroid-linkage
      [RB] - set the hierarchical clustering linkage method to
      average centroid linkage of sub-clusters (default).


    •   Use next-min-linkage
      [RB] - set the hierarchical clustering linkage method to the
      next minimum distance sub-cluster linkage in random order.


    •   Use cluster-distance matrix
      cache [CB] - if you do not have enough memory for clustering
      large gene sets, disable the cache. It will take MUCH longer
      without the cache.  When clustering, if there is
      not enough memory available for the cache, it will warn you and
      suggest you either reduce the number of genes being clustered or
      use a computer with more memory.)


    •   Use short else float
      cluster-distance matrix cache [CB] - if there is not enough
      memory for the set of genes you wish to cluster and you still
      want to use the cache, you can use 16-bit (i.e. short) data
      instead of the 32-bit (i.e. float) data. The results will be
      less precise.


    •   Use un-weighted else
      weighted average [CB] - set the hierarchical clustering vector
      averaging to un-weighted (the default weights it by the number
      of genes in that sub-cluster). Otherwise using weighted gives
      equal (0.50) weighting to each sub-cluster.
     

    
 
    


    Handling of hierarchical clustering of large numbers of genes -
problem with slow response
    

The hierarchical clustering algorithm uses a gene-gene floating
point (i.e. 32-bit) distance matrix of order N2 (for N data
filtered genes). This means that if you are experiencing a slow
response, this may be due to several factors some of which you may not
be able to control. You might:


      

    1.  If you determine that your computer is "paging", use a computer with 
     more memory. The currently distributed stand-alone version will use 
     up to 256 Mbytes of chip memory.

    2.  If that is not possible, reduce the number of genes being clustered. 
     Even if you have enough memory, the computation is still high if N is 
     large.

    3.  Set the Use short else float cluster-distance matrix cache 
     option in the cluster plot menu. This reduces the memory requirements
     for the distance matrix by 1/2.

    


    



    

    2.4.5.1 Cluster genes with expression profiles similar to current gene
    

The Cluster genes with expression profiles similar to current
gene is used to find genes with similar HP-E expression profiles
as measured by the least square error that are less than the cluster
distance threshold. It pops up the "Cluster Distance" threshold
scroller. Then click on a gene in the microarray image. It then pops
up up a window with a list of the similar genes and their expression
profile distances to the current gene.  Each gene that passes the
cluster distance threshold test is indicated in the image with a  blue square  where the size of the square is
proportional to its similarity. It also displays a sorted list of the
genes with the cluster distance in the cluster panel that was popped
up. On each lines is a series of '*****' - the more stars the higher
the similarity to the seed gene. This is a silhouette plot that
is used to display a sorted list of similar objects and is described
to that described in (Kaufman and
Rousseeuw, 1990).

 Larger squares indicate that more genes are similar. You may
change the cluster distance threshold and it will update the display
and the list.  In addition, the 'edited gene list' is set to the
subset of genes that belong to the current cluster.



    
A) 
Cluster of similar genes current seed gene in green, similar genes with
blue boxes, similarity report on right


    



    
B) 
Similar genes viewed in scrollable list of expression profile plots 
ordered by similarity


    


Figure 2.4.5.1 Similar genes clustered to the current gene.
This method finds all genes that are similar to the current gene as
those defined by their distance between expression profiles being less
than the threshold set by the user. Each gene that passes the cluster
distance threshold test is indicated in the image with a  blue square  where the size of the square is
proportional to its similarity. This data is from the 38 samples in
the MGAP database containing duplicated spots. A) Main windows
with popup cluster similarity report and cluster distance threshold
slider. B) Scrollable list of EPplots of similar genes with the
red error bars indicating the variation for duplicated spots for each
HP sample. The Err checkbox may turn the error bar overlays on
and off.


    

    



    

    2.4.5.2 Cluster counts of similar filtered genes by expression profiles
    

The Cluster counts of similar Filtered genes by expression
profiles command analyzes the set of all Filtered genes for the
expression profile defined by the HP-E samples. It counts the number
of similar genes for each Filtered gene and draws a 
blue circle  whose size is proportional to the number of genes
similar to that gene.  After it analyses these genes it lists the
genes and their counts in the cluster panel. You may change the
cluster distance threshold and/or Filter parameters and it will update
the display and the list. If you click on a gene with a  green circle, it will switch to single gene
cluster mode (with the  blue squares).

    

For both of these commands, if you want to view the expression profile
plots, click on the EP plot button in the cluster window and it
pops up the scrollable expression profiles window.  If you click on a
gene in the image, it will select it as the new current gene and seed
gene and recompute the cluster of genes most similar to the new see
gene.

    

For both of these commands, if you want a permanent report, click on
the "Cluster Report" button in the cluster window and it will generate
a report in the current modality (i.e. scrollable spreadsheet or
tab-delimited).  You may switch between these two modes by pressing
the "Go '...'"  button in the report.

    



    

Cluster counts showing clusters with large # genes with larger circles, 
cluster count report on right


    

Figure 2.4.5.2 Display of cluster counts for all genes less than
the cluster threshold from MGAP 38 sample database. The algorithm
counts the number of similar genes for each Filtered gene and draws
a  blue circle  whose size is proportional to
the number of genes similar to that gene. That is why there are a larger
number of the larger circles.


    

    




    

    2.4.5.3 K-means clustering' gene expression profiles for filtered genes
    

The K-means cluster gene expression profiles for Filtered genes
command searches the data Filtered gene list for the genes
(i.e. primary genes) with the N most orthogonal expression
profiles. It will start this recursive computation from the gene with
minimum distance to all other genes unless you have selected a
"current gene" with the mouse.  All Filtered genes are assigned to the
nearest K-means primary node. The mean cluster vector is computed and
used as the new definition of the cluster center.  If you set the "Use
median instead of mean for K-means clustering" option in the
Clustering submenu, it will compute the center as a median instead of
a mean (Bickel, 2001).  K-means
clustering is described in (Sneath
and Sokol, 1973).  A new K-means primary gene (i.e. gene for the
cluster center) is found that is closest to this new center. Then all
of the data Filtered genes are reassigned to the new cluster
centers. The mean+-stdDev of the within-cluster distance to its center
is computed. It then pops up a text window with an ordered report of
the Filtered genes illustrated by part of a report shown below. [This
is part of a report from a 38 sample MGAP database subset of 141 genes
from the set of named genes restricted by the CV data filter.] Note
that clusters where the "Similarity" data is plotted as a  silhouette plot use
variable length strings of '****' is about the same for the entire
cluster (e.g. cluster #4) contain genes that probably belong together
in the same cluster. Clusters that do not (e.g. Cluster 6) probably
contain two smaller more robust clusters.
    


    
A) 
K-means clustering, current cluster (#4) in scatter plot, clone 
report on right


    


    
B) 
Scrollable list of expression profiles for K-means clustering ordered 
by clusters


    


    
C) 
Scrollable list of mean expression profiles for K-means clustering


    


Figure 2.4.5.3 Genes clustered using the K-means cluster
method.  A) Using the current gene as the initial cluster,
MAExplorer finds N orthogonal clusters assigning the set of filtered
genes to these clusters using the HP-E expression profiles. All
genes are iteratively assigned to these clusters. Genes belonging to
the current cluster are labeled with a green cluster number both in
the array and in the scatter plot. The slider determines the number of
clusters (set to 6 here). A 2D scatter plot shows the genes belonging
to cluster 6. The K-means cluster report on the right contains a sorted
list of the genes in each cluster and has buttons to generate EP
plots and reports as well as summary mean EP plots (shown) and mean
cluster reports. The detailed list is shown below. B) Part of
the scrollable EP plots for this data showing genes belonging to both
clusters #5 and #6. C) The mean EP plots for the 6 clusters.


    

    


     
Cluster report for 6 K-means clusters with 141 genes being clustered.
The seed gene is [1248564] Jun-B oncogene.

Clone ID  Similarity      Cluster-#  Distance-to-cluster  Gene-Name
--------  --------------  ---------  -------------------  ----------------

1248411   **************  1          Cluster [26 genes] in cluster [distNext: 1.035] wiCdist:mn+-sd=1.223+-0.453 CV=0.371  Calpactin I light chain
1381592   **********      1          0.448  Surfeit gene 4
1247956   *********       1          0.706  Protein kinase, cAMP dependent, catalytic, beta
1381836   ********        1          0.761  Prohibitin
1382325   ********        1          0.771  M.musculus mRNA for C1D protein
1248270   ********        1          0.775  Seven in absentia 1A
1247716   ********        1          0.794  Lipoprotein lipase
1248184   ********        1          0.847  Mus musculus bromodomain-containing protein BP75 mRNA, complete cds
1248564   *******         1          0.864  Jun-B oncogene
1382667   *******         1          0.888  SERINE/THREONINE PROTEIN PHOSPHATASE PP2A-BETA, CATALYTIC SUBUNIT
1382561   *******         1          0.931  Mus musculus GTP-specific succinyl-CoA synthetase beta subunit (Scs) mRNA, partial cds
1248089   ******          1          1.013  M.musculus RPS3a gene
1247780   ******          1          1.088  Proprotein convertase subtilisin/kexin type 7
1247557   ******          1          1.104  M.musculus L28 mRNA for ribosomal protein L28
1248321   *****           1          1.278  Decay accelerating factor 1
1382751   ****            1          1.311  Clusterin
1382007   ****            1          1.357  Murine mRNA with homology to yeast L29 ribosomal protein gene
1382074   ****            1          1.390  Orosomucoid 1
1381963   ****            1          1.417  M.musculus mRNA for ribosomal protein L36
1248278   **              1          1.658  HISTONE H3.3
1247630   **              1          1.675  Procollagen, type I, alpha 2
1247865   *               1          1.837  Mouse beta-D-galactosidase fusion protein mRNA, complete cds
1382236   *               1          1.85  Caspase 7
1247833                   1          1.882  Mus musculus radio-resistance/chemo-resistance/cell cycle checkpoint control protein (Rad9) mRNA, complete cds
1248535                   1          1.953  M.musculus mRNA for selenoprotein P
1247702                   1          2.157  Cytochrome C oxidase, subunit Va
1382282   **************  2          Cluster [13 genes] in cluster [distNext: 24.199] wiCdist:mn+-sd=16.184+-6.667 CV=0.412  Max interacting protein 1
1382159   **********      2          9.086  TRANSPLANTATION ANTIGEN P35B
1247854   *********       2          11.002  Prolyl 4-hydroxylase, beta polypeptide
1247970   ********        2          11.786  Mouse mRNA for osteoblast specific factor 2 (OSF-2)
1381663   ********        2          12.948  Mus musculus vacuolar adenosine triphosphatase subunit A gene, complete cds
1382100   ********        2          13.34  T-complex protein 1, related sequence 1
1248366   ********        2          13.541  Mus musculus cytochrome c oxidase subunit VIIa-L precursor (Cox7al) mRNA, nuclear gene encoding mitochondrial protein, complete cds
1247568   ********        2          13.762  Cathepsin D
1247872   *******         2          14.015  Mus musculus endothelial monocyte-activating polypeptide I mRNA, complete cds
1382333   *******         2          14.065  Stromal cell derived factor 5
1382008   *******         2          15.985  Mus musculus FK-506 binding protein homolog (SAM11) mRNA, complete cds
1247724   ****            2          21.964  Glutathione-S-transferase, alpha 3
1247846                   2          34.704  House mouse; Musculus domesticus kidney mRNA for Phosphatidic acid phosphatase, complete cds
1247945   **************  3          Cluster [22 genes] in cluster [distNext: 11.979] wiCdist:mn+-sd=7.559+-3.347 CV=0.443  Mus musculus mRNA for DEDD protein
1247797   **********      3          4.159  Mus musculus Btk locus, alpha-D-galactosidase A (Ags), ribosomal protein (L44L), and Bruton's tyrosine kinase (Btk) genes, complete cds
1382087   **********      3          4.494  Cell division cycle 42
1247539   **********      3          4.511  EST
1248212   **********      3          5.009  Murine mRNA for integrin beta subunit
1248470   **********      3          5.044  EST
1247521   *********       3          5.299  Mus musculus mRNA for peroxisomal integral membrane protein PMP34
1381808   *********       3          5.924  Mus musculus UDP-GalNAc:polypeptide N-acetylgalactosaminyltransferase-T3 mRNA, complete cds
1381970   *********       3          6.285  Mus musculus thioredoxin mRNA, nuclear gene encoding mitochondrial protein, complete cds
1382168   *********       3          6.343  N-terminal Asn amidase
1382704   *********       3          6.36  Mus musculus N-myristoyltransferase 1 mRNA, complete cds
1248548   *********       3          6.378  Mus musculus WDR protein mRNA, complete cds
1247564   ********        3          6.652  Erythrocyte protein band 7.2
1248588   ********        3          6.67  M.musculus BAP31 mRNA
1247541   ********        3          6.690  Apolipoprotein D
1248462   ********        3          7.322  Sterol O-acyltransferase 1
1248462   ********        3          7.42  Sterol O-acyltransferase 1
1248521   ******          3          9.121  Mus domesticus nuclear binding factor NF2d9 mRNA, complete cds
1382212   ******          3          10.137  Thyroid autoantigen 70 kDa
1382270   *****           3          10.529  Voltage-dependent anion channel 2
1248152   *****           3          10.541  M. musculus mRNA for MAP kinase-activated protein kinase 2
1247678                   3          19.431  Casein alpha
1247543   **************  4          Cluster [44 genes] in cluster [distNext: 1.035] wiCdist:mn+-sd=0.439+-0.266 CV=0.606  RAS-related C3 botulinum substrate 1
1381923   ************    4          0.158  Prolyl 4-hydroxylase, beta polypeptide
1382052   ************    4          0.209  Trans-acting transcription factor 1
1247882   ***********     4          0.237  Mus musculus AMP activated protein kinase mRNA, complete cds
1248099   ***********     4          0.246  Mus musculus mitogen-responsive 96 kDa phosphoprotein p96 mRNA, alternatively spliced p67 mRNA, and alternatively spliced p93 mRNA, complete cds
1248351   ***********     4          0.251  Abl-interactor 1
1247540   ***********     4          0.255  Mus musculus mRNA for ZIP-kinase, complete cds
1248316   ***********     4          0.26  Mus musculus proteasome alpha7/C8 subunit mRNA, complete cds
1382671   ***********     4          0.264  Mouse MA-3 (apoptosis-related gene) mRNA, complete cds
1382014   ***********     4          0.277  Transcription elongation factor B (SIII), polypeptide 1 (15 kDa),-like
1247885   ***********     4          0.289  Mus musculus mRNA for ryudocan core protein, complete cds
1248294   ***********     4          0.292  Mus musculus thioredoxin-related protein mRNA, complete cds
1382066   ***********     4          0.306  Inhibitor of DNA binding 2
1248597   ***********     4          0.307  Lipocortin 1
1248591   ***********     4          0.324  Interferon beta, fibroblast
1248445   **********      4          0.333  Mus musculus beta prime coatomer protein mRNA, partial cds
1247775   **********      4          0.34  House mouse; Musculus domesticus male brain mRNA for ARF1, complete cds
1382750   **********      4          0.340  Thymoma viral proto-oncogene
1247905   **********      4          0.341  Monokine induced by gamma interferon
1381668   **********      4          0.351  Mus musculus mitogen-activated protein kinase-activated protein kinase mRNA, complete cds
1381811   **********      4          0.356  Protein tyrosine phosphatase, receptor type, D
1382031   **********      4          0.358  Protease (prosome, macropain) 28 subunit, beta
1248345   **********      4          0.363  Mus musculus alpha-methylacyl-CoA racemase mRNA, complete cds
1382555   **********      4          0.364  Lysosomal membrane glycoprotein 1
1247820   **********      4          0.367  Tight junction protein 1
1247598   **********      4          0.374  Retinoblastoma 1
1247595   **********      4          0.378  PROBABLE CALCIUM-BINDING PROTEIN PMP41
1381928   **********      4          0.379  Mus musculus MRJ (Mrj) mRNA, complete cds
1248196   **********      4          0.399  Max protein
1381691   **********      4          0.423  SRY-box containing gene 17
1248225   **********      4          0.434  Mus musculus heat shock transcription factor 1 (Hsf1) gene, partial cds
1248084   **********      4          0.442  Mus musculus Supl15h gene
1247941   *********       4          0.453  Fibroblast growth factor inducible 14
1381623   *********       4          0.468  Stearoyl-coenzyme A desaturase 1
1248202   *********       4          0.473  Mouse mRNA for PAP-1, complete cds
1382115   *********       4          0.512  GLUTATHIONE S-TRANSFERASE GT8.7
1382044   *********       4          0.515  Cartilage derived retinoic acid sensitive protein
1381636   ********        4          0.567  Lymphotoxin B
1381920   ********        4          0.569  Mus musculus mRNA for NEFA protein, complete cds
1247757   ********        4          0.596  Granzyme B
1382094   ********        4          0.609  High mobility group protein 1
1247545   ********        4          0.638  Carbon catabolite repression 4 homolog (S. cerevisiae)
1247607   ***             4          1.188  POLYADENYLATE-BINDING PROTEIN
1247727                   4          1.667  Malate dehydrogenase, mitochondrial
1248244   **************  5          Cluster [19 genes] in cluster [distNext: 3.473] wiCdist:mn+-sd=4.273+-2.059 CV=0.482  CD80 antigen
1248534   **********      5          1.648  Carbonyl reductase
1247764   **********      5          1.776  H-2 CLASS II HISTOCOMPATIBILITY ANTIGEN, GAMMA CHAIN
1381933   *********       5          2.345  Mouse rpS17 mRNA for ribosomal protein S17, complete cds
1381616   *********       5          2.42  Mus musculus oral tumor suppressor homolog (Doc-1) mRNA, partial cds
1248232   *********       5          2.486  Mus musculus putative glycogen storage disease type 1b protein mRNA, complete cds
1382644   ********        5          2.717  Cyclin G
1248125   ********        5          2.791  Histocompatibility 2, class II, locus Mb2
1247799   ********        5          2.869  Mus musculus signal recognition particle receptor beta subunit mRNA, complete cds
1247708   ********        5          3.024  Ephrin A1
1247932   ******          5          4.235  Mus musculus (clone: pMAT1) mRNA, complete cds
1382515   *****           5          4.668  ATPase, Na+/K+ beta 3 polypeptide
1248586   *****           5          4.838  Mus musculus viral envelope like protein (G7e) gene, complete cds
1248198   ***             5          5.874  Mus musculus D9 splice variant 2 mRNA, complete cds
1381623   **              5          6.224  Stearoyl-coenzyme A desaturase 1
1382086   *               5          6.885  Mus musculus (strain C57Bl/6) mRNA sequence
1247887   *               5          7.014  Mouse chromosome 6 BAC-284H12 (Research Genetics mouse BAC library) complete sequence
1247886                   5          7.810  Cut (Drosophila)-like 1
1248303                   5          8.094  Lipopolysaccharide response
1247621   **************  6          Cluster [17 genes] in cluster [distNext: 19.157] wiCdist:mn+-sd=12.410+-3.024 CV=0.244  Mus musculus Lsc (lsc) oncogene mRNA, complete cds
1248050   *******         6          7.407  Mus musculus C57BL/6J ribosomal protein S28 mRNA, complete cds
1247698   *******         6          7.571  Adipocyte protein aP2
1248240   *****           6          9.198  Mus musculus mRNA, complete cds
1247862   ****            6          9.844  Mus musculus Nmi mRNA, complete cds
1382162   ****            6          10.330  CAMP responsive element modulator
1248398   ***             6          11.007  Mouse mRNA for ribosomal protein S12
1248281   ***             6          11.143  M.musculus mRNA for histone H3.3A
1247852   ***             6          11.576  Twist gene homolog, (Drosophila)
1381991   **              6          12.809  Prolyl 4-hydroxylase, beta polypeptide
1382753   **              6          13.019  Mus musculus cleavage and polyadenylation specificity factor (MCPSF) mRNA, complete cds
1248368   *               6          13.639  Mus musculus ribosomal protein S26 (RPS26) mRNA, complete cds
1247639   *               6          13.692  SRY-box containing gene 4
1248435                   6          14.262  Thymus cell antigen 1, theta
1247961                   6          14.75  ATP SYNTHASE ALPHA CHAIN, MITOCHONDRIAL PRECURSOR
1248344                   6          15.217  Gut enriched Kruppel-like factor
1382234                   6          16.351  CD8 antigen, beta chain


    

We call the genes closest to the "center" of the K clusters primary
genes and they are reported with additional information.  The "Cluster
[# genes]" entries in the distance-to-cluster fields indicates that
these genes are the center of the clusters (i.e. primary genes). The
distNext is the distance from this cluster center to the next nearest
K-means cluster center. The number of clusters N (6 in this example)
is set in the popup state scroller. If you change the value of N, it
will recompute the clusters and the primary genes.
    




It draws  magenta circles around the
primary genes in the microarray and the cluster number to the right of
the circle. The size of a circle corresponds to the number of genes
clustered with that circle. If you click on a gene belonging to any
cluster, it defines that cluster as the "current cluster".  It will
change the labels of the subset of genes that belong to the current
gene from red (white) circle to a green (yellow) cluster number of the
current cluster in the intensity (ratio) pseudoarray image. In addition,
the 'edited gene list' is set to the subset of genes that belong to
the current cluster. If you are also displaying a scatter plot, genes
in the current cluster have their red '+' characters changed to the
cluster number.
    

You can click on that gene in the array image to determine its
identity.  You may also popup an ordered (same as the above report)
plot of the clusters expression profiles by clicking on the EP
plot button. You may plot the mean expression profiles of the N
clusters using the Mean EP plot button. You may generate a
report of all of the clustered genes or of the mean clusters using the
Cluster-Report or Mn-Cluster-Report buttons
respectively. If you change the Filter conditions, you may recompute
the clusters using the Recompute Clusters button.  Closing the
text window will remove the  magenta
circles. If you selected the current cluster, the genes that
belong to it will still be available in the 'edited gene list' for
making reports, saving as a gene subset or for additional gene
filtering. If you press the SaveAs GeneSets button, then K gene
sets are created with the names "Cluster#1", "Cluster#2", ...,
"Cluster#K". You can then save or rename the clusters you want and
delete the rest. If you press the ClusterGram button, it
displays the gene sets in a cluster gram order the same way as 
the cluster report.
    



    

    2.4.5.4 Hierarchical clustering of expression profiles
    

The Hierarchical clustering of expression profiles computes the
hierarchical clustering of the expression profiles of data Filtered
genes and displays a clustergram and optional dendrogram.
Hierarchical clustering is described in ( Sneath and Sokol, 1973). The gene
data is normalized either by the corresponding HP-X sample data for
each gene or the maximum raw intensities for each HP sample in the
expression profile set by the Normalize by HP-X else HP's max
intensities menu toggle.  There are three types of clustering
linkages: average-arithmetic-linkage,
average-centroid-linkage, and next minimum
linkage. These may be modified using the weighted average
that gives equi-weighting to the child clusters in computing the mean
of a new cluster, and un-weighted-average that weights them by
the number of non-terminal clusters. The average-linkage clustering is
very compute intensive and takes a while.  The next-minimum-linkage is
much faster and may result in adequate clustering for some
situations. 
    

Clustering is represented by a binary tree and is visualized as an
ordered gene clustergram and optional dendrogram sub-plot. This is
similar to the methods of (DeRisi,
1996), (Eisen, 1998), and
(White, 1999). Currently,
MAExplorer does 1-way clustering - not the 2-way clustering of (Weinstein, 1998) and (Eisen, 1998).  Each row of the
clustergram represents a gene and each column represents a HP in the
HP-E list of samples. Each box in a row represents the normalized
expression of that gene for the HP represented in that column. The
color of the box is one of 9 colors representing the normalized
expression ranges and assigned according to the following table:
    


    

    

Table 2.4.5.4. ClusterGram pseudocolor assignments. The
colors are assigned to "box" entries in the clustergram corresponding
to genes. The color represents data as either the X/Y ratio or X-Y
Zdiff relative to the normalizing HP.


    


    


    

     
.
.
.
.
.
.
.
.
.

    



     
bright green
.
.
 dark green
Black
dark red
.
.
bright red

    


     
<1/8X
1/6X
1/4X
1/2X
1X
2X
4X
6X
>8X 

    

    
    

    


    

The current gene may be set by clicking on a row that is then
highlighted in green. If you click on a colored box, it will also
report the HP name for that column and its normalized expression value
(highlighting that box with a white circle). If the Web genomic
databases are enabled (through the View menu, then it will also popup
a Web page for that gene). If you set the current gene in any of the
array, scatter plot, gene guesser, etc. displays, it will set it for
and position the clustergram at that gene.  If the Dendrogram
checkbox is enabled, then a dendrogram is drawn to the left of the
clustergram boxes. Clicking on a region in the dendrogram sets a
distance threshold (displayed at the top) and displays all parts of
the dendrogram tree in red that have a cluster distance less than what
you defined. If the zoom nnX button is pressed, then the
of dendrogram drawing is magnified by nnnn-fold to make highly similar
clusters more visible.  Pressing the button repeatedly cycles through:
1X, 2X, 5X, 10X, 20X.  Sub-regions of the clustergram may be explored
in more detail using the EP plot button that pops up a
scrollable window of the ordered gene list.  You may generate
multiple EP-subset plots so as to compare different parts of the
clustergram. A report of all of the ordered genes may be created
using the ClustGram Report button. The Show HP names
button pops up a numbered list of all samples used in the expression
profiles and clustergram.  This report has all of the normalized
expression profiles on the right side of the report.
    


    

    

    
A) 
Hierarchical clustering showing ClusterGram with current gene in green


    


    
B) 
Hierarchical clustering showing ClusterGram and Dendrogram with 
thresholded dendrogram in red


    


    
C) 
Selecting genes from Hierarchical clustering plots for the E.G.L. gene list


    



Figure 2.4.5.4 Hierarchical clustering clustergram of genes
filtered by ratio histogram bins for 19 samples from the MGAP data
set. The hybridized samples are drawn as colored boxes in the 19
columns.  Rows of boxes correspond to gene expression profiles.  In
A), the set of all genes and ESTs was filtered by the CV filter
set to 0.387 and the normalization was the Zscore.  The gene "Mus
musculus D9 spice variant 2 mRNA, complete cds" was selected as the
current gene in the clustergram. Data for this gene and the selected
HP column is indicated at the top of the clustergram. The list of the
19 samples is shown on the left.  B) Details of clustergram and
dendrogram are shown where the user had selected a cluster distance
threshold at "Mouse mRNA for mitochondrial cytochrome c oxidase
subunit Vb" in the dendrogram part of the plot (zoomed by 2X). This
selection draws all parts of the dendrogram tree that are less than
this distance are drawn in red.  C) shows the manual selection
of genes from the ClusterGram or Dendrogram by clicking on the genes
names you wish to capture in the Edited Gene List (EGL) while the
Control key is pressed. The zoomed subregion shows three genes in the
same cluster that were selected (magenta stars in the right edge of
the ClusterGram).


    


    

    



    







 MAExplorer - Microarray Exploratory Data Analysis



 



    

    2.4.6 Report menu
    

Various reports summarizing gene or sample data may be generated and
appear in popup tables.  These include:

    
      
    
    
    •  Data on all of the hybridized samples in the database
    including links to related Web databases
    
    
    •  Data on additional quantification and descriptive information
      on the hybridized array samples in the database

    
    •  Hybridized sample calibration, Samples vs. Samples correlation
         coefficient of Filtered genes, and mean & variance tables

    
    •  Tables of all named genes, the calibration DNA genes, etc.
    
    •  A table of genes in the 'Edited Gene List' 
    
    •  A table of genes in the current Gene Class
    
    •  A table of genes passing the "Filter"
    
    •  The N genes with the highest or lowest ratios (HP-X/HP-Y 
         or F1/F2) of the Filtered genes (N defaults to 100, but may
         be changed as a Preference)

    
    •  Tables of additional data for the Filtered genes including
         expression profile ratios, HP-X vs. HP-Y 'set' statistics if
         the data is enabled for using HP-X and HP-Y 'set' data.  If
         you are doing a t-test of Kolmogorov-Smirnov test, then it
         reports the p-value and test statistics.

    
    •  Table of statistics on F-test on the current Ordered
         Condition List of samples/condition, including p-value and
         F-test statistics.

    
    •  Tables of correlation coefficient statistics of data Filtered
         genes comparing all samples in the HP-E list against each other.

    
    •  Tables are presented as either: a) an active
         "clickable" spreadsheet that may contain links to Web sites
         and pops up a Web browser window to that site (e.g.
         histology links or model system in the array report), or
         display additional gene features and numeric
         comparisons. b) tab-delimited text that may be "cut"
         and then "pasted" into an Excel type spreadsheet for further
         analysis.
   
    
    •  Additional expression profile data (from the HP-E samples)
         and/or statistical data from the HP-X vs. HP-Y 'sets' may 
         be included in gene report tables.


    
    •  Clicking on a column name in the dynamic report will 
         sort the report by data in the report in ascending and
         descending order.

    
    



    

    

    


    

Samples Reports menu


    


Figure 2.4.6 Reports menu. You may create either dynamic or 
tab-delimited text reports of either Samples or of subsets of genes.


    

    

    


These may be presented as interactive dynamic tables as well as
scrollable text windows capable of being exported to Excel. If Web DB
access is enabled, clicking on an entry will bring up a Web browser
with access to GenBank data. If the report contains Clone ID as one
of the fields, you can click on it to have it define that gene as the
current gene and highlight it in the microarray image or scatter plot
(if it is being used). The reports are divided into two types - those
dealing with lists of arrays (i.e. the sample experimental condition)
and those dealing with lists of genes.
    
 
The Report menu includes:

 
      
 
    •  
      Array reports - 
       show tables of sample array information.
 
    •  
      Gene reports - 
       show tables of sets of genes information.
 
    •  --------------------
 
    •  
      Table format - generates the 
      table as 'Spreadsheet', 'Tab delimited', or 'Name=Value list'.
 
    •  
      Table font size - changes
       the report font size from default 10 point.

 
    




    

    

    2.4.6.1 Array report menu - hybridized samples global data
    

You may generate reports of sample array information. The first two
menu selections contain descriptive information about specific
hybridized microarrays samples. The "Extra Samples info" contains
quantitative and extra descriptive information (if available for your
database).
    

The "Samples vs Samples correlation coefficients" computes the correlation
coefficients in an upper diagonal matrix for the current set of
Filtered genes showing HP samples similarity. Then entries are of the
following form where HP:1 and HP:2 correspond to samples listed in the
field names of the table and the data is the intensity values using
the current normalization method.


      rSq=0.748, n=1656, HP:1(mn+-sd)=(28991+-19564), HP:2(mn+-sd)=(5044+-9766)

    
 


    
The "Calibration DNA summary" table contains the computed means,
std-dev, and computed normalization scale factor for all active
hybridized samples. The scale factors are used if the 'Calibration
DNA' normalization is used.
    

You must set the Web access checkbox if you want to click on a blue
hyperlink in the resulting report to access an associated Web
database.
 


 
      
 
    •  Samples info - show table of all samples accessible in
       the database.
 
    • Samples Web links - show table of Web links for all 
       sample samples accessible in the database.
 
    • MAE Projects DB - show table of project databases if the
       project startup database exists (stand-alone mode).
 
    •  Extra info on Samples - show table of 
      additional information on all of the samples in the database.
 
    •  Sample vs Sample correlation coefficients - show table of 
      correlation coefficients for all HP-E selected samples for genes
      meeting the data Filter criteria.
 
    •  'Calibration DNA' summary - show a table of means, std-dev, 
      scale factor for all HP-E selected samples.
 
    •  HP mean & variance summary - show table of means, 
      std-dev, min, max, etc. of raw intensity data for all HP-E selected 
      samples. 
 
    

    




    

    


    
A) 
Scrollable dynamic samples report


    

    
B) 
Scrollable dynamic samples report with clickable Web links in blue


    

    
C) 
Scrollable dynamic report of samples vs samples correlation using current data Filtered genes


    

Figure 2.4.6.1 Hybridized samples dynamic Report windows. A)
Samples Info report. B) Sample Web links. Clicking on a blue
hypertext link brings up the corresponding genomic Web database entry
in a separate Web browser window if the Web access is enabled. The
tab-delimited version of the same reports (not shown) may be cut and
then pasted into other programs such as an Excel spreadsheet.
C) HP vs HP correlation table on genes passing the data
Filter for all samples in the HP-=E list.


    

    

    



    

    2.4.6.2 Gene reports menu
    

You may generate gene reports with various additional options.  You
must set the Web access checkbox if you want to click on a blue
hyperlink in the resulting report to access an associated Web
database. In addition, specialized gene reports may be generated from
some of the cluster plot command windows. These include lists of
genes sorted by cluster (K-means cluster #), by hierarchical cluster order,
by similarity to a gene, etc. The mean cluster expression values may
be reported for K-means clustering.


 
      
 
    •  All named genes - show table of all genes where the gene
      is named.
 
    •  Genes in 'Edited Gene List' - show table of genes in the
      'Edited Gene List'.
 
    •  Genes in 'Normalization Gene List' - show table of genes in the
      'Normalization Gene List'.
 
    •  Genes in GeneClass - show table of genes in the current
      gene class.
 
    •  Filtered gene reports - show genes 
      that meet the data Filter criteria.
 
    

    


    

    2.4.6.2.1 Filtered gene reports menu
    

You may generate gene reports of Filtered genes with various
additional presentation options. In the highest/lowest N genes, N
defaults to 100 and is set by (Report | Table format | Set max # genes
in highest/lowest report) command.


 
      
 
    •  Genes passing the Filter - all genes passing the Filter
 
    •  Highest HP-XY ratios or Zdiff - top N Filtered genes
 
    •  Lowest HP-XY ratios or Zdiff - lowest N Filtered genes
 
 
    •  Highest F1/F2 ratios or Zdiffs - top N Filtered genes (if
     duplicates). If ratio data, then highest Cy3/Cy5

 
    •  Lowest F1/F2 ratios or Zdiffs - lowest N Filtered genes
     (if duplicates). If ratio data, then lowest Cy3/Cy5.

 
    •  Expression profiles Filtered genes - show numeric
      expression ratios for HP-E data scaled to the first HP in HP-E.

 
    • HP-XY 'set' statistics of Filtered genes - show
     (mean,stdDev,CV,n) data for both the HP-X and HP-Y
     sets. If the t-Test is active, also show (p-value, df,
     t-statistic, pF-sameValue). If the Kolmogorov-Smirnov test is
     active, then it reports the (p-value, df, D-statistic).

 
    • Ordered Condition list statistics of Filtered genes - for
     the current OCL if the F-test is active, show the (f-statistic,
     dfWithin, dfBetween, mnSqWithin, mnSqBetween)
     (mean,stdDev,CV,n) data for each condition.

 
    

    

If Cy3/Cy5 ratio data is being analyzed, then the Highest (Lowest)
F1/F2 entries become


 
      
 
    •  Highest Cy3Cy5 ratios or Zdiffs - top N Filtered genes (if duplicates)
 
    •  Lowest Cy3Cy5 ratios or Zdiffs - lowest N Filtered genes (if duplicates)
 
    

    



    

    


    
A) 
Scrollable dynamic report of 50 nameed genes sorted by HP-X/HP-Y ratio


    

    
B) 
Same report as tab-delimited text area that may be cut and pasted to 
Excel or saved as text file for export


    

Figure 2.4.6.2 Gene Report windows of 50 named genes with highest
HP-X/HP-Y 'set' ratios. A) Dynamic gene report of 50
genes with highest HP-X/HP-Y 'set' ratios. A similar report may be
generated for the lowest ratios or for single HP-X/HP-Y samples. This
type of report may be generated for the highest or lowest Zdiff values
when the Zscore normalizations are used.  Clicking on a blue hypertext
link brings up the corresponding genomic Web database entry in a
separate Web browser window if the Web access is enabled. It also sets
the current gene to the gene for that row.  B) The
tab-delimited version of the same report may be cut and then pasted
into other programs such as an Excel spreadsheet.


    

    

    


    

    2.4.6.3 Table format menu
    

The report is presented as a table. However, it may be visualized
several different ways. The scrollable spreadsheet includes the
ability to click on blue hypertext items and have a Web browser pop up
for that item on a Web database (e.g. GenBank, dbEST, UniGene,
LocusLink, mAdb Genes, GeneCard, etc).  The tab-delimited option
enables you to cut the table and paste it into a separate spreadsheet
program such as Excel. You may also extend the data in the table to by
'Adding' expression profile ratios and statistics from the HP-X and
HP-Y 'set' comparisons.


 
      
 
    •  
      Spreadsheet [RB] - some cells (indicated by blue) are 
      connected Web databases that are accessed by clicking on them (default).
 
    •  
      Tab delimited - suitable for export [RB] - as in Excel, etc.
 
    •  --------------------
 
    •  
      Set max # genes in highest/lowest report or filter [CB] - 
       sets the number of genes N to report or use in data Filter.
 
    •  
      Add EP data to Gene-Reports [CB] - for each of the 
      samples in HP-E.
 
    •  
      Use EP data in Gene-Reports [CB] - use raw EP data (under 
      the current normalization) else use data normalized to 
      0.0 to 1.0 by maximum value for all genes being displayed.
 
    •  
      Add HP-X/-Y 'set' statistics data to Gene-Reports [CB] - 
      that includes (mean,stdDev,CV,n) for both the HP-X and 
      HP-Y sets.
 
    

    



    

    2.4.6.4 Table font size menu
    

For wider tables, you can see more information if you use a smaller
font to display the table. The font sizes available are:


 
      
 
    •  
      12pt [RB]
 
    •  
      10pt [RB] - (the default size)
 
    •  
      8pt [RB] 
 
    

    


    

    



    







 MAExplorer - Microarray Exploratory Data Analysis



 
     

    

    2.5 View menu
    

The View menu options are used to modify the view of genes
visible in the pseudoarray image. Genes may be displayed with
additional properties or capabilities including access to Web-based
genomic database entries for specific genes. Note that depending on
your particular database, if some genomic identifiers are not
available then the corresponding "Enable display current gene in
genomic DB Web browser" will not appear in the menu. 



    


    

Example of View Menu


    


Figure 2.5 View Menu options.  These are divided into various 
options for modifying the presentation as well as recording activity
such as the messages or history popup scrollable log windows.


    

    

    

    


 
    



    


    
A) 
Example of UniGene popup Web browser page for current gene


    


    
B) 
Example of NCI/CIT mAdb popup Web browser page for current gene


    


    
C) 
Example of LocusLink popup Web browser page for current gene


    


Figure 2.5. Popup genomic browser database page. A) The
UniGene Web page pops up in a new Web browser window when the user
clicks on a gene in the array image, 2D scatter plot or Report and the
view is set to "Display current gene in Unigene Web Browser" toggle
was enabled in the View menu. The current gene was "Jun-B oncogene".
Alternatively, the B) mAdb Gene DB may be selected - as well as
GenBank or dbEST genomic databases.  C) Alternatively, data from
the NCBI LocusLink database may be accessed if either the GenBank ID or
LocusID is available.


    

    

    

    



    2.5.1 Logging MAExplorer messages 
    

MAExplorer shows various data measurements as well as many other types
of information in the three text lines in the status area of the main
window. The Show log of messages pops up a scrollable log of
all messages to the three line status area. This is useful for
recording measurements and other activity. The messages may be saved
in log file (typically maeMessages.log). Figure 2.5.2 shows an example
of the messages popup log window. Clicking on genes in the pseudoarray
image or in plots will log the gene data (    see Section 3.3) given the
current normalization, Samples use (single or multiple), and
pseudoarray display mode.  The current values of all of the State
Threshold scrollers are saved in the message log when the (Edit menu |
Preferences | Adjust all Filter threshold scrollers) State
Thresholds popup window is closed. This is useful for capturing the
current settings at any time.




    2.5.2 Logging command history
    

During a datamining session, the user will typically execute many
commands from the menu as well as clicking on genes in the pseudoarray
image or in plots. It is useful to recording the steps you took during
this analysis.  The Show log of command history pops up a
scrollable history of all commands issued to MAExplorer. The commands
are automatically numbered.  The history may be saved in log file
(typically maeHistory.log). Figure 2.5.2 shows an example
of the command history popup log window.


    


    

Examples of messages and command history popup log windows


    


Figure 2.5.2 Examples of messages and command history popup log 
windows. Measurements and other activity are shown in more detail
in the messages window whereas the command history indicates commands
(numbered in the order they are executed) in the command history window.
Data from either of these windows may be saved in text log files.


    

    


    







 MAExplorer - Microarray Exploratory Data Analysis



 
 

    

    2.6 Plugins menu
    

MAExplorer may be extended by users to use new analysis methods using
Java plugins. We call these new methods MAEPlugins which are small
Java programs written by users that may be dynamically loaded into
MAExplorer and then applied to their data. These plugins will include
plugins written by LECB, those written by academic or commercial
groups. See the MAEplugins for
details. If you have a Java compiled plugin in the form of either a
Java .class or .jar file, you may load it at run time using the "Load
plugin" command in the Plugins menu. If specified in the MAEPlugin, it
will be added to the appropriate menu in the MAExplorer menu tree at
the end of the specified submenu (see Appendix C.  Table C.5.7). If
this submenu "stub" is not specified, it will place in the list of
plugins in the Plugins menu (e.g. plugin #1, ..., plugin
#n).



    


    
MAEPlugin paradigm


    


Figure 2.6 MAEPlugins paradigm. If you have a MAEPlugin .jar
file, then it may be specified using the "Load plugin" command. When
you invoke the command from the menus (or other methods), it accesses
data from the current MAExplorer database it may need from the Open
Java API.


    

    

    


    

The Plugins menu includes:
    

 
      
 
    •  Load plugin - popup a browser to load a MAEPlugin jar file
 
    •  Unload plugin(s) - popup a selector to pick MAEPlugins to unload
 
    •  -------------------------------------
 
    •  RLO methods - list of
    executable R methods (R LayOuts). These may be added by the user
    using the RtestPlugin. An RLO
    analysis allows you to export data from MAExplorer, execute it
    with the R program, and import the R results back into
    MAExplorer. [This is under development and is alpha-level.]
 
    • Save RLO reports in time-stamped Report/ folder [CB] - puts files
    generated by R from successive executions of the same RLO into separate
    sub-folders in the Report/ folder with names 
    "RLOname-YYMMDD-HHMMSS/" to peep the data separate.
 
    •  -------------------------------------
 
    •  
 
    •  plugin #1 - 1st MAEPlugin added
 
    •  plugin #2 - 2nd MAEPlugin added
 
    •   . . .
 
    •  plugin #n - n'th MAEPlugin added

 
    


    

    RLO methods menu
     

This contains a list of executable R
analyses methods (called R LayOuts or RLOs created with the
RtestPlugin) for evaluating MAExplorer data with R analysis scripts.
It is only available if you have installed the R language program (www.r-project.org) on your
computer. An RLO analysis allows you to automatically export data from
MAExplorer, execute it with the associated R program, and import the R
results back into MAExplorer. [This is under development and is
alpha-level.] A recent poster on 

Extending MAExplorer with R is available as a PDF file.
    

The Save RLO reports in time-stamped Report/ folder [CB]
options puts files generated by R from successive executions of the
same RLO into separate sub-folders in the Report/ folder with names
"RLOname-YYMMDD-HHMMSS/" to peep the data separate. This is
useful when you want to compare results from the same RLO method but
with different MAExplorer preprocessing.
    

You may download the latest versions of all plugins using the (File |
 Update Plugins from
maexplorer.sourceforge.net) menu command. Similarly, you can
update your versions of the RLO methods using (File |

Update RLO methods from maexplorer.sourceforge.net
    



    

    2.6.1 Example of using a Plugin
    

This shows a short demonstration of what is involved in using a
MAEPlugin. The user first load the plugin from the disk. Generally the
plugins .jar or .class files are stored in the Plugins/ directory
where you have installed MAExplorer. Then they load a particular
plugin which installs it in the Plugins pull-down menu. Then they
revisit that menu to invoke the particular plugin. You may load any
number of plugins (until you run out of computer memory if that should
occur).
    



    

Loading a Plugin from the Plugins 'Load plugin' menu


    


Figure 2.6.1 Loading a MAEPlugin from your file system using
the Load Plugins command in the Plugins pull down menu. If you
have a plugin .jar or .class file, it may be specified using the "Load
plugin" command. This pops up a file browser to let you specify the
plugin file.



    


    

Selecting the loaded plugin command from the Plugin menu


    


Figure 2.6.2 Executing the new command previously loaded in the
Plugin menu. Selecting the new "Show List Active Filters" command
that now appears in the Plugins menu invokes the plugin. This pops up
a report shown in the next figure.



    


    

Popup window from executing the MAEPlugin


    


Figure 2.6.3 Popup window from executing the MAEPlugin. 
This plugin gives a full report on the data Filter status in a new
pop up window.


    



    

    



    


    

Selecting a previously loaded plugin from the Plugins menu


    


Figure 2.6.2 Plugins menu - executing a previously loaded
plugin. Plugins that do not go into particular MAExplorer
submenus go into the Plugins menu. Selecting the command will
invoke that MAEPlugin.


    

    

    


    

    


    







 MAExplorer - Microarray Exploratory Data Analysis



 
 

    

    2.7 Help menu
    

Various on-line help and documents are available if you are connected
to the Internet. These will appear in a separate pop-up Web browser
window so you may view them while working with MAExplorer.  This
includes on-line documentation (including this reference manual),
tutorials, and other information. This may be links to other Web pages
describing key areas of specific databases. For example, for the MGAP
database, the point back to key areas of MGAP including the MGAP Animal Models, Histology
atlas, etc.  You can then use the browser's "Save as" and "Print"
options to save the data to a file or print it.
    
 
The Help menu includes:

 
      
 
    •  Home page - MAExplorer home page on SourceForge.net
 
    •  Introduction - to MAExplorer
 
    •  Overview - overview of MAExplorer capabilities
 
    •  Short tutorial - simple tutorial
       of things to try in MAExplorer (Appendix A)
 
    •  Advanced tutorial - advanced tutorial
       of things to try in MAExplorer (Appendix B)
 
    •  Menu summary - short summary of the MAExplorer menus.
 
    •  Reference manual - this Reference Manual document
 
    •  MAEPlugins - plugin home page for MAExplorer
 
    •  Intro to data mining - for MAExplorer, (Chapter 3)
 
    •  Glossary - glossary
       of terms used in MAExplorer Reference Manual
 
    •  Index - Index
      of terms used in MAExplorer Reference Manual
 
    •  -------------------------------------
 
    •  
      
      Database-specific help menu entries - 
      
      entries defined for a particular database (see below)
      
      
 
    •  -------------------------------------
 
    •  About - show MAExplorer authors, version and revision 
      date
 
    

     

    


    2.7.1 Adding custom help links to your database to the Help menu
    

These Database-specific help menu
entries list of entries are keyed to the database you are
using and may be 
customized by the database maintainer in the configuration file
(Section C.5.6) to links relating to the particular database. For
example, database specific help for the MGAP database is:
    

 
      
 
    •  List of hybridized samples - available from MGAP.
 
    •  MGAP animal models - animal models used in MGAP.
 
    •  MGAP home page - Mammary Genome Anatomy Program
 
    

    

    



    







 MAExplorer - Microarray Exploratory Data Analysis



 


    

    3. Exploratory Data Analysis - Introduction to Data Mining
    

Data mining is the uncovering of relevant patterns of interest in data
from a particular problem domain (Tukey, 1977).  Typically this
involves using various statistical techniques to identify the patterns
including cluster analysis. See 
StatSoft Inc's, 2002 on-line  statistics textbook for definitions of
clustering and other statistical terms.  Researchers across a wide
range of fields such as (Tufte,
1997) and (Cleveland,
1985) have suggested that a major aspect of this problem is
finding the correct means of graphical presentation to allow humans to
be a part of the pattern recognition process.  Tufte argues that the
proper display of quantitative data in the context of the problem
domain can aid in the understanding of complex sets of data.  This
carries over to the analysis of microarrays with data mining involves
having statistical, genomic knowledge database, and graphical
components for success. (Jagota,
2001) discusses a number of methods and applications for
microarray data analysis and visualization. Other useful resources are
the sets of papers in (
"Chipping Forecast", Nature Genetics supplement, Jan,
1999), and (
"Chipping Forecast II, Nature Genetics supplement, Dec,
2002).

    

This section briefly addresses some of the issues you need to
consider. However, a full discussion of the issues involved is beyond
the scope of this manual. These issues are covered in other more
focused statistical methods literature and you might also address them
in consultation with biostatisticians. The Internet has vast resources
for microarrays. A few to get you started might include: a microarray
citation electronic library 
http://arrayit.com/e-library/, the National Library of Medicine  PubMed
journal search engine, a general microarray Listserv

GENE-ARRAYS@ITSSRV1.UCSF.EDU. The MGED group (Brazma, 2001) has published the MIAME
standard which specifies (Minimum Information About a Microarray
Experiment). This information is useful in doing an analysis.  Also
try searching using general Internet search engines. There are a
number of public microarray data repositories. One that we find useful
is NCBI's GEO (Gene
Expression Omnibus), that contains array data and MIAME compliant
information about the arrays.

    


      

    • Data mining is a pattern discovery activity - use all the tools
you have.


    • It is open-ended because of the variety of ways data may be
partitioned, normalized, pre-filtered, clustered, and viewed. Patterns
that are apparent in one view may not be apparent in another.


    • When data-mining microarray data, look at correlated genes from
the point of view of what relationships might be interesting from a
biological view by characterizing genes that cluster together using
additional information. I.e. check out the results with various NCBI
and PubMed database searches on the resulting genes, by designing
other lab experiments to better uncover the cause and effect,
etc. Remember correlation does not imply cause and effect.


    


    Organization of Sections in this Chapter
    


  •  3.1 discusses Objectives in data
mining, discovery and analysis,


  • 3.2 describes the steps in an
analysis,


  • 3.3 describes how the database may be interrogated for
gene spot intensity &
identification. 


  • 3.4 describes selecting subsets of
genes using the data filter. 


  • 3.5 describes selecting subsets
of hybridized sample conditions. 


  • 3.6 describes  setting thresholds
using state-slider controls.


  • 3.7 describes how to  export
(i.e. save) report and plot data to your local computer.

    



    

    3.1 Objectives in data mining, discovery and analysis
    

There are a number of objectives an investigator has when analyzing a
set of data. The types of analyses and how useful they are depends on
what they wish to get out of the analyses as well as the type of
data. 
    



    A good and appropriate experimental design (i.e. the design and
setting up of experiments to subsequently be analyzed) is critical for
resolving significant differences in gene expression between
experimental conditions. We touch on some of the issues here.  (Simon, 2001), (Dudoit,2000), and Kerr and
Churchill (2001a, 2001b) discuss some of the issues
of experimental design for microarrays.  We do not currently implement
the Kerr-Churchill method. However, some of the issues involved in experimental design based on the types
of arrays are discussed in Section 3.1.1 for (Cy3/Cy5)-labeled as well
as 33P-labeled samples.

    

If users are comparing two different types of samples, the analysis
would be different than if they were comparing an ordered sequence of
samples (e.g. time series, cell cycle, dose-response, tumor-stage,
etc.). MAExplorer gives users the ability to:


      

    1.  Organize their experiments by sample characteristics allowing them
to perform a variety of mining analyses comparing gene expression
patterns between sets of different samples or comparing a single
sample within an array's set of genes.


    2.  Explore, compare, and record these analyses and share the results
or intermediate data with other investigators.


    3.  Use graphical direct-manipulation combined with statistical
methods, clustering and spreadsheet techniques to gain different
insights into the structure of the data.


    4.  Access public Internet genomic databases for particular genes
that are found to be interesting.

    

    

Briefly, data mining is the discovery of potentially interesting
patterns in the data that were previously unknown. One approaches the
analysis of a set of data with minimal expectations. However, some
idea of what you are interested in helps focus the search. But beware
of the trap of mining the data until you get the results you hope
for. The following figure helps illustrate this process.



    

    
Flow chart of a typical data mining session


    



Figure 3.1 Flow chart of a typical data mining session.  The
user makes some initial decisions on the experimental design such as
which hybridized samples to compare, the type and numbers of
replicates. They then make initial guesses as to the normalization
method to use, and the gene subset (the gene class) to concentrate on
when setting the data filter. The data is viewed in various modalities
to get a feeling for its inherent dynamic range and where interesting
outliers might appear. Clustering and plots helps bring these
differences into view. The results are then evaluated and either the
process is finished or the views are refined by adjusting data
normalization and filter parameters, data subsets to be investigated,
clustering methods, plots etc. and the process repeated until the user
is able to see the differences between gene subsets more clearly or no
significant differences appear to be found.




    

Obviously, this approach is a first approximation to what is
eventually required. But it does capture the flavor of the data-mining
process. Typically the user would refine the search using variations
of the data filters and might contrast (using gene sets and hybridized
sample condition lists operations) results found under one set of
conditions with those found under another set of conditions. 


    Recording the analysis steps during your data mining session -
command history
    

Because of the iterative nature of this process, you might want to
keep a record of the commands you have used or the messages and
measurements you have made. To do this you need to enable message and
command history logging. Go to the View pull-down menu and then select
the type of logging you want using the Show log of messages or the
Show log of command
history commands.

    



    

    3.1.1 Some experimental design issues of microarray experiments
    

 *** THIS SUBSECTION IS IN THE PROCESS OF BEING UPDATED ***
    

Proper experimental design of microarray experiments is critical to
successful use of microarray data. Several recent reports discuss some
of the key issues involved in various aspects of statistical analysis
of microarrays: (Radmacher,
2001), (McShane, 2001),
(Korn, 2001), (Simon, 2001), (Dudoit,2000).


    

     Comparing HP-X/HP-Y for Cy3/Cy5 data as 'ratio of ratios'
    

If we have two samples HP-X and HP-Y with a common reference sample P
(e.g. Cy5P), then we would be comparing the HP-X
"intensity" Cy3X/Cy5X against the HP-Y
"intensity" Cy3Y/Cy5Y. Alternatively,
you can label Cy3 as the common reference sample P in which case just
swap Cy3 and Cy5 in these equations.  If you are using a common
reference standard (i.e. Cy5X1) is the same sample
as Cy5Y1 eg. a pooled sample
Cy5P, then


     a)   (Cy3X/Cy5X1) / (Cy3Y/Cy5Y1)
becomes
 b)   (Cy3X/Cy3Y)

    

However, this new comparison is accompanied by additional noise
because of use of the two Cy5P intermediaries.
    

An alternative method would be to compute
(Cy3X/Cy5Y) directly. However, this too
has its own sources of error and other problems, namely that not all
genes are labeled symmetrically with the two dyes since different dyes
may have different sequence specific affinities due to a variety of
causes.  For that reason, dye-swap experiments are often
done. I.e. the two samples would be run as
(Cy3X/Cy5Y) as well as
(Cy3Y/Cy5X). If one were to plot
(Cy3X/Cy5Y) against
1.0/(Cy3Y/Cy5X) and the data were
perfectly symmetric (which they are not) then one would expect
a straight line. That is generally not what you get in practice.


    

Another issue is that when you have a number of samples A, B, C, D,
..., N and wish to compare them, there are a number of alternate
experimental designs you can use with different resulting sets of
advantages and problems. If a common pooled Cy5P
sample P were used, then the following experiments would be done:


       (Cy3A/Cy5P), (Cy3B/Cy5P), ... , (Cy3N/Cy5P)

    

This assumes that there is enough of the pooled sample P to be used
for all of the experiments - otherwise additional sources of
error would be introduced. MAExplorer is ideally used with this
common reference sample P.

It a common pooled sample is not used, then the experimental
design becomes more complicated - especially if dye-swap experiments
are performed for all samples. For N samples taken 2 at a time
(i.e. Cy3 and Cy5), then the number of experiments may be impossibly
large to perform for other than a very small N. Eg. for N of 3, the
number of experiments is 3 and 6 if dye swap experiments are also
performed.  For N of 4, the number of experiments is 6 and 12. And
this is without doing any replicate experiments. If a reasonable
number of replicates is added, then this set of experiments becomes
even difficult to perform. 
    

MAExplorer is currently not oriented to handling these large
combinatoric types of non-pooled sets of experiments. However, you do
have the ability to swap (Cy3,Cy5) data on an individual basis so you
could compute an average of data from dye-swap experiments - but
with the caveats or non-uniform labeling mentioned above.


      [(Cy3X/Cy5Y) + 1.0/(Cy3Y/Cy5X)]/2

     

In general, this is probably not a very good estimate.

    



    

    3.1.2 Design philosophy of MAExplorer methodology 
    

There are several ways to implement a data mining system on moderate
size databases. The first is that all computations are performed on a
Web server and the user's Web browser displays the results. The second
is download an applet from the Web server, get the data from the
Web server and do computations in the Web browser. A third way is do
download data from a Web server and run a local stand-alone program on
the data. MAExplorer can be run using both the second and third
ways. However, we encourage the use of the stand-alone paradigm as
having the best bandwidth and being the most robust.

The browser-based computation paradigm (as opposed to server-based) is
somewhat unusual. It keeps both the program and data on the server,
making user maintenance of the latest versions easier than if they had
to constantly upgrade the program or data. This also has the distinct
advantage of giving the user instantaneous feedback through rapid
visual and tabular views and the ability to more effectively navigate
the data since the analysis is done on their desktop computer. Because
it is easy to access reference data from other genomic sources
(e.g. UniGene, GenBank, NCI/CIT's mAdb clone DB, dbEST, GeneCard,
etc.), it can be accessed from their respective Web servers as needed.
Complex browser-based computations are used in other data mining or
intensive computation domains.  With the increased bandwidth of the
Internet and compute power and memory of PCs approaching the Cray
supercomputers of the previous decade, this paradigm becomes even more
feasible. However there are limits to how well it scales because of
Web browser limitations. Appendix E.2
discusses these issues in more detail
    
 

    
The major focus of the MAExplorer is interactive data mining with an
emphasis on direct graphical and tabular manipulation of the data.
The investigator is able to interact with the system by clicking on
spots in the array image, points in graphic plots, cells in
spreadsheets, by manipulating threshold sliders or typing in gene
names/clone Ids.  This level of interaction allows investigators to
search for and identify patterns of differences with greater ease than
with a more static graphic system since it is easier to test ideas by
"grabbing onto the data".  For example, "what" is the identity of
"this" outlier I am pointing to in a scatter plot; "which" genes are
best clustered with "this" gene in this clustergram and are perhaps
co-regulated; "which" genes have expression ratios within the range of
the histogram bins to that I am pointing?
    

Direct user manipulation of data, as incorporated in MAExplorer, was
defined by (Schneiderman,
1997) who defends the position that the direct manipulation of data
in data mining is an extremely effective means to amplify human
creativity in understanding patterns.  Schneiderman's dogma states
"overview first, zoom, and then filter details on demand" and favors
the use of "shallow search trees, slide controllers, and
information-right screens with tightly coordinated panel view of
data", (Beardsly,
1999). MAExplorer also uses many of these direct manipulation
principles. It was designed to run on the desktop computers with data
residing on the same computer and loaded into its memory for rapid
direct manipulation - for both the Web browser and stand-alone
versions.
    


     

    3.1.3 Evolution of MAExplorer from earlier proteomic data mining systems
    

MAExplorer was designed to do flexible exploratory quantitative data
analysis of gene data from microarray hybridized sample experiments.
Many of the data-mining concepts are derived from a system called  GELLAB-II
(http://www.lecb.ncifcrf.gov/lemkin/gellab.html) that is a UNIX-based
stand-alone exploratory data analysis system for 2D protein gels over
multiple experiments (Lipkin and
Lemkin, 1981), a review (Lemkin
and Lester, 1989) and examples of graphical representations of
this type of data (Lemkin,
1995). An on-line 
GELLAB-II Web-Poster
(http://www.lecb.ncifcrf.gov/lemkin/gellab-ep93wd.html) is available
showing various screen shots of GELLAB-II in action.  Whereas GELLAB
works with sets of corresponding spots (i.e. proteins) across sets of
2D gel samples, MAExplorer works with sets of genes (spots in the
microarray) across sets of hybridized sample microarrays.  With
protein gels, one typically has spot alignment problems since gels are
generally not superimposable.  This is often called the rubber-sheet
distortion problem and requires localized alignment of spots based of
neighboring spot constellation morphology. We have used Web-based
visual methods to visually compare gels including the  Flicker
(http://www.lecb.ncifcrf.gov/flicker/) image comparison system a Java
applet, (Lemkin, 1997), and the  2DWG
(http://www.lecb.ncifcrf.gov/2dwgDB/) meta-database of 2D gel images,
(Lemkin, 1999a).  Since the genes
are precisely spotted on the arrays, aligning spots between arrays is
not required and greatly simplifies that the data analysis problem.
    

Part of the Flicker system allows comparison of user 2D gel images
with standard images from SWISS-2DPROT for putative identification of
unknown spots in the user gels. The user would select a standard
2D gel image from over 20 tissue types, enter their own 2D gel image
and align them at spots of interest. They could then switch to a
database access mode, click on those spots and generate popup
SWISS-2DPROT Web pages for those proteins - similar to Clone reports
in MAExplorer.  That is accessed at 
http://www.lecb.ncifcrf.gov/flicker/swissProtIdFlkPair.html.

    

MAExplorer will have a groupware facility similar to what we
have done with our 
WebGel (http://www.lecb.ncifcrf.gov/webgel/) system described in
(Lemkin et al., 1999b). It is a
two-dimensional electrophoresis system for sharing data analyses. In
WebGel, users may perform a data-mining analysis and leave the state
of the their analysis and accompanying notes to share with their
collaborators on a login-protected basis.
    



    

    3.1.4 Concepts used in data mining with MAExplorer
    

This section introduces some of the concepts used in data mining
microarrays with an emphasis on how they are used with MAExplorer.
	

    Gene data filters - a Boolean AND of gene set tests
    

A primary MAExplorer concept is that of      gene data filter that selects
a working set of genes by the conjunction (Boolean AND) of user
selectable tests. Each test further restricts the working set of genes
to those meeting the test.  These criteria include gene membership in
particular gene classes, membership in particular user defined or
computed gene subsets, and meeting a variety of statistical
constraints.  Statistics include intra- and inter-array CV, X-Y sets
t-tests. Range test criteria include X/Y ratio ranges and histogram
bins, intensity ranges and histogram bins.  Membership criteria
include test if genes are in the current-cluster (derived from
cluster-analysis), gene set membership, etc.  By selectively including
one or more of these filter restrictions, the user can home in on the
data that appears to be interest. Of course as in real mining, what
appears interesting may not be interesting based on further
investigation.
    



    Set operations on gene subsets
    
 
Because of the complexity of comparing many different replicated
samples, it may be difficult to manually organize the resulting
comparisons.  MAExplorer offers set-theoretic operations on  sets of genes and  sets of hybridized samples
(i.e. intersection, union, difference) to help with this organization
(step 9 in Table 3.2).  The results
of set operations may be saved and used in subsequent set operations,
normalization, as well as with the data filter. This is useful when
comparing and documenting procedures, methods, and analyses from
several subsets of experiments. 
    



    User exploration states
    

Users needs to be able to save and restore the current state of their
explorations of the data and option settings to document and continue
at later times.  When running in stand-alone mode, the user
may save their data mining session on the local disk as in named (.mae
file extension) startup files. Clicking on one of a startup file will
restart MAExplorer and restore the state to that of the time it was
saved.  In addition to filter and parameter status, the HP-X, HP-Y,
HP-X and HP-Y 'sets', HP-E 'list', the named gene sets and HP
condition sets are saved as part of the state 
    


    User groupware sharing of exploration states with collaborators
    

[In the future], these could be saved on a public Web server using
multiple named state files.  These are protected for the user using a
login procedure.  A groupware sharing of these intermediate
exploratory results is available when they allow another user to
access selected states. User states and groupware sharing complete
step 11 in the analysis described in Table 3.2. 
    


We now discuss using these tools for analyzing ones data.
    



    

    3.2 Steps in data mining, discover, and analysis
    

An analysis scenario may use many methods for viewing the data.  A
typical sequence of analysis steps is listed below in Table 3.2 in the
order they might be performed. Note that this is a rough guide
for a possible analysis and the iteration and backing up for
of some of these steps is required for data mining complex sets of
conditions, especially in the setting of constraints for the "data
filter" (step 4) when the user focuses on subtle patterns of interest
(c.f. Figure 3.1).



    

    
Table 3.2 Steps in a data-mining analysis. 

    

      


    1.  Select a set of hybridized samples to be analyzed. Additional
samples may be added or removed from the HP-X, HP-Y and HP-E 
sample lists prior to or during an analysis.


    2.  Organize samples by selecting query type: 2-class (X vs. Y)
sample sets or N-sample ordered expression lists.
 

    3.  Select normalization (transformation scaling) method appropriate
to the type of data and what types of changes you are looking for
(patterns or outliers).


    4.  Restrict search using the "data filter" to subset genes
(pre-filter data) by gene-class, gene-subset, ratio range, intensity
range, coefficient of variation, t-test, expression profile, cluster
membership, etc.


    5.  Review scatter and expression plots, ratio and intensity
histograms to gain insights in overall characteristics of the data
range and intensity distributions. Export results using either SaveAs
or cut and paste operations.


    6.  Cluster genes by similar expression profiles and other clustering
methods. Review similarity, clustergram and dendrogram plots and
reports. Export using either SaveAs or cut and paste operations.


    7.  Select subset(s) of genes of interest in plots or reports.
 

    8.  Access other Web genomic databases for genes of interest from
spreadsheets or plots.


    9.  Save genes of interest in named gene sets and perform set
operations if required.


    10.  Create gene reports for export to Excel using tab-delimited
Reports and either SaveAs or cut and paste operations.


    11.  In the stand-alone version of MAExplorer, save the state of
exploratory data analysis for later use or sharing.

    

    

In designing a data mining experiment, the first decision to be made
is selecting the set of hybridized samples to be compared (steps
1 and 2).  This is accomplished by setting the current hybridized
sample-X (HP-X) and hybridized sample-Y (HP-Y).  In Figure 2.4.4.2 for the
scatter plot we selected a single C57B6 pregnancy day 13 and a single
Stat5a (-,-) pregnancy day 13 as current HP-X and current HP-Y
samples. Changing the normalization changes the view in the scatter
plot so that hidden differences may be more apparent (see  Figure 2.4.2.3)

    

The names of the current HP-X and HP-Y samples are displayed at the top
of the main window.  The current HP-X and HP-Y samples may be changed
at any time by clicking on a new sample from a list of samples shown on
the left side of the main window or from lists of samples organized by
sample population in the Samples menu.
    

The next decision to be made is selection of the genes to be studied
by choosing a subset from the  gene class menu list
(step 4).  Further selection occurs throughout the analysis by
clicking on spots in microarray images, points in graphic plots or
cells in spreadsheets, by adjusting threshold sliders, or using the
text-entry "guesser" to type in gene names, clone IDs, genomic IDs,
samples, etc.
    

The next decision the user must make is to set the intensity data
normalization mode (step 3).  Normalization of quantitative data is
crucial when comparing data between different hybridized microarrays
because of spotting, hybridization efficiency, uniformity, and
other systematic errors. 
    

Genes of interest may be separated for all of the genes in the
database using a cascade of data filters (step 4).  Additional
filtering options are easily accessible in the (data)  Filter menu. Some of the filters
require additional parameters. These parameters are set by state
scroll bars that pop-up on the screen when data filters requiring them
are added to the filter cascade.  Changing scroller values causes the
data filter to be automatically be reapplied and a new set of genes
to be computed. 
    

It is desirable to reduce false-positives found by the data filter by
eliminating genes with high quantification variability between
duplicate spots on the same sample or spot duplicated in replicate
samples.  If duplicate genes are available on the array (denoted by
Field 1 and Field 2 or F1 and F2 spots), this allows the computation
of a coefficient of variation (CV) for the duplicates. This CV may be
used in a data filter to reduce potential false-positives.  CV is
computed as 2|F1-F2|/(F1+F2) using those spot values for each gene,
as StdDevHP/MeanHP for a set of replicate
hybridized samples.
    

Graphical views of the data give the user additional insights into the
data.  These include spot  intensity
and  ratio or
 Zdiff pseudoarray
images, scatter
plots, histogram plots, expression profile plots,
cluster plots showing genes similar to a
specified gene, the number of clustered
genes for each clone, divisive clusters for K-means clustering, and clustergrams and
dendrogams for hierarchical clustering.
    



    Scatter plots are useful for visualizing data from two conditions
    

The scatter plot method (step 5) allows the user to plot the intensity
data between two samples, the X-sample and the Y-sample.  Gene data
may be spot data for two different samples (HP-X and HP-Y), means of
two different sets of hybridized samples of replicate samples (sets of
HP-X and sets of HP-Y), or the left and right normalized replicate
data (F1 vs. F2) for the current hybridized sample. If Cy3/Cy5 data is
used, then each sample is the ratio of data from two different
hybridized samples. So if we have sample Cy3a and Cy3b then HP-X could
be Cy3a/Cy5 and HP-Y could be Cy3b/Cy5 such that we are scaling the
Cy3a and Cy3b samples using a common Cy5 normalization sample.
Scatter plots are useful for obtaining a better understanding of the
outliers when comparing different hybridized samples and determining
the reproducibility of spotting when comparing F1 vs. F2 data or
replicate sample data.
    


    

    Filtering genes by histogram plots of ratios, Zdiffs or intensities
    

Histogram plots may be generated from either X/Y ratios or (X-Y)
Zdiffs of two different hybridized samples (single samples or X and Y
replicates) or from the F1/F2 intensities of a single hybridized
sample.  Selecting a bin in a histogram restricts filtered genes to
those that are contained in that histogram bin. As an alternate
method, data filtering by ratio (Zdiff) or intensity range may
be used with adjustable range scrollers independent of the
histograms. However, histograms and scrollers may be used together.
For example, one could filter by the ratio histogram after filtering
out genes with low-intensity values that may be considered noise using
the intensity sliders.  That might help eliminate falsely high ratios
resulting from dividing high X values by a very small noisy Y values.
Histograms are useful for getting a better understanding of the range
and distribution of the gene intensities or ratios. 
    


    Expression profile plots (EP-plot) of N conditions for viewing
time series, etc.
    

List HP-E is an ordered list of samples - as different from HP-X and
HP-Y that are unordered sets of samples. The expression profile (step
5) of a gene is the plot of its normalized intensity as a function of
the samples in the ordered HP-E list.  It may be plotted for the
current gene in a pop-up window. Selecting a different current gene
causes the EP-plot to be displayed for that gene. Multiple EP-plots
may be created to view the differences between a few genes you are
investigating further. The HP name button pops up a window with
the ordered list of samples so you can see the details of the sample
names being plotted. Selecting a line in a plot displays the intensity
data and sample name for that hybridized sample. The data may be
plotted as a bar, point or continuous curve and error bars may be
turned off to better compare multiple plots.
    

When there are too many EP-plots to be viewed simultaneously, you
might use a scrollable list of expression profile plots that lets you
scroll through an arbitrarily large list of genes. However, it is
difficult to compare genes that are not sorted in some way
(i.e. clustered). Therefore, these are most useful when used after
clustering the data and  displaying the scrollable EP-plots of
the cluster-order data. 
    

Clustering is one way of possibly finding co-expressed genes that
exhibit similar expression changes in a set of samples. Genes may show
similar co-expression, but that does not prove they are co-regulated
at the same point in a pathway - merely that measurements of those
genes in a particular set of experiments show similar
expression. However, identifying genes with similar expression for
which some information is already known about some of the genes may be
useful as a starting point to help figure out gene function and
pathway using additional experiments and analysis.
    

There are many methods for doing clustering - each with advantages and
disadvantages. We present three methods in MAExplorer and plan on
adding a variety of more powerful methods through the MAEPlugin
facility under development.
    



    Finding clusters of genes with similar expression profiles:
similar, cluster counts, K-means, and hierarchical methods
    

We may define a cluster of genes as a set of genes whose expression
profiles are found to be similar (step 6). The samples used in
computing the expression profiles are specified by the HP-E ordered
list. You can scale the list of normalized intensity data for each
gene to 1.0 (resulting in finding genes with similar shaped
EP-plots).  Alternatively, if you don't scale this data it will
cluster more on magnitude changes. You can select either the Euclidean
distance or the correlation coefficient of the EP lists between two
genes as the measure of gene-gene distance. Similarity is 1.0 -
normalized distance.
    


The first cluster method finds a cluster of genes whose expression
profiles are similar to that of the currently selected gene. This list
of genes is restricted by the constraint that the cluster distance
between each of these genes to the selected gene is less than the
"Cluster threshold" distance set by the user with a scroll bar.  It
displays genes that are found both with blue boxes (the larger the
box, the higher the similarity) and in a text report window showing
the genes and their distances to the current gene.  By varying the
threshold and observing the results, the user can find a set of highly
correlated genes. If the threshold is set to 0.0, no genes are
found. If it is set too high, all data filtered genes are found.  So
it is critical to adjust the threshold to a reasonable level
commensurate with the type of data being analyzed and the approximate
number of genes expected.
    

A second cluster method draws blue circles in the array image around
all filtered genes meeting the threshold criteria, where the larger
the circle the larger the number of similar genes (i.e. passing the
threshold) are found to be clustered with that gene.  Clicking on a
gene toggles between the first and second methods.  For both of these
methods, it will pop-up a "Cluster Distance" threshold scroller and
recomputes the clusters if you change the scroller value or the current
gene. It also shows a text report that displays the number of genes
similar to each data filtered gene.
    

A third method called "K-means" clustering K genes (we call primary
nodes) whose expression profiles are most orthogonal to each other. It
uses the current gene as the first or "seed" node.  It then finds the
gene furthest from this and assigns it as node 2. Then the gene
furthest from both nodes 1 and 2 is assigned to node 3, etc.  This
process is repeated until all K nodes are assigned.  Then the
remaining genes are assigned to the closest node. Having defined the
initial cluster centers, it recomputes the centroid of each of the
clusters. The centroid can alternatively be computed using a median
instead of a mean in which case we would be doing K-median clustering
(Bickel, 2001). K genes are
then reassigned to the nearest new centroids as the new K-means node
instances.  Finally, the remaining genes are assigned to the nearest
centroid.  A scrollable K-means cluster text window report pops up
with genes sorted by cluster. Clicking on a gene in either the array
image or scatter plot assigns all genes in the cluster to which that
gene belongs to the "current cluster".  Genes in the current cluster
are labeled in the array and scatter plot with a small number of the
cluster.  In addition, genes in the current cluster are copied to the
E.G.L. where they can be used in a report, saved in a named gene set,
or used for additional filtering.  It also pops up a "N-clusters"
scroll bar window to let you dynamically adjust the number of
clusters.  Changing N will recompute the clusters.  When the K-means
is recomputed, it uses the current gene as the initial seed gene.
    

The fourth method is a hierarchical clustering method that generates a
clustergram and dendrogram similar to that of Eisen's red-black-green
clustergram (Eisen, 1998). This
was derived from the clustered correlation map (ClusCor) of Weinstein
et al. (Weinstein, 1997).  The
MAExplorer clustergram and dendrogram are dynamic and may be
interrogated and used to set the current gene. This means that it may
also position a corresponding ordered list of expression profile plots
to the same gene so you may view the data as a plot as well.  The
dendrogram may be zoomed in to explore a part of the dendrogram in
more detail.  As with the K-means clustering, a report can be made of
the ordered genes.
    



    Gene reports: dynamic spreadsheets for Web access or
tab-delimited for exporting data to Excel
    

Pop-up report windows (step 7) may be generated for either individual
genes or a global array sample data. Instances of the latter include
experimental information and Web links, global statistics, correlation
coefficients between array samples, etc.  Gene reports may present
this data in a number of ways. These include: highest/lowest gene
ratios, profiles, parametric and cluster statistics, etc.  Reports may
be presented as either dynamic Web-interactive spreadsheet tables or
as static tab-delimited tables. The latter is useful for exporting
data using cut and paste into Excel (step 10).  If the user clicks on
a blue hyperlinked cell in a dynamic spreadsheet table, it pops up
another Web browser window and loads it with data (step 8) from the
respective Internet genomic database such as mAdb Clone DB, UniGene,
GenBank, dbEST, GeneCard, and MGAP model and histology Web pages.
    



    Collaborative groupware environment
    

Having immediate access to collaborator's data is a powerful research
tool. A collaborative environment is being implemented for MAExplorer
that allows groups of users to share data and intermediate
results. These capabilities include: 1) the ability to save and
restore exploratory data mining sessions (states) through the Web
server including named sets of genes, and 2) to selectively share
these states with collaborators. The latter process is sometimes
called a groupware environment because if offers a collaborative group
the ability to share and interact. These capabilities are modeled
after our WebGel system (Lemkin
et al., 1999b).  In addition, users can create a custom database
Web page as a subset of samples from the entire database. This may be
saved on their own computer through their Web browser's "File/Save as"
command. This hypertext file could then be used at a later time to
access the database or be E-mailed to a collaborator to do the same.

    


    

    3.2.1 Definition of expression profile
    

It is helpful to define an expression profile. There may be alternate
definitions, but the following is useful for getting an understanding
of how it might be computed.  An expression profile
ej of an ordered list of N samples (k=1
to N) for a particular gene j is a vector of scaled
expression values vjk. 
    

Then, the expression profile is expressed as a list of values:


       ej = (vj1, vj2, vj3, ..., vjN)

    

A difference between two genes p and q may be estimated as a
N-dimensional metric "distance" between ep and
eq. The Euclidean distance is then defined as


       dpq = (1/N SUMj=1:N (vjp - vjp)2 )1/2

    
             
Other distance measures may include correlation coefficient,
city-block (or manhatten distance) etc.
    

For scaled data such that dpq has a maximum value of 1.0
ovger all samples. A similarity measure could be computed as
1.0 - distance or


     
  spq = 1 - dpq

    



    

    3.2.2 Clustering Methods
    

Clusters represent one way to identify similar gene expression across
a set of experiment samples. There are many ways to cluster the data,
some of which are available in MAExplorer. These include:


      

    1.  Finding genes with similar expression 


    2.  K-means clusters where the number of clusters K is fixed prior to
clustering and a particular gene is used to start off the cl.


    3.  Hierarchical clustering where a binary tree hierarchy is created

    

Other methods include Self Organizing Memory (SOM), fuzzy clustering,
Support Vector Machines (SVM), etc.



    

    3.2.2.1 Clustering similar genes
    

If we have a particular gene s (the "seed" gene), we may want
to find a set of all genes {gj} similar to
gs. We can find this set of genes by testing

We define a particular gene gj as similar to seed
gene if the distance between genes s and j  meets
the following criteria. 


      djs < T

    

The threshold T is set by the investigator and in MAExplorer
is changed using a slider. Typically, the set of all genes
{gj} found is sorted by similarity before being
viewed.



    

    3.2.2.2 K-means clustering
    

K-means clustering finds K clusters of genes with similar expression
profiles to a given gene (see     
Sneath and Sokol, 1973).  Given the number of clusters K,
we could use high variance of clusters to determine if they should
split into sub-clusters. K-means clustering does not need a distance
matrix (see Hierarchical clustering which follows), so it is faster
and may cluster large numbers of N genes. However, it is
highly dependent on seed selection. It may be useful for getting an
initial estimate - especially if other techniques (such as silhouette
plots) are also used.  The following is a simplified definition of one
way to compute a set of K-means clusters of gene expression profile
data.
    


    

    

    

Algorithm:

      

    1.  Pick seed gene s and put it into cluster 1 (let k = 1)


    2.  For all clusters j = 1 to k, find gene q such
that djq is a maximum


    3.  Set k = k+1.  Put gene q into new cluster
k


    4.  For j = k to K, repeat steps 2 and 3 until there are
K clusters


    5.  Then, assign (N-K) remaining genes q into one
of the K clusters j with minimum
djq


    6.  Compute new virtual gene cluster centroids as means or medians
{ek} for each of K clusters


    7.  Reassign all N genes q into K new
clusters with minimum dpq using virtual genes
{ep}


    8.  Variants: use multiple seed genes, range of K values, minimize CV


    

    

    

    



    

    3.2.2.3 Hierarchical clustering
    

Hierarchical clustering of a set of genes will generate a binary tree
of clusters with the genes at the terminal ends of the tree and a
single cluster of the entire tree at the top (also called the root) of
the tree. See (Sneath and Sokol,
1973) for a discussion on hierarchical clustering. There are many
other variants of hierarchical clustering.  Hierarchical clustering
requires a distance matrix or the equivalent of one [there are more
efficient ways to compute it]. ForN  genes (terminal
clusters), it generates 2N-1 clusters.  Distance matrix is
upper diagonal matrix D  of dpq  of size
N(N-1)/2.
    

D  can get quite large for clustering a large number of genes
N  [for N=5000, this is > 50 Mbytes!]
    

The following is a simplified definition of one way to compute a
hierarchical clustering of gene expression profile data.
    


    

    

    

Algorithm:


       1. Assign all N genes to clusters 1 to
N, set n to N


       
2. Find two clusters p and q
such that dpq is a minimum doing the following:

  
    2.1 Compute a virtual cluster vector ek = average (ep,eq)
  
    2.2 Set n = n+1
  
    2.3 Assign virtual cluster to new cluster 
  


       3. Repeat step 2 until n = 2N-1.

    

    

    

    




    

    

    3.3 Display of gene spot intensity and identification data measurements

    

You may select the current gene by clicking in the pseudoarray image or in the X-Y scatter plot and
MAExplorer reports. The
microarray grid coordinates, normalized quantified spot intensity
data, plate coordinates, gene name (if known) and associated data for
that gene.  If you are displaying a pseudocolor ratio (X/Y) or Zdiff
(X-Y) image, it will report HP-X/HP-Y or (HP-X - HP-Y) data
respectively.  It also sets the gene as the current gene. The
pseudoarray image coordinates are reported as:
    


         [<field>-<grid name><row#>,<col#>].
e.g.
     [1-A4,3]

    
 
    

If there is only one field in the array, it will appear as field 1. In
the above example, [1-A4,3] is field 1 grid A
row 4 and column 3. Note that the pseudoarray coordinates are for
visualization purposes in MAExplorer and may or may not be the same as
the coordinates on the actual array. That depends on how the
MAExplorer database was defined in the configuration file described in
Appendix C.
    

When the current gene is defined, it will draw a yellow (green) circle
around the spot in the ratio (intensity) pseudoarray image and display
other features of the gene in the three-line status area near the top
of the main window.  If background correction is enabled (the "Use
background intensity correction" in the Normalization menu), then spot
intensity values will appear as intensity' (with background
intensity subtraction) and intensity (without background
subtraction). 
    

There are a number of different reporting formats available depending
on the array display mode and particular normalization method
selected.  These include: the pseudoarray image of the intensity of a
single sample, the pseudocolor ratio X/Y or Zdiff (X-Y) image (using
either HP 'sets' or single samples), or the ratio of Cy3/Cy5 for
dual-labeled dyes or F1/F2 for replicate spots for a single sample.
In addition, the normalization mode is also displayed in the reporting
line. We will present examples of each of these different reporting
formats.
    

You may show the intensity data for a particular spot in the currently
displayed pseudoarray image.  First select the "Pseudograyscale image"
option in the "Show Microarray" submenu in the "Plot menu".  If your
data has duplicate grids (i.e. fields F1 and F2) then you may look at
F1, F2 and mean (F1+F2)/2 data in the reports when you click on a
spot. If the "Gang F1-F2 scrolling" switch is disabled in the "View
menu", then the intensity value is the intensity data value
for the gene at that location. If the "Gang F1-F2 scrolling" switch is
enabled, then it reports intensity[F1], intensity[F2], and the F1/F2
ratio. These two formats are shown in the following two examples for a
C57B6 pregnancy day 13 samples in the MGAP database:
    

a) Field F1 spot for a single spot in a single sample with the median
intensity selected.
    


    
[1-A4,5] intensity=4.5267, (Norm.: median intensity)
CloneID: 1248228, dbEST3': 2279072, GenBankAcc3': AI463183, UniGene: Mm.13859, plate[5,A,5]
GeneName: Mus musculus ribosomal protein L41 mRNA, complete cds


    


b) Field F1 and F2 replicate spots for a single sample. The top line
is shown for each of the different normalization methods.


    
[1-A4,5] intensity[F1]=-0.3067, intensity[F2]=-0.2312, F1-F2=-0.0755, (Norm.: Zscore intensity)
[1-A4,5] intensity[F1]=4.5267, intensity[F2]=6.2408, F1/F2=0.7253, (Norm.: median intensity)
[1-A4,5] intensity[F1]=0.8755, intensity[F2]=1.1457, F1-F2=-0.2701, (Norm.: log median intensity)
[1-A4,5] intensity[F1]=-0.1442, intensity[F2]=-0.0945, F1-F2=-0.0497, (Norm.: Z-score, stdDev, log intensity)
[1-A4,5] intensity[F1]=-0.1533, intensity[F2]=-0.1004, F1-F2=-0.0528, (Norm.: Z-score, mean abs.deviation, log intensity)
[1-A4,5] intensity[F1]=630.9911, intensity[F2]=869.9273, F1/F2=0.7253, (Norm.: calibration DNA intensity)
[1-A4,5] intensity[F1]=1919.9376, intensity[F2]=2646.957, F1/F2=0.7253, (Norm.: scale to max. (65K) intensity)
CloneID: 1248228, dbEST3': 2279072, GenBankAcc3': AI463183, UniGene: Mm.13859, plate[5,A,5]
GeneName: Mus musculus ribosomal protein L41 mRNA, complete cds


    

If the "Pseudocolor HP-X/HP-Y ratio or Zdiff" option is selected in
the "Show Microarray" submenu, data is reported as either Ratio or
Zdiff data depending on the normalization method selected. The data
used in the following examples is for C57B6 pregnancy day 13 (HP-X)
compared with Stat5a (-,-) pregnancy day 13 (HP-Y).
    


c) Ratio data for two samples X and Y in separate hybridized
arrays. Ratio data for the field F1 and F2 spot data as well as the
mnX/mnY ratio is reported. The median normalization was used in this
example.


    
[1-A4,5] HP-XY: mn(X,Y)=(5.383,6.834) (X/Y)(F1,F2,mean)=(0.651,0.928,0.787), (Norm.: median intensity)
CloneID: 1248228, dbEST3': 2279072, GenBankAcc3': AI463183, UniGene: Mm.13859, plate[5,A,5]
GeneName: Mus musculus ribosomal protein L41 mRNA, complete cds


    


d) Zdiff data for two separate samples X and Y. Ratio data for the
field F1 and F2 spot data as well as the mnX-mnY Zscore difference is
reported. The three Zscore, ZscoreLog, and logMean normalizations were
used in this example (first lines are shown).


    
[1-A4,5] HP-XY: mn(X,Y)=(-0.269,0.151) (X-Y)(F1,F2,mean)=(-0.470,-0.370,-0.420), (Norm.: Zscore intensity)
[1-A4,5] HP-XY: mn(X,Y)=(-0.119,0.051) (X-Y)(F1,F2,mean)=(-0.199,-0.142,-0.170), (Norm.: Z-score, stdDev, log intensity)
[1-A4,5] HP-XY: mn(X,Y)=(1.010,1.224) (X-Y)(F1,F2,mean)=(-0.362,-0.064,-0.213), (Norm.: log median intensity)
CloneID: 1248228, dbEST3': 2279072, GenBankAcc3': AI463183, UniGene: Mm.13859, plate[5,A,5]
GeneName: Mus musculus ribosomal protein L41 mRNA, complete cds


    


e) Example of when the "Use dual HP-X & HP-Y Pseudoimage" mode is
enabled in the "Show Microarray" submenu of the "Plot" menu. This
displays mean data for the HP-X and HP-Y data side-by-side. The median
normalization was selected.


    
[1-A4,5] intensity[X]=5.3837, intensity[Y]=6.8342, X/Y=0.7877, (Norm.: median intensity)
CloneID: 1248228, dbEST3': 2279072, GenBankAcc3': AI463183, UniGene: Mm.13859, plate[5,A,5]
GeneName: Mus musculus ribosomal protein L41 mRNA, complete cds


    




    

    Reporting for multiple hybridized samples when using HP-X/-Y 'sets'
    

If you have enabled MAExplorer to "use HP-X and HP-Y 'sets' of
multiple samples" rather than single samples" in the Samples menu, it
will report a spot differently using the means (mn), standard
deviations (S.D.), coefficient of variations (CV) for the samples in
the HP-X and HP-Y 'sets'. For duplicate fields, these are computed
using the normalized average of F1 and F2 spots for each gene in each
samples.  The data used in the following examples is for three C57B6
pregnancy day 13 (HP-X) samples, and five Stat5a (-,-) pregnancy day
13 (HP-Y) samples. 
    


f) Multiple HP-XY 'sets' using median normalization for the pseudoarray 
image display for the HP-X 'set' of three C57B6 samples.


    [1-A4,5] HP-X 'set' mean intensity=3.295 stdDev=1.482 CV=0.449 n=3, (Norm.: median intensity)
CloneID: 1248228, dbEST3': 2279072, GenBankAcc3': AI463183, UniGene: Mm.13859, plate[5,A,5]
GeneName: Mus musculus ribosomal protein L41 mRNA, complete cds

    


g) Multiple HP-XY 'sets' using median normalization for the pseudoarray image
display for the HP-Y 'set' of  five Stat5a (-,-) samples.


    [1-A4,5] HP-Y 'set' mean intensity=8.180 stdDev=0.986 CV=0.120 n=5, (Norm.: median intensity)
CloneID: 1248228, dbEST3': 2279072, GenBankAcc3': AI463183, UniGene: Mm.13859, plate[5,A,5]
GeneName: Mus musculus ribosomal protein L41 mRNA, complete cds

    
    

h) Multiple HP-XY 'sets' using median normalization for the
pseudoarray image display for the HP-X and HP-Y 'sets' when the "Use dual
HP-X & HP-Y Pseudoimage" mode is enabled in the "Show Microarray"
submenu of the "Plot" menu.



    [1-A4,5] HP-XY 'sets': mn(X,Y)=(3.295,8.180) mnX/mnY=0.402 SD(X,Y)=(1.482,0.986) CV(X,Y)=(0.449,0.120)\
      n(X,Y)=(3,5), (Norm.: median intensity)
CloneID: 1248228, dbEST3': 2279072, GenBankAcc3': AI463183, UniGene: Mm.13859, plate[5,A,5]
GeneName: Mus musculus ribosomal protein L41 mRNA, complete cds

    
    

i) Multiple HP-XY 'sets' using median normalization for ratio (HP-X/HP-Y) data
for the "Pseudocolor HP-X/HP-Y Ratio or Zdiff" display.


    [1-A4,5] HP-XY 'sets': mn(X,Y)=(3.295,8.180) mnX/mnY=0.402 SD(X,Y)=(1.482,0.986) CV(X,Y)=(0.449,0.120) \
        n(X,Y)=(3,5), (Norm.: median intensity)
CloneID: 1248228, dbEST3': 2279072, GenBankAcc3': AI463183, UniGene: Mm.13859, platey[5,A,5]
GeneName: Mus musculus ribosomal protein L41 mRNA, complete cds

    

    

j) Multiple HP-XY 'sets' p-value using median normalization for ratio
(HP-X/HP-Y) data for the "Pseudocolor (HP-X,HP-Y) 'sets' p-value
display.


    [1-A7,20] HP-XY: mn(X,Y)=(3.449,0.853) (X/Y)(F1,F2,mean)=(4.09,4.008,4.041), (Norm.: median intensity)
CloneID: 1382656, dbEST5': 1775754, GenBank 5': AI036495, UniGene: Mm.300,  plate[12,A,8]
GeneName: Carbonic anhydrase 3

    

    



    

    Reporting for Cy3 and Cy5 channels for a single hybridized sample
    

k) If you have Cy3/Cy5 data, then you can look at the two channels for
a single sample ( the current HP sample). For median normalization and
the display set to "Pseudocolor Red(Cy5)-Yellow-Green(Cy3) Cy3/Cy5
data" display.


    [1-A6,11] Cy5/Cy3=0.3588, Cy5=67.324, Cy3=187.622, (Norm.: median intensity)
CloneID: IMAGE:1054189, 
GeneName: expressed sequence AW213287

    

    



    

    Reporting HP-X (Cy3 or Cy5) vs HP-Y  (Cy3 or Cy5) data for 2 samples
    

l) If you want to compare Cy3 or Cy5 in the HP-X sample with a Cy3 or Cy5
value in the HP-Y sample, you do it through the special Cy3,Cy5
scatter plots. There are four types of plots:

      

    1.  HP-X Cy3 vs HP-Y Cy3

    2.  HP-X Cy3 vs HP-Y Cy5

    3.  HP-X Cy5 vs HP-Y Cy3

    4.  HP-X Cy5 vs HP-Y Cy5

    

After the plot is started, clicking on a scatter plot will report data
from the point in that plot will print the following data as shown in
the following example where HP-X Cy3 is plotted against HP-Y Cy3.



    [1-A5,16] intensX=4.695, intensY=5.923, (X-Y)=-1.2275, (Norm.: log median intensity)
CloneID: IMAGE:963758, 
GeneName: RIKEN cDNA 2410114O14 gene

    

    



    

    


    

    3.4 Selecting subsets of genes using the data Filter
    

Genes may be selected on a number of criteria specified by the     data Filter (Section 2.4.3)
that is a cascade of data tests. The first might be the gene class (Section 2.4.1) to
restrict the set of all genes to a particular subset.  Various numeric
and statistical data tests might be applied on the remaining genes to
exclude those not meeting these tests. For example, genes having a
high coefficient of variation between duplicate spots on the same
sample or duplicate samples could be eliminated.  Then one could
select genes that had a (HP-X/HP-Y) ratio greater than 4.0 but less
than 8.0, etc. The latter could be done using either the ratio
scrollers or by clicking on that bin in the ratio histogram plot. See
the Filter menu options and look at one of the tutorials (Appendix A) for ideas on
adjusting the Filter to close in on a particular subset of genes.
    




    

    3.5 Selecting subsets of hybridized sample conditions
    

Sets and lists of hybridized sample conditions (HP-X and HP-Y sets,
HP-E list) may be selected using various commands from the  Samples Menu (Section 2.2)
including pull-down menus, guessing by name or part of a name, or
using a 
"Chooser" (see Figure 2.2.1) to design your settings for the
current (HP-X and HP-Y sets, HP-E list). The Chooser is the easiest
way to select these entries.  In addition, if you want to change the
current HP-X or HP-Y individual sample, you can do this directly from the array
pseudoarray image by clicking on the [X] or [Y] part of the image
and then selecting the particular sample to use. Note that if the
mouse-over checkbox is enabled, then moving the mouse over the sample
names gives you the full sample name. Otherwise, the sample name may
be truncated.


    



    

    3.6 Setting threshold values using the state-scroller sliders
    

You may filter genes using a variety of thresholding operations (see
the Filter menu (Section
2.4.3) to select any of these).  For example, these include a spot
intensity (per channel) range [SI1:SI2], gene intensity range [I1:I2],
ratio range [R1:R2], Zdiff range [Z1:Z2], Coefficient Of Variation
(CV) range [0:1.0], p-value range [0:1.0] for the
t-test, etc.  Additional threshold scrollers are used with the
clustering methods including the number of clusters (default 6), the
maximum cluster distance from a gene to a another gene for the latter
to be considered in the same cluster, and the absolute difference
between HP-X and HP-Y.
    

For the intensity and ratio threshold filters, the range
interpretation may be inside, or outside the specified range.  The
ratio range [R1:R2] is between 0.01 and 100.0. The Zdiff range [Z1:Z2]
and [CZ1:CZ2] are between -4.0 and +4.0. The intensity threshold range
[I1:I2] is set to the dynamic range of the min and max intensity for
the current normalization method.

    

A list of possible threshold sliders is shown in the following table.
When a Filter is enabled that requires a slider, it pops up the
State Scrollers window that contains one or more
slides. When you disable all filters that use these sliders, the
popup window will disappear. The corresponding Ratio R1[R2] or
Zdiff Z1[Z2] sliders are used if you are using a ratio or
Zscore normalization - and will change if the normalization changes
while the filter is active.
    

Some of the sliders are implemented with a non-linear scale so that
you have more resolution at the low end (eg. p-Value, Spot CV, Diff
HP-XY).
    

Depending on the set of data Filters selected, there may be multiple
sliders present in the State Slider popup window (eg. see  Figure
2.4.3).
    



    

    

Table 3.3.1. List of threshold sliders. Sliders are enabled in
the State-Scroller popup window when the corresponding data filters
are enabled.

    


    

    Slider nameAssociated with operation
    

    Spot Intensity SI1Filter by spot intensity range per channel
    

    Spot Intensity SI2Filter by spot intensity range per channel
    

    Percent SI OKFilter by percent of spots whose spot intensity
    
is in threshold range criteria meets the AT LEAST or AT MOST criteria 
    

    Intensity I1Filter by gene intensity range
    

    Intensity I2Filter by gene intensity range
    

    Ratio R1Filter by gene X/Y ratio range
    

    Ratio R2Filter by gene X/Y ratio range
    

    Zdiff Z1Filter by gene X-Y Zdiff range
    

    Zdiff Z2Filter by gene X-Y Zdiff range
    

    Ratio CR1Filter by Cy3/Cy5 gene X/Y ratio range
    

    Ratio CR2Filter by Cy3/Cy5 gene X/Y ratio range
    

    Zdiff CZ1Filter by gene (Cy3-Cy5) X-Y Zdiff range
    

    Zdiff CZ2Filter by gene (Cy3-Cy5) X-Y Zdiff range
    

    p-ValueFilter by t-Test
    

    Spot CVFilter by Coefficient of Variation
    

    Cluster DistancePlot - cluster by expression similarity
    

    # of ClustersPlot - K-means clustering
    

    Diff HP-XYFilter by absolute difference (HP-X,HP-Y) 
    

    Spot QualityFilter by continuous spot quality (If data available) 
    

    

    

    


    

    

    3.7 Exporting report and plot data
    

Data is typically reported in MAExplorer in report and plot
windows. These may be saved using cut and paste if your are using
MAExplorer as an Applet or with "SaveAs" buttons on the popup
windows if you are running it as an stand-alone application . Reports
are then saved as text (.txt extension) files, and plots are saved as
GIF (.gif extension) files.

    

If you are running on a windowing system supporting cut and paste,
then you may cut and paste data from reports and plots into
applications on your system that allow you to save or print this
data. Set the Report menu table-format to "Tab-delimited".  Then, in
Windows 95/98/NT/2000/XP, cut data from the popup tables (or other text
reports) and paste it into Microsoft Excel.  In Windows, you can
capture (i.e. "cut") the entire screen by pressing the "Prt Sc" or
print screen button. To capture a specific window (e.g. a scatter
plot), hold the "Alt" key when pressing the "Prt Sc" key.  Then go
into a Windows imaging application (such as PhotoShop) and paste it
into the application.  In PhotoShop, in the File menu, select New (or
type Control/N). Then when the window is opened, click on the window
and paste the MAExplorer screen you had cut into the image window by
typing Control/V. In both Excel and PhotoShop you may print the data
or save it in a file.



    







 MAExplorer - Microarray Exploratory Data Analysis



 


    4. Status and Bugs of MAExplorer
    

This section discusses the status and known bugs in MAExplorer. It
also discusses dealing with the reporting of fatal errors so we can
resolve them.
    

Section 4.1 discusses known bugs, Section 4.2
lists the revision notes for older versions known bugs. If you have experienced bugs
with an older version of MAExplorer, you might check the revision notes
to see if the bug was fixed and download a new version.  Section 4.3
discusses problems in using MAExplorer
as an applet with Web browsers.  Section 4.4 describes handling fatal "DRYROT" errors.


    


    


    List of known bugs in MAExplorer
4.1 Known Bugs in MAExplorer
    

Disclaimer: none of our code ever has bugs... :-). So despite
this, we are working on resolving these bugs and implementing planned
functionality. Here is a short non-inclusive list of known problems
that we are resolving. We welcome and encourage you to E-mail us with
any bugs that you find do exist as well as suggestions for
capabilities you would like to see. As the new open-source  MAEPlugins facility evolves, most
new (and some old) functionality will migrate to these plugins. Then
the user community can help maintain these analytic methods.
    

If you encounter a fatal error that is detected by MAExplorer, it will
popup an  error reporting
window. Please E-mail this data to us so we can try to resolve the
problem.

    

In the mean time, partially implemented commands are disabled to keep 
you out of trouble :-) ...
    

You can help us and get MAExplorer to do more of the things you
would like to see. Let us know of problems that you encounter as well
as suggestions for changes or new methods you would like to see - 
send us E-mail.

    


    

    4.1.1 Browser Applet Bugs

    4.1.2 Downloading and Installer Bugs

    4.1.3 Computation speed and display Bugs

    4.1.4 User state and login Status

    4.1.5 Data file names Bug

    4.1.6 Gene Sets Bugs

    4.1.7 Clustering Bugs

    4.1.9 Expression profile Bugs

    4.1.9 Data conversion problems

    4.1.10 Java Plugins Bugs

    

    


 
    

    4.1.1 Browser Applet Bugs
    
  
      
  
    1.  When using MAExplorer as a Web browser applet (this does not
apply if running as a stand-alone application - and we recommend
running it as a stand-alone application not as an applet), there are
problems with running MAExplorer on some of the Web browsers on some
operating systems. It generally works with Netscape 4.7 or later, on
Windows PCs (95/98/NT/2000/XP), and on SUN Unix and with Microsoft
Internet Explorer 5.0 on PCs. It may not work as well as an applet on
Macintoshes - even with Internet Explorer (although it works fine as a
stand-alone application on the Mac) and problems have been reported on
SGI systems because of browser problems. 
      

If you are experiencing Web browser problems using the MAExplorer
applet, you might check the discussion of  possible solutions.


    2.  When run as a Web browser applet, you should only click once since multiple clicks may cause
some browsers to hang. Note that when you click the first time, it
starts downloading the applet.  Clicking a second time may cause a
second request for the applet to be launched. This interferes with the
first request and so neither is started correctly.


    3. When the MAExplorer applet is being downloaded, some browsers
do not indicate to the user that it is being downloaded and so it
may appear that nothing is going on. Some browsers will not
indicate that it is downloading an applet. So 
please be patient.  When it starts, the main window will
popup and it continues to download data. Wait a while for it to load
the applet before giving up. For a 28Kb Internet connection, this
could take several minutes. When it is finished, the menu bar will
become active and it will display Ready - click on a
gene to query database.


    


 
    

    4.1.3 Downloading and Installer Bugs 
    

      


    1.  We have had reports of problems with MAExplorer installations
or running MAExplroer on MacOS 9 since we started distributing
MAExplorer with the new InstallAnywhere version 5.0.2. We suggest you
switch to MacOS-X or use a Windows, Linux or Solaris system until this
gets resolved.


    2.  Installer does not start or does not download the
data. Normally, when you attempt to download MAExplorer to your
computer, the InstallAnywhere software running in your Web browser
will detect which OS you are running and suggest the particular
download to use in:

           "Recommended version for your computer
      Download installer for ...your OS..."?

      
Occasionally, we have seen instances where you can not install
MAExplorer from within the Web browser. The solution is to explicitly
download the particular Platform for your OS in the Available
Installers list. And then to follow the instructions on running it.


    3.  If you have problems downloading this with Netscape 4.7x or
later, then try Internet Explorer 5.0. It could be a Mime/type
problem with your particular browser not setup correctly.


    4.  On Solaris, and possibly other Unix systems, you may have
problems with the stack limits. Do a "man limit" to read about the
command for your particular Unix shell. We have found that the
following seems to work. For CSH, add the following to your .cshrc
startup file. With versions 96.25 and later of MAExplorer, you may
adjust the memory size using the (Edit menu | Preferences |
Resize MAExplorer memory limits for the next time it is run) command.
This edits the startup file according to the new memory limits you 
specify.

         limit stacksize unlimited

      


    


     
    

    4.1.4 Computation speed and display Bugs
    

      


    1.  Because it does a lot of computation, MAExplorer runs best on
a fast computer with lots of memory. An optimal system should have at
least 128Mb of memory at least 500Mhz CPU speed. For doing
hierarchical clustering we recommend 256Mb of memory which will allow
you to cluster larger sets of genes without paging the operating
system. So you may run into problems with running out of memory on an
under-powered computer. To increase the usable memory size to more
than 256Mb (assuming your computer has it), then you may need to edit
the  MAExplorer.lax file
that resides where you installed MAExplorer on your computer to
increase the  heap and
stack maximum limit.


    2.  We have seen instances where the same system will run out of
memory with particular Web browsers (again - not a problem when
running stand-alone) Netscape 4.7 on a system with a lot of
memory. This does not seem to happen with Internet Explorer 5.0. This
is because Netscape4.7 on the Windows PC seems to be able to use only
about 5.5Mb before it runs out of memory. This is not the case with
Internet Explorer 5.0.


    3.  MAExplorer is difficult to use with a system with a small
screen because of the size and number of multiple graphic images and
plots.  We recommend a screen size of at least 1024x768. As
they say these days, more is better :-), generally.


    4.  For problems with MAExplorer and MacOS, you might also see our
 MacOS problems FAQ.



    5.  For problems with MAExplorer and Sun Solaris OS, you might
also see our  SUNOS
problems FAQ.


    6.  We have on rare occasions seen a data-related problem on MacOS
systems.  If the data was edited in such a way that the line
terminators were Return characters instead of Linefeeds or
Return-Linefeeds, the data may become corrupted and MAExplorer has
difficulty reading it. The solution is to replace the Return
characters with Linefeeds or Return-Linefeeds. One (painful) way this
might be done by exporting the data to a system such as a Windows PC;
read the data into Excel; save it as an Excel worksheet file format;
exit Excel; start Excel again on the new data files; save the data
files back into the original MAExplorer directories as tab-delimited
text files. MAExplorer should recognize Return as the line
terminator. This bug will be resolved in a future release.



    

 
    

    4.1.5 User state and login Status
    
   
      


    1.  User registration and the groupware access functionality are
not available yet. However, login is available for collaborator
databases on the MGAP MAExlorer Web server.


    2.  The commands to save and restore user states for a particular
user on the back-end server are not available yet. However, in
stand-alone mode you may save the state (including all gene and
condition sets, parameter conditions, condition sets, etc) on a local
computer by doing a (File menu | Databases | Save As DB). The
specialized groupware commands that will allow users to selectively
share their saved states with particular other users on a public
groupware server is not available yet.


    

     
    

    4.1.6 Data file names Bug
    
   
      


    1.  There is a potential problem with using long file names when
using MAExplorer with MacOS-8 or MacOS-9. Both of these operating
systems limit file names to 32 characters. This could be a problem if
importing data from another operating system (Windows, Unix, Linux,
etc) that does not have this restriction. The solution is to use short
file names. This should not be a problem with MacOS-X since it allows
file names with up to 256 characters.


    


     
    

    4.1.7 Gene Sets Bugs
    
  
      

    1. The full range of GeneClass subsets (Section 2.4.1) is not
available yet.  However, you may use the gene name guesser to find a
set of genes by wildcard into the Edited Gene List (e.g. "*ONCO*" to
find all oncogenes or proto-oncogenes, etc). These EGL sets may then
be saved as named gene sets and used in the data filter as "Filter by
'User Gene Set'". See the  Gene Class ontologies
using Gene Set operations (Section 2.4.1) that can achieve a
similar effect.


    


 
    

    4.1.8 Clustering Bugs
    
  
      

    1.  The hierarchical clustering method is buggy.  It currently
clusters genes - but not samples. The latter is under construction.
Centroid averaging is implemented and the arithmetic averaging is
under construction. Although it generates a clustergram plot, the
dendrogram drawing may not be drawn correctly using centroid
averaging. Depending on the number of samples, it you do a complete
hierarchical cluster for all genes and ESTs on a Netscape browser on a
Windows PC, the browser may run out of memory and nothing
appears. [When done in the stand-alone application, this is less of a
problem since there are no memory restrictions.]  You can do three
things at this point: 1) switch to Internet Explorer, or 2) decrease
the number of genes being clustered (e.g. just "All named genes") or
the CV filter. The third option is to disable the clustering
cache. This will be done automatically for you and it will continue
doing the clustering. You may turn off (or on) the Use cluster
distance matrix cache in the "Hierarchical Cluster Plots
submenu. It will perform the clustering, but will take MUCH longer
without the cache.


    2.  If you are working with very large data sets then if you start
a find similar genes or K-Means clustering with a very high threshold,
you can not change the threshold until it is done. In these
situations, try pre-setting the threshold distance, number of clusters
using the (Edit | Preferences | Adjust all Filter threshold
scrollers).  This will popup the state scroller window with all of the
thresholds.  You may also select the current gene prior to starting
clustering.


    

     
    

    4.1.9 Expression profile plots
    
   
      

    1.  The Expression Profile Overlay display is being improved to offer
better graphics and interaction. 


    2. We have observed some instances of the popup EP plot where data
does not seem to correspond to the data. This may be related to the
normalization method.


    3. When viewing an ordered list of EP plots associated with a
sorted list of genes (e.g. clustering genes similar to a seed gene),
if there are replicated genes in the clustered genes, they may not
show up in the scrollable list of EP plots.


    

     
    

    4.1.10 Data conversion problems
    


      


    1.  The Cvt2Mae data file converter application is currently
available for beta-testing on the a related MAExplorer Cvt2Mae Web page. It enables users
to convert their data files (academic one-of-a-kind as well as
commercial e.g. Affymetrix, Incyte, GenePix, Scanalyze, etc) to the
standard MAExplorer files as described in Appendix C and D.


    2.  If you are not able to convert your data using Cvt2Mae (which
is currently in early Beta testing - see the Cvt2Mae Web page for
current status), you can always manually convert the data using the
examples in Appendix C.


    3.  We have seen some problems running the converter with some
types of data on MacOS 8-9. The temporary solution is to convert your
data using another system such as Windows until we fix the problem.



    4.  If you are using the NCI-CIT mAdb system to package data for
MAExplorer, you must select all samples from multiple projects that
you want to compare with MAExplorer while packing the data in
mAdb. (See PDF document  "How to
download mAdb data For MAExplorer") Currently, you can not easily
merge the data after you have downloaded the files to your computer.
The solution is to use mAdb to prepackage all data you want to analyze
in a single .zip file. You can not currently merge data from
separately unzipped files (without manually editing the SamplesDB.txt
file).  The way you package data using mAdb is to: 1) select ALL of
the projects that contain samples that you want (on the mAdb "Top
Level Analysis Selection" web page); 2) further select the exact
subset of samples that you want in the "Array Selection" table on the
"mAdb: Database Retrieval" web page). NOTE: currently, you can only
select arrays using the same GAL file so they have compatible
geometries. This is on our list of things to change so you can mix and
match data from different chips.
 



    

 
    

    4.1.11 Java Plugins bugs
    

      

    1.  Java Plugins are in the process of being implemented and will
be made available to the user community to enable them to add their
own analysis methods to MAExplorer.


    


    

    


     

     

    4.2 Revision notes
    

This section lists the revision history and is useful for deciding
whether to upgrade to the most recent release.  You may want to check
for the latest the  current
"Stable release" available on the MAExplorer Web site. That may be
different than the  Stable
release listed in this copy of the Reference Manual. The "Beta
release" listed below the Stable release in the previous links is
experimental and may generally be downloaded as it has more
functionality. If you experience problems, you can just reinstall the
Stable version.

    


    
Note: An archive of some of the

stable older releases is available on the NCI/LECB Web site for a
limited period.

    

    


    

    


      


    • Version 0.96.34.01: (11-24-2003) Fixed problem
introduced in the previous release where you could not increase the
memory size invoked by the (Edit | Preferences | Resize MAExplorer
memory limits for the next time it is run) command.  To fix this bug
you must reinstall MAExplorer using the full download using the
Java installers. Also added ability to visualize the CV (coefficient
of variation of a set of replicate samples) using the new (Analysis |
Plots | Show microarray | Pseudocolor HP-EP 'list' CV (Coefficient of
Variation) [RB]) command. It computes the CV for the HP list. So use
the (Samples | Choose HP-X, HP-Y and HP-E samples) command to define
the HP-EP list of samples you want to investigate.


    • Version 0.96.33.07: (08-15-2003) The code implementing
the R eval paragigm has been refactord so that it is clearer and will
be easier to use for other projects. See the 
RtestPlugin documentation Section 6 for details.


    • Version 0.96.33.04: (07-31-2003) Fixed minor bugs in
MAExplorer and Reval data export for RLO methods.


    • Version 0.96.33.03: (07-23-2003) Fixed minor bugs in
current OCL state as well as adding support for future MAERlibr.zip
updates from the Web.


    • Version 0.96.33.01: (07-09-2003) Fixed bugs where the XY
scatterplot and other functions may not work with some data where
there were empty genes in the database. Changed the underlying control
mechanism to switch between the 4 clustering methods so it is more
robust and easier to use.


    • Version 0.96.32.10: (06-30-2003) added loessPlot() R
method to MAERlibr R library. This is used by RtestPlugin for
generated code.


    • Version 0.96.32.09: (06-26-2003) added new command
(Plugin menu | Save RLO reports in time-stamped Report/ folder
[CB]). put files generated by R from successive executions of the same
RLO into separate sub-folders in the Report/ folder with names
"RLOname-YYMMDD-HHMMSS/" to peep the data separate. This is
useful when you want to compare results from the same RLO method but
with different MAExplorer preprocessing. Also added new command
(Analysis menu | Filter | Filter by genes with non-zero intensity)
which can be used to omit genes with zero intensity values in any
samples of the HP-EP set of samples. This is useful when exporting
data to RLOs that may have problems by dividing by 0.0 etc.


    • Version 0.96.32.08: (06-17-2003) fixed bug in
RtestPlugin for Version 0.96.32.07 where it did not always use the
intput file mode correctly. Simplied the "Add Template" usage to
"Generate Template for 1st input file". Finished inplementing the
(Files | Update RLO methods from Web server) so that it updates the
MAERlibr package library file (lib/MAERlibr/R/MAERlibr) as well as the
.R and .rlo files for the RLO methods.


    • Version 0.96.32.07: (06-15-2003) fixed RtestPlugin
editor bug that did not always setup parameters for New RLO
correctly. There is still a but that may require you to set the input
files mode to the corresponding Demo BEFORE doing "Add Template".
This will be corrected shortly.


    • Version 0.96.32.06: (06-13-2003) fixed RtestPlugin editor
bug that did not always save the Routput files types. Also fixed
bug in demo R script Test.R.


    • Version 0.96.32.05: (06-12-2003) fixed code generators
and changed all R methods in the MAExplorer MAERlibr to have a "mae."
prefix to minimize conflict with other packages. Added alpha-level
support for XY overlay plots in MJAplot class of the API.


    • Version 0.96.32.04: (06-11-2003) fixed code generators
on Demos 1-15.  Demos 16-17 now broken with VSTACK error. Major code
generators cleanup in RtestPlugin. Demo 15 (with use Filter Action)
works but does not toggle the (Analysis | Filter | Filter by user gene
set) menu checkbox.  Also added code to R2MAE actions to better
support reimporting gene sets back into MAExplorer.


    • Version 0.96.32.03: (06-08-2003) Moved the MAExplorer-R
interface R library code from the .R files to an full R library that
now is downloaded when you do the full download/install of MAExplorer.
We will be adding an update for this MAERlibr library. The Web update
is not available currently available.


    • Version 0.96.32.01: (05-29-2003) Added (Files | Update
RLO methods from maexplorer.sourceforge.net) command to update RLO
methods from the server. RLO methods consist of pairs of R scripts and
RLO files.  Note: R must be installed on your computer to use these
methods. When MAExplorer is restarted these new RLO methods will be
available in the (Plugins | RLO methods) submenu.


    • Version 0.96.31.23: (05-27-2003) Fixed bug in reading
RLOs if they were manually edited with a Windows text editor that
replace line feeds with carriage returns.


    • Version 0.96.31.22: (05-22-2003) Minor changes in
RtestPlugin and R evaluation support code.


    • Version 0.96.31.21: (05-18-2003) Reorganized RtestPlugin
code and updated some of the code generators.


    • Version 0.96.31.20: (05-14-2003) Fixed bug where
MAExplorer could crash if R was not installed. This was also in the
RtestPlugin. Now, it detects that it is not there if you cancel the
prompt for specifying R is when it can't find it. This is noted in a
special .RbasePath file which contains "**NO_R_INSTALLED**".
This is then used to prevent you trying to access any R commands or
menus (i.e. keep you out of trouble).


    • Version 0.96.31.19: (05-14-2003) Fixed minor bugs
in RtestPlugin and support for horizontal and vertical stacked data.


    • Version 0.96.31.18: (05-08-2003) Fixed minor bugs
in RtestPlugin and support.


    • Version 0.96.31.17: (05-06-2003) Fixed prompt in
"Update Plugins".


    • Version 0.96.31.16: (05-05-2003) Changed the "Update
MAEPlugins from maexplorer.sourceforge.net" so that it will download
ALL of the plugins if you answer YES rather than one at a time.  More
cleanup for RtestPlugin Template paradigm.


    • Version 0.96.31.15: (05-05-2003) In RtestPlugin,
simplified R code to use new multi-line header data file paradigm and
started to add data model Template generation code. OCL demo does not
work with new paradigm.


    • Version 0.96.31.14: (04-30-2003) Fixed problem with R script
evaluation if the sample names had spaces in the name. Quoting these
fixed the problem.


    • Version 0.96.31.12: (04-28-2003) Fixed fatal error
problem introduced in 0.96.31.12 if start MAExplorer with no database.
This version fixes bugs in some of the RtestPlugin Demo R code
generators and data files.


    • Version 0.96.31.11: (04-25-2003) Fixed quote problem
with with executing R in some cases for RtestPlugin. Fixed Prev/Next
problem.


    • Version 0.96.31.10: (04-23-2003) Added "Save ALL Demo
RLOs" to RtestPlugin to generated demo RLOs for (Plugins | RLO
methods) menu next time MAExplorer is restarted. The demo R Code
generators are not validated yet.


    • Version 0.96.31.9: (04-22-2003) Fixed RtestPlugin
problem with sample set names when generate RLOs for XY sets and OCL
condition sample sets. Changed the export data formats so easier to
use in R.  The demo R Code generators are not validated yet.


    • Version 0.96.31.8: (04-21-2003) Fixed problem with
RtestPlugin that sometimes showed up when installed in "C:/Program
Files/" on a Windows system (needed to quote the path). It now works
on windows. Note: RtestPlugin Demos 10-13 and Demo 16 are not working
correctly. The demo R Code generators are not validated yet.


    • Version 0.96.31.7: (04-18-2003) Gene Set actions now
work for RLOs. The demo R Code generators are not validated yet.


    • Version 0.96.31.5: (04-13-2003) Extended the RLO
definition and support code. Extended RtestPlugin to access this.
The demo R Code generators are not validated yet.


    • Version 0.96.31.4: (04-11-2003) Added "RLO methods to
Plugins menu. This uses RLO files generated by the
RtestPlugin. Additional bug fixes and better error reporting of R
evaluation errors. The demo R Code generators are not validated yet.


    • Version 0.96.31.2: (04-07-2003) Extended RtestPlugin and
corresponding changes in MJA API. The demo R Code generators are not
validated yet.


    • Version 0.96.31.1: (04-03-2003) Cleaned up gene 
filter processing model.


    • Version 0.96.30.10: (03-29-2003) Better support for
RtestPlugin. 


    • Version 0.96.30.8: (03-28-2003) Modified the MJAReval,
MJAgeneList, MJAfilter API class methods to support the RtestPlugin.
RtestPlugin now supports "New RLO" at alpha level.


    • Version 0.96.30.6: (03-25-2003) The RtestPlugin editor
is working (alpha level) for demonstration purposes-only at this time.


    • Version 0.96.30.5: (03-18-2003) The RtestPlugin is alpha
level. Modified the MJAReval class methods to support this. This is
still alpha-level, and may change.


    • Version 0.96.30.4: (03-17-2003) Added new functionality
to evaluate MAExplorer data using the R system. There is a new MJAReval
to access methods to do this. Using MJAReval, one can write MAEPlugins
to process MAExplorer data in R and to bring back the results into
MAExplorer. This is still alpha-level, and may change.


    • Version 0.96.30.3: (03-04-2003) Fixed radio-button 
usage of NormalizationPlugin so that only one normalization (either
built-in or plugin) is active at a time. The MJA API was enhanced
to add this functionality.


    • Version 0.96.30.2: (03-02-2003) Major ehnancements of
the NormalizationPlugin paradigm in the MJA library to support a wide
variety of normalization methods - both global and local. This
includes support code throughout MAExplorer to support it and to make
it easier to debug. Although it works, the NormalizationPlugin is
still Alpha-level and will be documented fully when it enters
Beta-level code.


    • Version 0.96.29.3: (02-25-2003) Rebuilt installers
(Windows, Linux, Solaris) with Java Virtual Machine (JDK) 1.4.1_01
that has improved performance. You must do a download and reinstall --
not just an update from (File menu | Update MAExplorer from
maexplorer.sourceforge.net).


    • Version 0.96.29.3: (02-23-2003) Added more support for
FilterPlugins.  The ExampleXYdataFilterPlugin example now works. It is
meant to be a model for more sophisticated plugins. [TODO] it needs
parameters GUI popdown.



    • Version 0.96.29.2: (02-21-2003) Fixed many problems
with MJA classes where some methods which should have been public
were not so MAEPlugins could not access them. This has been
corrected. The FtestNconditionsFilterPlugin is now working.


    • Version 0.96.29.1: (02-19-2003) Added popup alert
message window for bettering informing users of conditions that
prevent them from doing the operation they requrested. They must press
the Close button to pop-down the message, although they can do a
SaveAs to a file to save the message. For complex problems, some of
the messages may suggest what they need to do to correct the problem.


    • Version 0.96.28.3: (02-18-2003) Added 2 new filters:
"Filter by HP-X,HP-Y 'sets' t-Test [p-Value] slider [RB]" and "Filter
by current Ordered Cond. List (OCL) F-Test [p-Value] slider [RB]".
The p-Value related Filter tests were changed to radio buttons [RB] so
that only one would be operative at a time. If another p-Value
dependent test is on, it will be shut off it you select a different
p-Value dependent test. Added new support methods in the MJA API in
classes MJAsampleList, MJAstatistics, MJAfilter, and
MJAmath. Implemented the "List OCL" and "List All" OCLs in the OCL
chooser. Improved the GUI legends in the Condition Chooser and the
Ordered Condition Chooser.


    • Version 0.96.28.1: (02-15-2003) fixed default R2 and
CR2.  Modified State Scrollers paradigm so that it uses slider usage
counters (instead of flags) so that MAEPlugins may share sliders
(e.g., p-Value, cluster distance, etc).


    • Version 0.96.27.1: (02-12-2003) added named Ordered 
Condition List (OCL) editor as a builting method.


    • Version 0.96.26.1: (02-08-2003) added methods to
MJAsampleList to get either normalized Filtered data or normalized
complete set of genes data.  Fixed bug in CompositeDatabase.


    • Version 0.96.25.5: (01-28-2003) Fixed bug in "Remove
Parameter" in the condition chooser. Added new methods MJAcondition,
added method in MJAsampleList to get all raw sample data.


    • Version 0.96.25.4: (01-13-2003) Fixed bug in "Remove
Parameter" in the condition chooser.


    • Version 0.96.25.3: (01-02-2003) Fixed bug in "Remove
Condition" where it previously deleted the wrong condition list.


    • Version 0.96.25.2: (12-14-2002) Added "Remove Condition"
to (Sample menu | Choose named condition lists of samples) as well as
fixed several bugs related to the condition chooser. Also added the
same command to the (Edit menu | Sets of conditions | Choose named
condition lists of samples) menu since it is a condition editing
function.


    • Version 0.96.25.1: (12-11-2002) Made some of the tests
for names case-independent for smoother operation.


    • Version 0.96.25: (12-06-2002) Added new a new command
(Sample menu | Choose named condition lists of samples) lets you
define or edit new named lists of hybridized samples.


    • Version 0.96.24.12: (12-04-2002) Added support for
RefSeqID and alternate spellings of other genomic
identifiers. Modified MAJcondition class to support ConditionChooser.


    • Version 0.96.24.11: (12-02-2002) Added support for
RefSeqID and alternate spellings of other genomic
identifiers. Modified MAJcondition class to support ConditionChooser.


    • Version 0.96.24.10: (11-30-2002) Updated the default
URLs used for GenBank, UniGene, LocusLink, and added OMIM ID support.


    • Cvt2Mae Version 0.73 (11-26-2002) Added ability
specify multiple chips/sample (e.g. Affy U133A and U133B) for the
MAS5.0 data.  It will merge the data into one sample during the
conversion.


    • Version 0.96.24.8: (11-20-2002) The ConditionChooserPlugin
is now working (alpha) and is included in the distribution. Additional
changes were made to MAExplorer to support it.


    • Version 0.96.24.7: (11-18-2002) Fixed Scrollbar initialization
problem for the non-linear scrollers (p-Values, CV value, etc.)


    • Version 0.96.24.6: (11-15-2002) Fixed Q&A error in the
command "Resize MAExplorer memory limits for the next time it is run"
is in the (Edit | Preferences) menu. Added or debugged methods to
MJAproperty and extended MJAcondition MJA API.


    • Version 0.96.24.5: (11-07-2002) Add command to resize
the memory limits of MAExplorer. The command "Resize MAExplorer memory
limits for the next time it is run" is in the (Edit | Preferences)
menu. After the command is run, you must exit and restart MAExplorer
for the new memory limits to take affect. It changes the memory limits
in the MAExplorer.lax startup file.


    • Cvt2Mae Version 0.72.4 (11-07-2002) Fixed bugs in
"Define Gipo Fields" where it would be be available under certain
conditions.  Also fixed another bug introduced with the addition of
the MAS5.0 data.


    • Cvt2Mae Version 0.72.3 (11-05-2002) Increase buffer
sizes to fix problem for very large input files with large gene
descriptions.


    • Version 0.96.24.4: (11-03-2002) Fixed bug in non-linear
sliders in State Scrollers. This had affected the values for the
p-value and CV value.  Extended functionality of popup State Scrollers
sliders window. You can now toggle between all sliders, even those not
active, and only those that are active.


    • Cvt2Mae Version 0.72.2 (11-02-2002) Fixed bugs in
Affymetrix MAS5.0 where the "Define Quant Fields" were not being mapped
automatically and separate files were not converting correctly.


    • Version 0.96.24.2: (10-24-2002) Added (File | Update
Plugins) operation. It will prompt you on whether you want to update
each default plugin one by one.


    • Cvt2Mae Version 0.71.2 (10-23-2002) Moved the name
of the available Cvt2Mae.jar version number to the popup dialog
query when doing the "Update Cvt2Mae" operation.


    • Version 0.96.24: (10-22-2002) Extended the number of
grid groups from 52 [A-Z,a-z] to 152 [A-Z,a-z,1-100]. Moved the name
of the available MAExplorer.jar version number to the popup dialog
query when doing a (File | Update MAExplorer) operation.


    • Version 0.96.23: (10-21-2002) Extended the MJAgene.java
API class so MJAPlugins can set gene annotations and properties
from the plugin using external data.


    • Version 0.96.22: (10-18-2002) Extended the (File |
Update MAExplorer) command so that it reads the version number of the
MAExplorer.jar file on the maexplorer.sourceforge.net server and
displays it prior to requesting the user to finish requesting
the update. This allows users to see if they want to do the update.


    • Cvt2Mae Version 0.71.1 (10-16-2002) Added a "Update
Cvt2Mae" button.  This will (1) backup the current Cvt2Mae.jar file as
Cvt2Mae.jar.bkup; (2) copy the latest Cvt2Mae.jar file from the
maexplorer.sourceforge.net Web site and replace your Cvt2Mae.jar file
in your installation directory.  Then when you restart MAExplorer, it
will use the new version of the program. The much more time consuming
alternative is to do an entire download and reinstallation from the
Web site.


    • Version 0.96.21: (10-15-2002) fixed comments for (File |
Update MAExplorer) to give the actual server it will use. Fixed
default PseudoArray Image to use when have single Cy3/Cy5 sample to
use (Red-Yellow-Green) display. Cvt2Mae Version 0.70.7
(10-15-2002) has a major re-indentation of the Java source code to
make it easier to read using the convention describe in the previous
revision.


    • Version 0.96.20: (10-14-2002) We added a "Define GEO
Platform ID" command for arrays that were submitted to NCBI's GEO
(Gene Expression Omnibus) for use with future MAEPlugins. You can now
use the new "Update MAExplorer" command in the File menu.  This will
(1) backup the current MAExplorer.jar file as MAExplorer.jar.bkup; (2)
copy the latest MAExplorer.jar file from the
maexplorer.sourceforge.net Web site and replace your MAExplorer.jar
file in your installation directory.  Then when you restart
MAExplorer, it will use the new version of the program. The much more
time consuming alternative is to do an entire download and
reinstallation from the Web site. This version also has a major
re-indentation of the Java source code to make it easier to read.  It
uses the automatic java indentation engine in Fote 4.0 (2 spaces/tab,
convert tabs/space, space before '{', no space before '(').


    • Cvt2Mae Version 0.70.6 (10-11-2002) Fixed bug in
Configuration file generator.


    • Version 0.96.18: (10-09-2002) Changed popup HP chooser
initial window sizing so it works more consistently across platforms.


    • Version 0.96.17: (10-04-2002) Fixed bug with DetValue
slider (the test was reversed). Also increased the dynamic range and
precision of value indicated. The default is to leave the Detection
Value filter off. Changed the default PseudoArray Image if there is
one Cy3/Cy5 sample from the intensity display to the sum of cy5 (red)
and Cy3(green) channels.


    • Cvt2Mae Version 0.70.5 (10-03-2002) Fixed bug in
Affy MAS4 converter when selected "Generate genomic IDs from
Descriptiopn". Also fixed bug in Quant Field assignments so it now
shows the "DetValue" field when it was selected by the DetValue
checkbox in the Genomic Identifiers Wizard.


    • Version 0.96.16: (9-30-2002) Fixed bug with positive data
filter whereby it defaulted to being on. This was changed so that you
need to explicitly enable it in the Filter menu.


    • Cvt2Mae Version 0.70.4 (09-27-2002) Added GEO
Platform ID support.


    • Cvt2Mae Version 0.70.3 (09-23-2002) fixing the
ArrayLayout Affy MAS5 tmp file converter.  It now appears to generate
the temp file correctly and read it correctly.  Need to verify the
code generators for the GIPO.txt file. There was a problem with
embedded double quotes and the MAS5 ALO was not correct. Also had to
add the additional fields to the FieldMap when the tmp file is
extended.


    • Version 0.96.15: (9-19-2002) Added a new "Filter by Spot
Detection Value" data filter. This can be used with the new Affymetrix
MAS5.0 "Detection p-value" mapped through the "DetValue" or "CorrCoef"
field in the .quant file.  The next version of Cvt2Mae after 0.70.2
will output the "DetValue" field.


    • Cvt2Mae Version 0.70.2: (09-18-2002) Added automatic
delete of old default Affymetrix*.alo files when Cvt2Mae starts
up. This was required since we changed the names to reflect the
MAS-4.0 and MAS-5.0 versions of the analysis software.  Without this,
you would have multiple names for the same files in the Array Layout
chip list.


    • Cvt2Mae Version 0.70.1: (09-17-2002) Fixed bugs 
introduced in 0.70. Added remap code to better handle MAS5.0 data. 
 

    • Cvt2Mae Version 0.70: (09-15-2002) Added Affymetrix
MAS5 array layout so it will map (Signal, Detection, Detection
p-value) to (RawIntensity, QualCheck, CorrCoef) Quant data
fields. Fixed bug in "SwissProtID" data parser.


    • Version 0.96.14: (9-14-2002) Fixed incorrect base URL in
"Help" pull-down menu. It had pointed to the old MAExplorer Web site
not the the new http://maexplorer.sourceforge.net/MAExplorer/. The same
documents were always visible from the Web site, but this makes it
more convenient.


    • Cvt2Mae Version 0.68: (08-30-2002) Fixed bugs in
Edit Layout where it was not always saving the changes you make to the
state.  Pressing Cancel in the Edit Layout restores the initial state
before starting the edit. Pressing Back saves the state of the current
panel you are working - previously only Next or Finish did that.


    • Version 0.96.12: Fixed bug introduced in version 0.96.11
such that it would pop up the dialog query when you did a "Open Disk DB"
command.


    • Version 0.96.11: Extended MJAeval methods so can more
easily invoke a new .mae startup file via a client-server MAEPlugin as
well as the invokeCommand method. Modified internal methods and
structures to support this.


    • Cvt2Mae Version 0.67: (08-15-2002) Fixed bug where
the generated pseudoarray image was overiding the user specified
(grid,row,column) geometry.


    • Version 0.96.10: Fixed bug in pseudoarray image whereby
the current gene icon (yellow circle) sometimes was not reset properly
for a database with X,Y coordinate data if the current gene was selected
and you switched the current sample.


    • Version 0.96.08: Fixed bug in pseudoarray image whereby
the current gene icon (yellow circle) sometimes was not reset properly
if you toggled the mouseover checkbox, selected a different sample
using the sample list in the pseudoarray image, or scrolled the
pseudoarray.


    • Version 0.96.07: Fixed bug in font size updates for the
pseudoarray image and scatter plot. Added methods to MJAproperty to
get font size and font familiy.


    • Version 0.96.06: Modified data filter property bits for
Gene, MJAgene, and Filter to make them more consistent when writing
MAEPlugins.


    • Version 0.96.05: Fixed bug in data filter that caused
problems with FilterPlugin's.


    • Version 0.96.04: Fixed bug when run database with no
startup database, it does not refresh spot lists in the PseudoArray
image.


    • Version 0.96.02: Fixed bug when run database with no
startup database and no default MGAP demo database. It now lets you
search the disk to find a .mae startup file instead of aborting with a
Dryrot error. Fixed bug with chooser where it sometimes fails to let
you access the last element of a list. The NCI/LECB site and the
SourceForge site now both have the same full installers with JDKs (except
for AIX and HPUX) and with the MGAP database.

      
A Cvt2Mae Version 0.66: bug has been fixed that makes it easier
to automatically find the first row of spot data when scanning the
users data input data file. Previously, the user might have to
manually enter that starting data arow number in the Edit Layout
wizard.


    • Version 0.96.01: Initial release on SourceForge Logo. Note:
The NCI/LECB site will have the same installers., but with JVMs if you
want to download a version with the JVM for your computer. The
SourceForce does not have the JVMs because of space limitations.


      


    • Version 0.95.20: Inserted Open Source License into all
code.


    • Version 0.95.19: Changed default for (File | Save file
DB) and (File | SaveAs file DB) so that it saves all samples in the
new .mae startup file - not just the samples in HP-X 'set', HP-Y 'set
or in HP-E 'list'.  Made the three status lines in the main window
non-editable.



    •  Version 0.95.18: Maintenance release. The Open Java API
has been changed in the mjaGeneList, mjaGene, mjaEval classes.


    •  Version 0.95.17: Maintenance release. The Open Java API
has been updated and now includes working updateCurGene(),
updateFilter(), updateSlider(), updateLabel(), and close() methods as
well as new methods in the MJAgeneList API class.  The stable release
is now the version with the MaeJavaAPI active. The previous stable
release 0.95.04 did not have the API.


    •  Version 0.95.16: Maintenance release.


    •  Version 0.95.15: Maintenance release to fixed
FilterPlugins so the FilterTestPlugin example is working. Fixed bug
recently introduced that prevented selecting spot in pseudoarray
image.


    •  Version 0.95.14: Fixed bug recently introduced that
prevented menu commands from working correctly.


    •  Version 0.95.12: Maintenance release. Changes to help
support MAEPlugins and additional functions in the Open Java API.
Fixed Quant input data parser bug so it  now handles strictly
lower-case letters for (Grid,Row,Column) data.


    •  Version 0.95.11: Maintenance release. Changes to help
support MAEPlugins and additional functions in the Open Java API.


    •  Version 0.95.10: Maintenance release changes to help
support MAEPlugins and additional functions in the Open Java API. Menu
Plugin command is enabled to load MAEPlugins built using the Open Java
API (under construction).


    •  Version 0.95.08: Maintenance release to help
support MAEPlugins and additional functions in the Open Java API. Menu
Plugin command is enabled to load MAEPlugins built using the Open Java
API (under construction). Added Plugins/ folder to distribution that
is installed where you install MAExplorer. It contains several
alpha-level JAR file plugins that work with the (Plugins | Load
Plugin) command. We will be releasing the source code soon as the Open
Java API alpha release in put on the MAEPlugins Web site.


    •  Version 0.95.05: Maintenance release. Changes to help
support MAEPlugins and additional functions in the Open Java API. Menu
Plugin command is enabled to load MAEPlugins built using the Open Java
API (under construction). Added (Plugins | Test Plugin Code) to
invoke code for a plugin a debugging context (maintenance).


    •  Version 0.95.04: Same as Version 0.95.03 optimized. It
was compiled with the optimizing Java compile without debugging symbol
tables. All Plugin support codes was removed. This results in a
MAExplorer.jar file of 449Kb vs 599Kb for. Since the MGAP Web site Applet
version can not use Plugins, this version is optimized for Applets
and is to preferred over the Beta 0.95.03 version for now.


    •  Version 0.95.03: Maintenance release. Changes to help
support MAEPlugins. Menu Plugin command is enabled, but will only load
MAEPlugins built using the Open Java API (under construction).


    •  Version 0.95.01: Increased the number of spots/array
that may be used from 16K to unlimited. Maintenance release. Changes
to help support MAEPlugins.


    •  Version 0.94.15: Fixed bug where occasionally, it would
not load additional samples from the (Samples | Set Samples from
Lists | ...) commands.


    •  Version 0.94.14: Maintenance release. Changes to help
support MAEPlugins. This release fixes a menu-related bug that occured
if you used (File |Open File DB) to switch to a different database or
to start the initial database rather than starting directly from the
.mae startup file.  The problem was that some of the sub menus
generated might not correspond to the type of database.  For example,
if the previous database was an intensity database with duplicates F1
and F2, switching to a Cy3/Cy5 database might have some of the menus
still appear as (F1/F2).


    •  Version 0.94.12: Maintenance release. Changed placement
of magenta circle marker so it is more visible in ClusterGram when
selecting genes in the dendrogram/ClusterGram for the EGL by clicking
on a gene with the Control key pressed.


    •  Version 0.94.11: Fixed fatal error introduced ni
0.94.10 that occurs if you start the database with no data. Version
0.94.10 did work if you started it with a particular .mae startup file.


    •  Version 0.94.10: Maintenance release. The "Filter by
positive Quant data" toggle checkbox in the (Analysis | Filter menu)
is enabled for all databases - not just those requesting it. If the
database has 2 channels (F1, F2) or (Cy3,Cy5) each channel is
checked. If the background correction is enabled, the background
corrected values are tested to see if any of them are negative.
Changed the labels in the scatter plot so that instead of displaying
the current gene values as intensX= and intensY= or
intens'X= and intens'Y= , it now displays it as
X= and Y= or X'= and Y'=. The '
indicates that background correction is active. A new (Analysis | Plot
| Show Microarray | "Scale pseudoarray image by 1/100 to zoom
low-range values) command has been added to rescale intensity and
(Cy3+Cy5) and (HP-X + HY-Y) (Red-Yellow-Green) plots to so low values
are easier to visualize.



    •  Version 0.94.09: Maintenance and Reference Manual
update. Added copy of "Show 'Edited Gene List'" to Edit EGL submenu so
easier to find.


    •  Version 0.94.08: Changed button layouts on popup plots
so it is easier to resize them to remove the right part of the plot
if you want to conserve screen space.


    •  Version 0.94.07: Fixed bug recently introduced in the
histogram plots which prevent them from working. Fixed another
histogram plot bug where the frequency count for "><"
thresholding was reported incorrectly.


    •  Version 0.94.06: Added the saving of the current values
of all of the State Threshold scrollers when the (Edit menu |
Preferences | Adjust all Filter threshold scrollers) in the
message logging area when the State Thresholds popup window is closed.
The Cvt2Mae (Version 0.60) has been extended to now handle Scanalyze
data files as well as supporing a separate GIPO input data file.


    •  Version 0.94.05: Added logging of MAExplorer messages
to buttons in popup plots and reports. Fixed bug where old fixed
gene sets for empty wells were not always restored correctly fro the
previous state.


    •  Version 0.94.04: Added logging of MAExplorer messages
and command history in popup windows in the View Menu as "Show log
messages" that occur during a session and "Show log of command
history" respectively. The windows may be saved in log files. Added
additional error detection of bad (field,grid,row,column) data when
reading the GIPO and Quant files.


    •  Version 0.94.03: Maintenance release for improving
MAEPlugin support. 


    •  Version 0.94.02: Minor bug fixes related to the
renaming of HybProbe to Samples menus. Also, extensive
update of reference manual in both text and figures to showing many of
the renamed menus so it is synchronized with the program.


    •  
       
 Version 0.94.01:
Major version release.  
 

       
Renamed all previous references in the program to "hybridization probe" or
"hybridization sample probe" to the new term "hybridized sample" for
clarity. Changed many "HybProbe Menu" to "Samples Menu" and also many
menu selections as well as plot and report labels to reflect this
change. We are in the process of updating the manual computer screen
figures and PDF slide presentations so that "hybridization sample
probe" is shown as "hybridized sample". Also fixed other minor
problems including fixing the inverted color scale for the
"Pseudocolor Red(Cy5)-Yellow-Green(Cy3) Cy5+Cy3 ratio or Zdiff"
command.


    •  Version 0.93.03: Minor reorganization of (Plots |
Clusters) submenus. This is the last release using the "HybProbe"
menu.


    •  Version 0.93.02: Beta-test of JDK-1.3 MAExplorer.jar
file and new InstallAnywhere 4.5 installer. This adds JDK-1.3
installers for MacOS-X, IBM AIX, HP-UX, Linux. However, MacOS 8.1-9.x
use the MRJ-2.2.5 JDK1.1.8 library. It also fixes a problem recently
found running MAExplorer on some Windows-NT systems.


    •  

      
 
Version 0.93.01: Major version release.


      
Moved "Cluster Plots" submenu of the "Plots" submenu
up one level in the "Analysis" menu.


    •  Version 0.92.24: Maintenance and added more Plugins support. Top
level Plugins menu appears, but is disabled in production version.


    •  Version 0.92.23: Maintenance and changed names of some commands
and some messages. Major edit of Reference Manual including expanded
table of contents.


    •  
      
 
Version 0.92.22: Last Stable Release. 


      
Major version release. Optimized colors in grayscale display for
"Pseudocolor (HP-X,HP-Y) 'sets' p-value". Fixed error and optimized
t-test computation.


    •  Version 0.92.21: Changed grayscale display for "Pseudocolor
(HP-X,HP-Y) 'sets' p-value" to display it as a color spectrum. Changed
data displayed so p-Value is in the front of the list when you
click on a spot etc. Added tests to disallow this display unless
the data supports it.


    •  Version 0.92.20: Beta testing new pseudoarray image display
"Pseudocolor (HP-X,HP-Y) 'sets' p-value" that displays a gray value
proportional to the p-Value in a t-Test of the HP-X 'set' vs HP-Y
'set'.


    •  Version 0.92.18: Improved EP plot labels so it no longer
prints the "MID:" prefix. Improved spot features list so it no longer
prints the "plate[null,null,null]" if the plate coordinates are not
present in the GIPO file.


    •  Version 0.92.17: Fixed a bug which incorrectly computed the
max and minimum ratio Cy3/Cy5 ratio values if the data allows negative
intensity on these channels after background subtraction. This is
required to support the GenePix array data format converter in the new
version of Cvt2Mae (11-24-2001).


    •  Version 0.92.15: A new (Edit | Preferences) command Adjust
all Filter threshold scrollers - popup the state scroller window
with all of the thresholds. This is useful when you want to adjust
thresholds before you enable data Filtering or clustering.  A
bug was fixed where the state of the previous clustering method was
not always cleared if you aborted clustering by clicking on the delete
window button (eg. in Windows or the Mac). Click on a row with the
Control (Shift) key pressed in the ClusterGram for hierarchical
clustering will add(remove) that gene to(from) the Edited Gene
List. If the (View | Show 'Edited Gene List') is enabled, then it will
draw a magenta '*' before the gene name to indicate that you have
selected it. This lets you select a particular subset of the genes
from the hierarchical cluster.  The  Cvt2Mae data converter now has an
alpha-release to let you convert non-standard data using the
<User-defined> data option.


    •  Version 0.92.14: The name of sample names and sample file
names IDs has been made more flexible. If the "Database_File" is
longer than 32 characters, there may be problems reading that file
with MacOS-8/9. Therefore, you can use the "DatabaseFileID" to specify
the "Database_File" (in which case they would be the same). Then, the
labels used for the samples are the "Sample_ID" fields. This is
upwards compatible with the previous method which used the
"Database_File" for both the file name and the label name. The Reports
of Highest (Lowest) Cy3/Cy5 data for a single sample were not easily
accessible and this has been fixed. Alternate names for automatic
detection of ESTs, ESTs similar to named genes were expanded and also
includes the "EmptyWell" gene set (i.e. spots with no genes) (see  Automatic Gene Class naming
based on Gene Name in Appendix C Table C.4.1). A clustering option
was added, Use median instead of mean for K-means clustering,
of mean when computing K-means clustering (Bickel, 2001)).


    •  Version 0.92.13: Maintenance release. Changed per-sample Good
Spot filter algorithm and the CV filter algorithm. Added options to
filter by either "HP-X 'set'" or "HP-Y 'set'". Algorithm now uses
filters "HP-X or HP-Y" and "HP-X or HP-Y 'sets'" rather than the
previous "HP-X and HP-Y" and "HP-X and HP-Y 'sets'". Adding some of
the missing state that was not being saved during a "SaveAs DB"
operation when creating a checking .mae startup file.


    •  Version 0.92.12: Maintenance release for Plugins. 


    •  Version 0.92.11: Maintainence release. Increased width of 
scrollable sample names selection windows so it is easier to see
the details on long sample names.


    •  Version 0.92.10: Maintenance release. Due to a problem that
some users are experiencing with the Java Installer, we are reverting
to the previous version 4.01 (that does not support Mac-x).  We will
resolve these problems as soon as possible. Also fixed a problem with
the Pseudocolor Red(Cy5)-Yellow-Green(Cy3) Cy3/Cy5 ratio or
Zdiff which is now resolved.


    •  Version 0.92.09: Maintenance release. Distribution of Java
application with InstallAnywhere 4.5 (previous was version
4.0.1). This now supports MacOS-X, other Unix systems and may fix some
other installation problems.


    •  Version 0.92.08: Fixed bug in Edit use (Cy5/Cy3) else
(Cy3/Cy5) for each HP command in HybProbe menu that changes were
not saved in the state. Fixed bug introduced in HP Cy3 vs Cy5
intensity scatter plot.


    •  Version 0.92.07: Added display of Cy3 and Cy5 channel
intensity when click on gene in Pseudocolor Red-Yellow-Green
Cy3/Cy5 ratio or Zdiff mode. Also added additional support
for plugin installation from Configuration DB  and .mae startup files.


    •  Version 0.92.06: The Pseudocolor Red-Yellow-Green Cy3/Cy5
ratio or Zdiff - display the Cy3 and Cy5 microarrays as a
pseudocolor image as the sum of Cy3 (Red) and Cy5 (Green).


    •  Version 0.92.04: Internal changes. Also fixed bug in reading
Affymetrix data converted with Cvt2Mae in the case where there is
no genomic identifier and the "Location" is used as the genomic
identifier. 


    •  Version 0.91.03: Made configuration database "swapRowsColumns"
default to FALSE. This should probably never need to be true except
for the initial MGAP database. If this flag was not set to false in
previous configuration DB files, it would swap rows and columns in the
pseudoarray image display.


    •  Version 0.91.02: maintenance revision.


    •  

      
 
Version 0.91.01: Major version release.


      
This corrects a few minor bugs including crashing when starting up 
an empty database (that bug was introduced sometime in the last month).
It is the first release with some reorganized code. 



    •  Version 0.90.08: Fixed bug in "Filter by 'Good Spot data'"
where the initial "Check spots for Good Spot mode" was not properly
initialized.


    •  Version 0.90.07: Additional fixes in QualCheck mapping now
handles the three types: Alphabetic codes, MAExplorer integer property
codes, continuous floating point monotonic quality value. If the
latter is used, then a "Spot Quality threshold" slider will popup when
it is invoked. The MAExplorer Plugins
 Web page is also available at thei Web site and will track progress
with the development of the MAExplorer Plugins Open Java API for
investigators adding their own analysis methods.


    •  Version 0.90.06: Fixed bugs in Genomic database popups. It was
ignoring the request to popup a web page for particular genomic IDs.


    •  Version 0.90.05: Added QualCheck mappings of Affymetrix "Abs
Call" of "P" (or "G" or "T") to Good Spot, "A" to Bad Spot, and "M"
(or "B" or "F") to Marginal Spot. This allows the "Filter by 'Good
Spot data'" to handle this type of data.


    •  Version 0.90.04: Added experimental GenomicMenu/URL
configuation options for future use.


    •  Version 0.90.03: Added QualCheck data filter for "Filter by
'Good Spot data'". This will be active if the QualCheck data is
available in the .quant files.


    •  Version 0.90.02: Fix bug in some of the State scroller
controls when adjusting a scroller latches into a fixed state that can
only be changed by restarting MAExplorer. Added LocusLink popup Web
pages from LocusID (if it exists) or LocusLink from GenBank
identifiers.


    •  

      
 
Version 0.90.01: Major version release.


      
(very Beta) Changing convention so use "MasterID" as the master gene
index. This makes it more flexible than when used the CloneID as the
master gene index. Added LocusLink.


    •  Version 0.89.41: Added new scatter plots for comparing Cy3/Cy5
ratio data to compare either Cy3 or Cy5 of HP-X against Cy3 or Cy5 for
HP-Y. Added additional change limit constraints "Compare channels
meeting range" to "Filter by spot intensity [SI1:SI2] sliders". This
allows partial filtering by spot intensity.


    •  Version 0.89.40: Some of the State Scrollers were too
sensitive at low values. Therefore, we set them to respond non-linearly
with a more precise vernier at the low end.


    •  Version 0.89.39: Fixed bug t-Test if using multiple samples
with arrays with 1 Field.


    •  Version 0.89.38: Fixed bug in Filter that allows the user to
use either "positive only" or "positive or negative" intensity data
(used with Affymetrix data).


    •  Version 0.89.37: Increased the dynamic range of the scrollers
for p-value, cluster distance, [Z1:Z2], |Diff XY|. Fixed labels in the
Cy3 vs Cy5 plot so they are updated when the current sample changes.


    •  Version 0.89.36: Fixed error in "Inside Range" for "Filter by
Ratio ..." and "Filter by Cy3/Cy5 Ratio ...". Fixed HP-X,Y,E labels in
the popup HybProbe menu "Choose HP-X,Y,E ..." command. Also added a
new button "<< that removes all entries from the Selected list
to the Remainder list. Changed the way the Cy3 vs Cy5 (for ratio data)
or F1 vs F2 (if you are using intensity data where all spots are
replicated) scatter plots work - the HP-X vs HP-Y scatter plots are
not changed.  The data Filter still works for all of the
plots. Changed the name of the "NPN clustering" method in the menus
and reports to "K-means clustering" since that is essentially what it 
is.


    •  Version 0.89.35: Improved HP sample ID reporting in various
lists, lists, etc. Added HybProbe menu command "Edit use (Cy5/Cy3)
else (Cy3/Cy5) for each HP" for use with ratio data. This selectively
swaps (Cy3,Cy5) data entries so may use (carefully!) dye-swap data 
for replicates. Added UP/DOWN buttons in the HP chooser to make it
easier to adjust the order of the HP-E list.


    •  Version 0.89.34: Changed the pseudoarray image generation paradigm
so that it uses a fixed size spot and spacing and makes the pseudoarray
image size dependent on the number of grids, rows, columns and fields. This
results in a more consistent interface.


    •  Version 0.89.33: Added "Filter by Cy3/Cy5 HP-X ratio or Zdiff
sliders" to the Filter menu. This is useful for filtering data from a
single sample.


    •  Version 0.89.31: Added Rename command for Gene Sets and
Condition lists. Added "SaveAs GeneSets" to K-means clustering. This saves
all of the clusters as named Gene sets ("Cluster #1", "Cluster #2",
etc.);


    •  Version 0.89.30: Fixed problem with popup Web browser for
Macintoshes.


    •  Version 0.89.29: Optimized sorting on startup so it starts
much faster when working with large arrays.


    •  Version 0.89.28: Gene set and Condition set operations now let
you select the sets by a pull-down choice menu as well as by typing in names
of sets.  Additional error checking added for input data files.


    •  Version 0.89.26: Enhanced configuration file parser and sample
name menu default; better error checking on input files to help catch
some errors in manually edited user data files.


    •  Version 0.89.25: Fixed configuration parser errors when
reading non-standard data.


    •  Version 0.89.24a: Fixed data for MAExplorer MGAP demo.


    •  Version 0.89.24: Fixed bugs in popup browser to use GenBank ID
(if it exists) with NCBI server, else it uses the Clone-ID (if it
exists) with the nciarray cloneID-to-GenBank server.



    •  Version 0.89.22: Fixed bugs in ClusterGram, saving current
Gene Class.


    •  Version 0.89.21: Fixed bugs in HP Chooser, Pseudocolor
Red-Yellow-Green HP-XY plot menu, HP vs. HP correlation Report.


    •  Version 0.89.20: Added correct scale for (Plot | Show
Microarray | Pseudocolor Red-Yellow-Green HP-XY ratio or Zdiff).


    •  Version 0.89.19: Added (Plot | Show Microarray | Pseudocolor
Red-Yellow-Green HP-XY ratio or Zdiff) command to display the X and Y
data as an additive sum of red (HP-X) and green (HP-Y) data.


    •  Version 0.89.18: New version of download Java installer
program that improves portability on different platforms (eg. MacOS,
etc).  Minor changes (spelling errors in parts of the
program). Improved documentation.


    •  Version 0.89.17: Added commands and functionality to a) find
all copies of a named gene in the guesser using the SetE.G.L. button,
b) added the "Replicate genes" GeneClass, c) added the "Filter by
Genes with replicates" data Filter.


    •  Version 0.89.16: Fixed several consistency checks when reading
non-standard array data.


    •  Version 0.89.15: Ignore enclosing space characters in
SamplesDB, Configuration and .mae startup files.


    •  Version 0.89.14: Fixed bug in Gene guesser when press the
"Done" button. It sets the gene, but did not update the pseudoarray 
image.  Disable genomic browser options in View menu if dbEST3'/5' or
GenBankAcc3'/5' is not in the GIPO file database. Since GenBank and
dbEST may be accessed via the mAdb Clone report or UniGene report,
this should not be a problem. In the Reference Manual a) figures were
updated for Genes instead of the older notation of Clone (Version
0.88.*); b) the Short Tutorial (Appendix A) was updated and clarified.


    •  Version 0.89.12: Added "/Report" directory to hold SaveAs text
(.txt) and plot window image (.gif) files. Improved fatal error (called
DRYROT errors) reporting. Additional parts of the state saved.


    •  Version 0.89.11: Added "Filter by positive quant. data" to
filter out raw quantified data with negative values which may occur
with some types of arrays. Also improved some of the popup window
updates when the data Filter, normalization, EGL, or current clone
changes.


    •  Version 0.89.10: Fixed bug in correlation coefficient
computation so it works correctly with all Gene Classes and data
Filter.


    •  Version 0.89.09: Fixed bug in reading configuration file that
occurs if SubMenus are omitted. Fixed bug recently introduced when
updating the finding clones similar to the current clone.


    •  Version 0.89.08: Fixed bug in auto-update of List of Gene Sets
when used with adding(removing) genes using the Control(Shift)
clicking on a gene in the array image, scatter plot, etc.


    •  Version 0.89.07: Fixed bugs in state saving.


    •  Version 0.89.06: Save the full State when do a (File | SaveAs
DB). The full state is restore when you startup the database you
saved. Corrected bug in Gene Set Difference operator. Clarified some
menu option names. Can now set # genes to report or Filter in a
highest/lowest ratio genes in (Filter | Set max # genes in
highest/lowest report).  Added Report for (Report | Genes in
'Normalization Gene List').


    •  Version 0.89.05: Added information fields in HP chooser.


    •  Version 0.89.04: Corrected bug in Ratio Median correction
option if using ratio data (i.e. Cy3/Cy5). Improved scatter plot
labeling.


    •  Version 0.89.03: Optimized the main screen area
usage. Corrected data-dependent bug which sometimes occurs with the
Use Ratio Median correction option if using ratio data (i.e. Cy3/Cy5).


    •  Version 0.89.02: Set the Use Ratio Median correction option if
using ratio data (i.e. Cy3/Cy5).


    • 

      
 
Version 0.89.01: Major version release.


      
Because MAExplorer can be used with both spotted clone arrays and
oligo arrays,we renamed clones as genes (except where Clone ID is
used) in both the MAExplorer program and in the Reference Manual.


    •  Version 0.88.08: Enabled saving the name of the last project
when you exit so it knows which file to use as a default when you do
an "Open disk DB".  Fixed the mnX/mnY data in XY-Statistics
Report. Fixed title update in main window when open a new
database. Also has fix for Linux Java installer (tested on 
 Red Hat Linux version 
6.0).


    •  Version 0.88.07: Disabled the (File | Set Project) command as
there is occasionally a problem with reading the maeProject.txt database
file which causes it to hang. This should not be a problem for users
with multiple databases as you still get the popup startup file browser
when you invoke (File | Databases | Open file DB).


    •  Version 0.88.06: Edit submenu "Sets of Conditions" HP lists is
working and is saved/restore with the File menu "Save disk DB"/"Open
disk DB".


    •  Version 0.88.05: Added mouse-over to main window and moved
gene locator button to left.  Added "Use ratio median correction"
option to Normalization menu. This option can be used for rescaling
Cy3, Cy5 intensity data if the medians are unequal. Also, the manual
has been updated and more discussion on  data mining has been added.


    •  Version 0.88.04: Edit submenu "Sets of HybridProbes" Condition
lists are partially working (not working with File menu "Save disk
DB"/"Open disk DB", set operations not enabled yet).


    •  Version 0.88.03 Edit submenu "Sets of clones" and File menu
"Save disk DB"/"Open disk DB" Save/Restore clone sets as .cbs clone
bit set files in stand-alone.


    •  Version 0.88.02: cleaned up SamplesDB.txt and MaExplorerConfig.txt
specifications and sample files in Appendix C.


    •  Version 0.87.08: Fixed some bugs in HP-X,-Y,-E chooser. Added
additional checking for missing field names in data files. If a field
is incorrect, MAExplorer can't continue. It now gives a popup "Dryrot"
error message to give us information about which fields are incorrect.


    •  Version 0.87.06: May now use combined chooser for selecting
the active HP-X, HP-Y and HP-E probes.


    •  Version 0.87.05: May now save all popup text windows as .txt
files in stand-alone mode.


    •  Version 0.87.01: May now save all plot windows as GIF images
as .gif files in stand-alone mode.


    •  Version 0.86.36: Fixed bug in "Filter by spot intensity
[SI1:SI2] sliders". Allow simultaneous viewing of HP-X vs HP-Y and Cy3
vs Cy5 (or F1 vs F2) scatter plots. You may now also simultaneously
view and HP-X/HP-Y and Cy3/CY5 ratio histograms.


    •  Version 0.86.35: A new "Filter by spot intensity [SI1:SI2]
sliders" mode has been added to the Filter menu. It is useful for
filtering individual changes when analyzing Cy3/Cy5 data.


    •  Version 0.86.34: The new stand-alone distribution is now
generated with InstallAnywhere 3.5 instead of 3.5 beta. This corrects
some of the problems we had seen with very large core images. 


    •  Version 0.86.34: The handling of "Out of memory errors" seen
when clustering large numbers of clones on a small computer has been
changed. Previously, it shut off the cache and continued the
computation - although VERY slowly. Now, it prints out the following message:

       There is not enough memory to cluster current filtered clones. Options:
    1. reduce the number of filtered clones and try again, or,
    2. disable cluster-cache (Clustering menu) - will be VERY slow.

      


    •  Version 0.86.33: A new "Set project" command has been added to
the File menu. It selects the current project, if multiple projects
have been set up (using the future "New project" command).


    •  Version 0.86.29: A new "Show mouse-overinfo" mode has been added
to the View menu (default is on). It reports HP probe and spot/clone details
in the array image, scatter plot, expression profile overlay plot, etc.


    •  Version 0.86.27: In stand-alone mode, the "Open disk DB" now
lets the user open a .mae startup file. The user browses the
local file system to select the startup file to use.


    •  Version 0.86.25: When using ratio (i.e. Cy3/Cy5) data, the
menu entries, plot and report legends change the names from (F1,F2) to
(Cy3,Cy5) where appropriate.


    •  Version 0.86.24: Corrected bug in using the ratio and
intensity thresholding of mean HP data was previously using the single
probe data.


    


     

    4.3 Web Browser problems when running MAExplorer as an applet
    

Because MAExplorer is a large system, and there may be occasional
problems running it in some Web browsers on some operating systems. We
recommend you run MAExplorer as a stand-alone application as it is
more robust.

    



     

    4.4 Handling fatal error reporting (i.e. DRYROT errors)
    

If you encounter a fatal error that is detected by MAExplorer, it will
popup an error reporting window. We call this a "DRYROT" error (thanks
to "S.A.I.L." - Stanford AI Lab) because something is wrong in the
program or in the user's data files and from which it can not
recover. This type of error should not have happened. Please save and
e-mail the report to us so we can try to fix the bug or diagnose the
problem. The following figure shows an example of part of a DRYROT
error report.
    



    


    

Example of a fatal Dryrot Error


    

Figure 4.4 Example of a fatal Dryrot Error window.  This may occur for
a variety of reasons. This window lists the main reason and also lists
some of the MAExplorer state information. If you wish, you may save this
window (press the "SaveAs" button) and mail it to us. We may try to
correct the problem in the next release if it is a problem with MAExplorer.
Alternatively, it could be a user data error.


    


    


    

Example of a fatal Dryrot Error after SaveAs


    

Figure 4.4.1 Example of a fatal Dryrot Error window after SaveAs. 
This tells you where the saved error message file was saved and 
the email address to send it to if you wish.


    


    







Release Archive for stand-alone MicroArray Explorer on NCI/LECB







    

    
Release Archive for stand-alone MicroArray Explorer on NCI/LECB

    

This is an archive of some of the older stable versions of the
MAExplorer stand-alone application program. These are the full
installers which include the Java JDKs for all operating systems as
well as the MGAP database. The user reference manual (available as a
zip file) specific for that version is also included. After a while,
we will remove some of the older releases.  To find what the current
and beta releases are, see the  Install home page.  The
changes between releases are listed in  Section 4.2 Revision Notes.

    



    

    
Release
Release Date
Manual (.zip) for Release

    



     
0.96.02

07-02-2002
- 

    


     
0.95.20

05-31-2002
MaeRefMan.zip (10Mb) 

    



     
0.95.16

05-24-2002
MaeRefMan.zip (10Mb) 

    



     
0.95.04

03-22-2002
MaeRefMan.zip (10Mb) 

    



    

    


    

    


    






 MAExplorer - Microarray Exploratory Data Analysis



 


     

    Acknowledgements
    

Primary contributers to MAExplorer were Peter Lemkin
(LECB/NCI), Greg Thornwall (SAIC/FCRDC) in the Laboratory of Experimental and
Computational Biology, NCI/NIH, and Jai Evans (DECA/CIT/NIH).
    

Primary contributers to Cvt2Mae were Peter Lemkin (LECB/NCI), Greg
Thornwall (SAIC/FCRDC), Bob Stephens (ABCC/NIH).
    

We wish to thank the many members of Lothar Hennighausen's

Laboratory of Genetics and Physiology (NIDDK) who inspired the
initial development of MAExplorer and its continued
development. Thanks also to:
    

Greg Alvord (SAIC/FCRDC),
    
Kevin Becker and Chris Cheadle (NIA/NIH),
    
Breast Cancer Think Tank (NCI),
    
Damien Chaussabel (NIAID),
    
Terry Clark and Josef Jurrek (U. Chicago),
    
Mitko Dimitrov (LECB/NCI),
    
Jai Evans and Chris Santos (DECA/CIT/NIH),
    
Troy Moore (Research Genetics),
    
Peter Munson (CIT/NIH),
    
Alan Li (SourceForge),
    
Quang Tri Nguyen (LECB+LCRC/NCI),
    
John Powell and Esther Asaki (CIT/NIH),
     
Eric Shen (U. Arizona),
    
Moshe Shani (Agr. U. Israel),
    
Richard Simon (NCI/NIH),
    
Bob Stephens and Gary Smithers (ABCC/FCRDC),
    
Ron Taylor (U. Colorado),
    
Mark Vawter (NIDA/NIH, UC-Irvine),
    
John Weinstein (LMP/NCI), David Kane (SRA/NCI), Ajay (LMP/NCI),
    


and to many others for useful discussions and suggestions that have
helped improve the MAExplorer's capabilities and usability.


    

Thanks also to Jeff Thomas, Charmaine Richman, and Tom Stackhouse
(NCI) for helping with the MAExplorer Open Source process.


    










 MAExplorer - Microarray Exploratory Data Analysis



 

    

    References to related exploratory data analysis methods and MAExplorer
    

This short list of references is limited to a few related to
exploratory data analysis methods for microarrays as they relate to
MAExplorer. It is not meant to be inclusive. More extensive
lists of references to many of the array preparation and data mining
methods can be found in some of these papers and on the Internet.
    


      


      

    1.  Beardsly T (1999) Scientific American, March 1999, 35-36.


       
    2.  Bickel, D (2001) "Robust Cluster Analysis
of DNA Microarray Data: An Application of Nonparametric Correlation
Dissimilarity.  Proceedings of the Joint Statistical Meetings of the
American Statistical Association (Biometrics Section, to appear).  [
http://www.mathpreprints.com/math/Preprint/bickel/20010730/3/]


       

    3. Brazma A, Hingamp P, Quackenbush J, Sherlock
G, Spellman P, Stoeckert C, Aach J, Ansorge W, Ball CA, Causton HC,
Gaasterland T, Glenisson P, Holstege FC, Kim IF, Markowitz V, Matese
JC, Parkinson H, Robinson A, Sarkans U, Schulze-Kremer S, Stewart J,
Taylor R, Vilo J, Vingron M (2001) Minimum information about a
microarray experiment (MIAME) - toward standards for microarray
data. Nature Genetics 28: 365-371. [See 
http://genomics.nature.com/supplementary_info, and http://www.mged.org/)]


      

    4.  Cleveland WS (1985) The Elements of Graphing Data. Wadsworth 
Press, Monterey, CA, pp 1-323.


       

    5.  
DeRisi J, Penland L, Brown PO, Bittner ML, Meltzer PS, Ray M, Chen
Y, Su YA, Trent JM (1996) Use of a cDNA microarray to analyze gene
expression patterns in human cancer. Nature Genetics 1996,
14: 457-460.


       

    6. 
Dudoit S, Yang YH, Callow MJ, Speed TP (2000,Aug)
Statistical methods for identifying differentially expressed genes in
replicated microarrays experiments. TR-578, Dept. of Statistics,
Stanford Univ., Stanford, CA.


       

    7.  
Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster
analysis and display of genome-wide expression patterns.  PNAS
USA 95: 14863-14868.


       

    8. Jagota, A (2001) Microarray Data Analysis and
Visualization. 
Bioinformatics By The Bay Press, 101 pp. ISBN-09700297-3-X.


       

    9. Kaufman L, Rousseeuw PJ (1990) Finding Groups in
Data: An Introduction to Cluster Analysis. J. Wiley and Sons, Inc.,
New York.


       
    10.  
Kerr MK, Churchill GA (2001) Statistical design and the analysis
of gene expression microarray data. Genet Res.
77(2):123-8. Review.


       
    11.  
Kerr MK, Churchill GA (2001) Bootstraping cluster analysis:
Assessing the reliability of conclusions from microarray
experiments. PNAS 98:8961-8965.
         

    12.  

Korn EL, Troendle JF, McShane LM, Simon R (2001)
Controlling the number of false discoveries: Application to
high-dimensional genomic data. BRB, NCI, Bethesda, MD. Aug 22, 2001,
Tech Report #3.
         

      

    13.  
Lipkin LE, Lemkin PF (1980) Data-base techniques for multiple
two-dimensional polyacrylamide gel electrophoresis analyses.  
Clin. Chem.  26: 1403-1412. (GELLAB-II
home page http://www.lecb.ncifcrf.gov/lemkin/gellab.html)


      

    14.  
Lemkin PF, Lester EP (1989) Database and search techniques for
two-dimensional gel protein data: a comparison of paradigms for
exploratory data analysis and prospects for biological modeling.  
Electrophoresis 10: 122-140.


      

    15.  Lemkin PF (1995) Representations of protein patterns from 2D gel
electrophoresis databases.  In: Pickover, C., (Ed) The Visual
Display of Biological Information, World Scientific Publishers,
River Edge, New Jersey, pp 43-59.



      

    16.  Lemkin
PF (1993) Xconf: a network-based image conferencing system.
Comput. Biomed. Res. 26, 1-27.


      

    17.  Lemkin
PF (1997) Comparing Two-Dimensional electrophoretic gels across
the Internet.  Electrophoresis, 18: 461-470.
[Extended paper], (Web site 
http://www.lecb.ncifcrf.gov/flicker/)


      

    18.  
Lemkin PF, Thornwall G (1999a) Flicker image comparison of 2-D gel 
images for putative protein identification using the 2DWG meta-database.
Molecular Biotechnology, 12:(2) 159-172.
[Extended paper and Web site http://www.lecb.ncifcrf.gov/2dwgDB/]

      

    19.  
Lemkin PF, Myrick JM, Lakshmanan Y, Shue MJ, Patrick JL, 
Hornbeck PV, Thornwall GC, Partin AW (1999b) Exploratory Data Analysis 
Groupware for Qualitative and Quantitative Electrophoretic Gel Analysis 
Over the Internet - WebGel. Electrophoresis 20:(18)
3492-507. (PDF), 
(also see WebGel server:
 
http://www.lecb.ncifcrf.gov/webgel).


      

    20.  
Lemkin PF, Thornwall GC, Walton KD, Hennighausen L (2000) The
Microarray Explorer tool for data mining of cDNA microarrays -
application for the mammary gland Nucleic Acid Research
28:(22) 4452-4459.
(PDF).


      

    21.  Lemkin PF, Thornwall GC, Walton KD, Hennighausen L (2000) 
MicroArray Explorer - a tool for data mining of microarrays - Overview.
(PDF).


      

    22.  Lemkin PF, Thornwall GC, Walton KD, Hennighausen L (2000)
MicroArray Explorer - a tool for data mining of microarrays - Examples.
(PDF).


       
    23.  Lemkin PF, Thornwall GC, Powell J, Asaki
E (2000) Using MAExplorer with the NCI/CIT mAdb Web server. (PDF) and [http://nciarray.nci.nih.gov/]
 


       
    24.  Lemkin PF (2001) Introduction to Data
Mining of Microarrays using the MicroArray Explorer.  (PDF) or (PPT).


       

    25.  Lemkin PF (2001) Using Cvt2Mae to convert Affymetrix array
data for MAExplorer.  (PDF)


       

    26.  Lemkin PF, Thornwall G, Hennighausen L (2001) MicroArray Explorer
- A Java-based Tool For Data Mining Microarrays.  Paper presented at
the AMS-IMS-SIAM Summer Conference on Statistics in Functional
Genomics, June 10-14, 2001. 
(PDF).


       

    27. 
McShane LM, Radmacher MD, Freidlin B, Yu R, Li M-C, Simon R (2001)
Methods for assessing reproducibility of clustering patterns
observed in analyses of microarray data. BRB, NCI, Bethesda,
MD. July 1, 2001, Tech Report #2.


       

    28. 
Radmacher MD, McShane LM, Simon R (2001) A paradigm for class
prediction using gene expression profiles. BRB, NCI, Bethesda, Nov
19, 2001, Tech Report #1.


      

    29.  Schneiderman B (1997) Designing the Human Interface, 3rd
edition. Addison-Wesley Pub. Co., NY, pp 1-638.


      

    30.  
Schulze A, Downward J (2001) Navigating gene
expression using microarrays - a technology review. Nature Cell
Biology 3: E190-E195.


      

    31. 
Simon R, Radmacher MD, Dobbin K (2001) Design of Studies Using
DNA Microarrays. BRB, NCI, Bethesda, MD. 2001, Tech Report #4.


      

    32.  Sneath PHA, Sokol RR (1973) Numerical taxonomy. W.H. Freeman
and Co, San Francisco. pp 1-573.


       

    33.  StatSoft Inc. (2002)
Electronic Statistics Textbook, Tulsa, OK: StatSoft.  Available at 
http://www.statsoftinc.com/textbook/stathome.html


      

    34.  Tufte E (1997) Visual Explanations. Images and quantities,
evidence and narrative. Graphics Press, Cheshire, CT, pp 1-156.


      

    35. Tukey J (1977) Exploratory data analysis. Addison-Wesley
Pub. Co., Reading, MA, pp 1-688.


      

    36.  
Vawter MP, Barrett T, Cheadle C, Sokolov BP, Wood WH III,
Donovan DM, Webster M, Freed WJ, Becker KG (2000) Application of cDNA
Microarrays to Examine Gene Expression Differences in
Schizophrenia. Submitted.


       

    37.  
Weinstein JN,  
Myers TG, O'Conner PM, Friend SH, Fornace AJ, Kohn KW, Fojo T, Bates
SE, Rubinstein LV, Anderson NL, Buolamwini JK, van Osdol WW, Monks AP,
Scudiero DA, Sausville EA, Zaharevitz DW, Bunow B, Viswanadhan VN,
Johynson GS, Wittes RE, Paull KD(1997) An Inforation-Intensive
Approach to the Molecular Pharmacology of Cancer.  Science
275: 343-349.
 

       

    38.  
White KP, Rifkin SA, Hurban P, Hogness DS (1999) Microarray Analysis
of Drosophila Development during Metamorphosis. Science
286: 2179-2184.



    






 MAExplorer - Microarray Exploratory Data Analysis



 

    

    Appendix A. Short tutorial for MAExplorer
    

This tutorial is for use with MAExplorer, an exploratory data analysis
facility for microarray DNA databases.  It may be used with any
MAExplorer database.  As with all tutorials, they are only starting
points for getting you started - in this case into understanding the
data mining analysis environment. Try out new options on your own,
you can't break anything :-). 


    
This tutorial lets you


      

    1. Analyze expression of individual genes

    2. Analyze expression of gene families and clusters

    3. Compare expression patterns in multiple hybridized samples

    


    

    

     NOTE: THIS APPENDIX IS BEING REVISED AND EXPANDED...
    

    

    



    

    A.1 Demonstration data
    

Note that the downloadable MAExplorer stand-alone application includes
a subset of 50 hybridized samples from the MGAP database including a
number of startup files for that data (see the      the list of startup .mae
files included in the download
installation).
    

There is also a 
pre-computed example of an Ordered Condition List using 4
conditions of replicates of C57B6 (pregnancy day 13, lactation days 1
and 10, and stat5a(-,-) 15 samples. The database also includes 4
additional condition sets of this data and an Ordered Condition List
of the 4 conditions (in the State/ directory). This may be used to
demo the OCL F-test filter.

    

If you have access to another MAExplorer database, you can use it
instead since the tutorials are fairly generic. 
    


    
Using the stand-alone application for the tutorial

    
These same subsets as well as other subsets of the MGAP data are
available in the set of .mae startup files distributed with
MAExplorer. To access these files,

      

    1.  Start MAExplorer after you have installed it. Eg. in Windows, go
to the Windows "Start Menu" and click on MAExplorer. If it is not in
your Start Menu, you can go to where you installed it (typically
C:\Program Files\MAExplorer) and click on
MAExplorer.exe.


    2. Then after it starts, go to the "Files" menu and select "Open disk
DB" and select the startup file you want. Alternatively, you can go
directly to the list of startup files in C:\Program
Files\MAExplorer\MAE) and double-click on one of the  startup files.


    


    

    A.2 General instructions:
    

Throughout this tutorial we refer to condition X and condition Y.
These are different hybridized samples in the particular database you
have loaded. For example, in the MGAP database X might be lactation
and Y might be pregnancy. X and Y 'sets' are multiple samples of these
two conditions.

    


First, select one of the start up databases.


    

 If the particular samples you want to analyze are not listed in that
example, after it starts you will be able to add samples you do want
and remove samples you don't want - regardless of which example was
intially used if the database "Samples" database contains additional
hybridized samples.
    

When it starts, a main window will pop up. It then downloads a gene
database tables and the particular hybridized samples you specified.
When it is ready for you to begin interaction, the menu bar will
become active and it will display a green  Ready -
click on a gene to query database  message. Depending on your
Internet connection speed, it may take a few minutes to set up. If you
are running MAExplorer as a stand-alone application and it is getting
data from your local disk, startup will be much faster.

    


Second, go to the A.3 instructions for
self-guided tutorial below for instructions on what to do next.
    

HINT: print this tutorial page and then read
the following instructions from the printout rather than trying to
keep this window visible. You might also print the parts of the
MAExplorer Reference Manual for the same reason.
    

HINT: You might want to keep a record of the
commands you have used or the messages and measurements you have
made. To do this you need to enable message and command history
logging. Go to the View pull-down menu and then select the type of
logging you want using the 
Show log of messages or the  Show log of command history
commands.
    

NOTES:.  On computers with low resolution
(i.e. less than 1024 X 780) you may need to resize the windows and
move them to different parts of the screen to view them
simultaneously.
    


    

    

    A.3 Self-guided tutorial of MAExplorer - notation and examples
    

The following is a  self-guided tutorial
(you issue the commands) that illustrates some of the data
analysis capabilities. In the following examples, the notation "go
to A:B:C" means go pull-down menu A, then submenu B and, then make
selection C. "Selecting a gene" from the microarray image or scatter
plot means clicking on a spot in the  pseudoarray image or a point in
the any of the plots.
    


    


    

    A.3.1 Review of types of gene data available in the database
    

step 1: go to Analysis: GeneClass: All genes
     
          
the array shows all genes with white circles.
    

step 2: go to Analysis: GeneClass: All named genes
     
          
the array shows named genes with white circles.
    

step 3: go to Analysis: GeneClass: ESTs similar to genes
    
          
the array shows ESTs similar to named genes with white circles.
    

step 4: go to Analysis: GeneClass: ESTs
    
          
the array shows unknown ESTs with white circles.
    

step 5: go to Analysis: GeneClass: All genes and ESTs
    
          
the array shows all named genes and all ESTs with white circles.
    

step 6: go to Analysis: GeneClass: Replicate genes
    
          
the array shows replicate genes having at least 2 copies in the
    
          
array with white circles.
    

step 7: go to Analysis: GeneClass: Calibration DNA
    
          
the array shows calibration DNA (if present) with white circles.
    

step 8: go to Analysis: GeneClass: Your plates
    
          
the array shows clones from user's plates (if present) with white circles.
    


    


    

    A.3.1.1 Analysis of the expression of a single known gene
    


      

    1. ratio between two conditions X and Y (HP-X, HP-Y)

    2. expression profile of a set of conditions (HP-E) (see 
Example A.3.1.7)

    

step 1: click on the blue "Enter gene name" button to pop up a name
 entry window
    

step 2: start typing gene name into blue text entry window
    

step 3: once gene names appear, click on gene of choice
    

step 4: press "Done" button in pop up window
    
          
A yellow circle will define the gene as the "current gene" in the microarray
     
          
pseudoarray image (info on gene is also provided in the status area above the array).
    
          
If there are replicate grids (left and right fields of repeated genes are denoted
    
          
by F1 and F2) in the array (HP). The mean(HP-X,HP-Y) values and the (HP-X/HP-Y)
    
          
values for the specified gene are reported are reported.
    

step 5: alternatively, click on an array spot of choice to define any gene
    
          
in the array as the new current gene
    




    

    A.3.1.2 Find a subset of genes with a common substring (e.g. *ONCO*)
    

step 1: click on the blue "Enter gene name" button to pop up a name
 entry window
    

step 2: start typing "*ONCO*" (without the quotes) into blue
text entry window
    

step 3: once gene names appear, press "Set E.G.L." button in pop up 
window
    
          
Magenta squares will indicate these genes in the pseudoarray image.
     
          
These include the 'onco'genes and the proto-'onco'genes
    


    

    

    A.3.1.3 Two conditions - scatter plots:
    

Create a scatter plot of two hybridized samples where condition X data
is on the X axis and condition Y data on the Y axis.
    

step 1: go to Analysis: Plot: Scatter plots: HP-X vs. HP-Y.
    
          
then click on yellow circle in scatter plot to get HP-X/HP-Y
ratio for the gene
    

step 2: click on any point in the scatter plot
    
          
this also alternatively defines any gene in the plot as the new 
current gene
    

step 3: zoom in on a region of the plot using the vertical or
horizontal scroll bars
    

step 4: click on another point in the scatter plot to get the 
HP-X/HP-Y ratio another gene
    

step 5: press "Close" button to remove pop up window
    




    

    A.3.1.4 Scatter plot of Cy3 vs Cy5 or replicate spots (F1 vs F2) of one sample
    

Create a scatter plot of Cy3 vs Cy5 channels or replicate spot F1, F2
data if your database is contains (Cy3,Cy5) ratio data or it contains
replicate spot fields (F1,F2).

    

step 1: go to Analysis: Plot: Scatter plots: Cy3 vs. Cy5
    
          
or go to Analysis: Plot: Scatter plots: F1 vs. F2
    
          
Then, click on green circle in scatter plot to get Cy3/CY5 
ratio for the gene
    
          
or F1/F2 ratio for replicate spots for that gene
    

step 2: click on any point in the scatter plot
    
          
this also alternatively defines any gene in the plot as the new 
current gene
    

step 3: zoom in on a region of the plot using the vertical or
horizontal scroll bars
    

step 4: click on another point in the scatter plot to get the 
HP-X/HP-Y ratio another gene
    

If you are working with Cy3/Cy5 dye-swap data, you may swap the
Cy3/Cy5 channel data to Cy5/Cy3 for any selected subset of
samples. This may make it easier to use the data in various ways when
data mining. If you do not have this type of data, go to step 7.
    

step 5': go to Samples: Edit (Cy5/Cy3) else use (Cy3/Cy5) menu
     

step 6': select the samples you wish to swap and press "Done". This
    
          
enables you to see the swapped results in the scatter plot

    

step 7: press "Close" button to remove pop up window
    





    

    A.3.1.5 Filter by expression ratio between two conditions X and Y
    

step 1: go to Analysis: Plot: Histograms: HP-X/HP-Y
    
          
the histogram shows the ratios
    

step 2: move pop up plot so you can see it and the array
simultaneously
    

step 3: choose (click on) a ratio bin 
    
          
genes filtered by the ratio range of the bin will light up on the array ('+'s)
    

step 4: click on different bin in the histogram to select
another bin
    

step 5: click on word "Freq" on left in histogram to remove the
histogram bin filter
    

Note of caution: if the signal is close to background the X/Y ratio 
may be bogus. 
    
You can filter out low intensity genes by
    


     

    

    A.3.1.6 Filter by spot intensity range
    

step 1: go to Analysis: Filter: Filter by spot intensity [SI1:SI2] 
sliders: Use spot intensity [SI1:SI2] sliders
    

step 2: adjust intensity lower bound (SI1) to remove low ratio
genes
    

step 3: when done, remove the 'Filter by intensity sliders' by
toggling it off (redo step 1 to toggle it off)
    

step 4: repeat steps 1-3, but this time use Filter : Filter
by [I1:I2] sliders : 
     
          
Use spot intensity (or Cy3/Cy5) [I1:I2] sliders
    


    

    

    A.3.1.7 Multiple conditions - expression profile plots of HP-E data:
    

step 1: go to Analysis: Plot: Expression profile: Display a gene's
expression profile
    

step 2: 
after the expression profile window pops up, click on a
gene in array to see its profile 
    

step 3: click on a line in the profile plot to see its intensity
    

step 4: click on a different gene in the array to see its
profile
    

step 5: press "Show HPs" button  to see the list of samples used
    

step 6: press "Close" button to remove pop up windows
    



    



    

    A.3.2 Changing the normalization between hybridized samples
    

You may change the normalization method used to scale data between
hybridized samples so they may be compared. 

    

    

    A.3.2.1 Set normalization
    

step 1: go to Analysis: Normalization: Median intensity
    

step 2: go to Analysis: Plot: Scatter plots: HP-X vs. HP-Y
    
          
to see the effect of normalization on the scatter plot. 
Note how outliers appear.
    

step 3: go to Analysis: Normalization: Zscore of intensity
    

step 4: go to Analysis: Normalization: Zscore of log intensity, stdDev
    

step 5: go to Analysis: Normalization: Unnormalized
    
          
this does not scale data between samples.

    

step 6: go to Analysis: Normalization: Median intensity
    
          
this leaves the normalization method in Median mode.

    


    



    

    A.3.3 Analysis of the expression profiles of gene classes
    

You may restrict the set of genes by Gene Class. Several built
in gene classes are defined. You may also set up additional ones and
filter by those (not covered in this short tutorial).

    

    

    A.3.3.1 Filter by gene class membership
    

step 1: go to Analysis: GeneClass: All known genes
    
          
the array only shows named genes (additional gene subclasses
are being added)
    

step 2: go to Analysis: Plot: Scatter plots: HP-X vs. HP-Y
    
          
to see the two condition expression of just these genes
    

step 3: go to Analysis: Plot: Expression profiles: Display Filtered
genes expression profiles 
    
          
to see the multiple condition expression of just these genes. This may take a

    
          
while if there are many genes
    

step 4: you can click on a line in any of the plots to see the 
samples' intensity value for that gene 
    

step 5: when done, press "Close" button in all pop up plot windows

    

    

    

    A.3.3.2 Gene Reports
    

step 1: go to Analysis: Report: Gene reports: Filtered genes: Genes passing Filter
    
          
Clicking on a blue entry will bring up I.M.A.G.E, dbEST, UniGene, or GenBank,
    
          
LocusLink, or mAdb Clone database in pop up Web page
    

step 2: press "Close" button in report, and close this pop up Web page
    

step 3: go to Analysis: Report: Table format: Tab-delimited
    
          
to enable creating Excel-compatible reports
    

    

    

    A.3.3.3 Exporting Gene Reports to Excel
    

step 1: repeat step 1 of the Gene Report, but this time
to make text-formated report
    

step 2: cut the text from this window and paste it into an Excel window.
    
           
This is useful for exporting data if you are on a Windows PC
    

step 3: go to Analysis: Filter: all genes
    
          
to restore it to all of the genes from all named genes
    

step 4: go to Analysis: Report: Table format: Spreadsheet
    

step 5: press "Close" button in report
    



    



    

    A.3.4 Analysis of the expression profile of multiple hand picked genes
    

Users can manually define a set of genes which are kept in the
Edited Gene List (E.G.L.). Various operations can then use the
EGL to restrict the set of data being analyzed.

    



    

    A.3.4.1 Define a list of edited genes, then plot all their expression
profiles at one time
    

step 1: go to View: Show 'Edited Gene List' 
    
           this turns on the
'Edited Gene List' magenta square box overlays
    

step 2: hold CONTROL key and click on genes in array to add a gene
    
step 2': hold SHIFT key and click on genes in array to delete a gene.
    
          
This lets you edit a list of genes. It also works when clicking in a scatter plot
    

step 3: go to Analysis: Plot: Scatter plots: HP-X vs. HP-Y
    
          
to see the Edited Gene List in the scatter plot
    

step 4: try defining (or removing) E.G.L. genes in the scatter
plot by holding the
            
   CONTROL (or SHIFT) key when clicking on points in the
scatter plot
    


    

    

    A.3.4.2 Filtering by edited gene list
    

step 1: go to Analysis: Filter: Filter by 'Edited Gene List' 
    

step 2: go to Analysis: Plot: Expression profiles: Display Filtered genes expression profiles
    
          
scroll through the plots to see all of the profiles
    

step 3: go to Analysis: Filter: Filter by 'edited gene list' 
    
          
this turns off the 'edited gene list' filter
    

step 4: press "Close" button in expression profiles window
    


    

    

    A.3.4.3 Report of edited gene list
    

step 1: go to Analysis: Report: Gene report: genes in 'edited gene list'
    
          
reports edited genes
    

step 2: press "Close" button in report
    

step 3: go to Analysis: Filter: Filter by 'edited gene list' 
    
          
this turns off the 'edited gene list' filter
    

step 4: go to View: Show 'edited gene list' 
    
          
this turns off the 'edited gene list' squares overlay
    


    



    

    A.3.5 Identify a cluster of genes with similar expression 
profile to the current selected gene
    


step 1: go to GeneClasses: All named genes and ESTs
    

step 2: go to Analysis: Plot: Cluster plots: Cluster genes with expression profiles similar to current gene 
    
          
this will pop up a cluster summary and cluster distance slider control window.

    
          
 Move the summary and slider windows so you can see all 3 windows. The size of 
    
          
 the cyan boxes on similar genes in the pseudoarray is proportional to the similarity.
    
          
Adjust the cluster distance slider  to smaller values and note how
the number of genes clustered decreases. 
    
          
It should be set for a reasonable number considering the material you
are analyzing.
    

step 3: select (click on) a new current gene 
    
          
the genes which belong to that cluster are labeled in the array with cyan boxes
    
          
and are defined as the "current cluster". The current gene you click on has
    
          
a green circle around it
    

step 4: press "Cluster Report" button in the cluster summary
    
          
this pops up a Gene Report for the clustered genes
    

step 5: press "Close" button in the  report
    

step 6: press "EP plot" button in the cluster summary
    
          
this pops up a scrollable list of expression profile plots sorted by similarity
    
          
to the current selected gene.
    

step 7: press "Close" button in the report
    

step 8: press "Close" button in the cluster summary 
    


    



    

    A.3.6 Identify clusters of genes with similar expression under
various conditions using data mining filters
    


step 1: go to GeneClasses: ESTs similar to genes
    

step 2: go to Analysis: Plot: Cluster plots: K-means clustering of 
gene expression profiles 
    
          
this will pop up a cluster summary and slider control window. Move the
summary
    
          
and slider windows so you can see all 3 windows. The size of the 
    
          
magenta circles in the array is proportional to # genes/cluster
    

step 3: select (click on) a new current gene 
    
          
the genes which belong to that cluster are labeled in the array with tiny green
    
          
 numbers are defined as the "current cluster". The current gene you click
    
          
on has a green circle around it
    

step 4: go to View: Show 'edited gene list'
    
          
genes in the current cluster were also copied to the edited gene 
list
    

step 5: go to Analysis: Report: Gene report: genes in 'edited gene list'
    
          
reports genes in the current cluster
    

step 6: press "Close" button in report
    

step 7: go to View: Show 'edited gene list' 
    
          
this turns off the 'edited gene list' squares overlay

    

    

    A.3.6.1 Varying the number of clusters
    

step 1: vary the "# of clusters" slider value from 6 to 10, then 20
    
          
note the number of clusters changes and the gene cluster composition also 
changes
    


    

    

    A.3.6.2 Defining a new cluster "seed" to recluster the genes
    

step 1: select a new current gene in array and press the
"Recompute clusters" button
    
          
this recomputes the clusters using the current gene as the new seed gene

    



    

    A.3.6.3 Cluster expression profile plots
    

step 1: press "EP plot" button and scroll down the list after they appear
    
          
the primary nodes for each cluster are indicated with red labels
in the set of
    
          
profiles, and the other genes are labeled with their cluster number

    

step 2: press "Mean EP plot" button and scroll down the list after they appear
    
          
these are the mean expression plots of the primary nodes clusters.
    


    

    

    A.3.6.4 Report of all clusters
    

step 1: press the "Cluster-Report" button to get a sorted cluster
    
          
list scroll the spreadsheet to the right to see the cluster statistics
    

step 2: press the "Mn-Cluster-Report" button to get a sorted cluster list
    
          
scroll the spreadsheet to the right to see the mean expression profiles
    

step 3: press "Close" button in pop up windows
    

    

    

    A.3.6.5 Current cluster in scatter plot
    

step 1: go to Analysis: Plot: Scatter plots: HP-X vs. HP-Y
    

step 2: move the plot so you can see both scatter plot and array
    

step 3: click on a gene in the cluster or on spots in the scatter plot
    
          
note that the green cluster numbers are drawn in the scatter plot
    

step 4: go to Edit: Sets of genes : Save 'Edited gene list' as gene sets
    
          
this will pop up a dialog box requesting "Enter new gene set name"
    

step 5: type "Genes in current cluster class"
    
          
this will save the current cluster in a gene set. 
This gene set will
    
          
be used in the next example
    

step 6: press "Close" button in pop up windows
    

step 7: (optionally) investigate hierarchical cluster with clustergrams and
    
          
dendrograms by going to Plot : Cluster Plots : Hierarchical clustering plot for HP-E
    


    



    

    A.3.7 User Gene Set operations
    

You may manipulate sets of genes. Some of these are predefined for you
by the database (eg. All named genes, ESTs, etc.). Others are defined
by particular operations (E.G.L., clustering, etc.), and lastly others
may be defined by you using logical operations on these sets (OR, AND,
DIFFERENCE).

    

    

    A.3.7.1 List of the current gene sets
    

step 1: go to Edit: Sets of genes : List saved gene sets
    
          
this lists the current list of gene sets
    

step 2: Change the E.G.L.      set
of genes and note how the # of E.G.L. genes changes in the list.
    
          
You can add (remove) genes to the E.G.L. by clicking on a spot in the array while the
    
          
CONTROL (SHIFT) key is held down.
    



    

    A.3.7.2 Filter by user defined gene set
    

step 1: go to Edit: Sets of genes : Set 'User Filter Gene Set' (for Filter)
    
          
this will request a gene set to use with the Filter in a pop up dialog box.
    
           
Enter gene set # for the set for "Genes in current cluster class" which you saved
    
          
in the previous example.
    
          
then press "Ok" in the dialog box.
    

step 2: go to Analysis: GeneClass: All genes and ESTs
    
          
this resets the filter to look at all genes and ESTs
    

step 3: go to Analysis: Filter: Filter by 'User Gene Set' membership
    
          
this restricts the genes to the saved current cluster in the previous 
example
    




    

    A.3.7.3 Gene set operations
    

step 1: go to Edit: Sets of genes : OR (Union) of 2 gene sets
    
          
this will request 3 gene set names in a pop up dialog box.
    
          
Enter set # for (All known genes) for the 1st gene set name,
    
          
Enter set # for (Genes in current cluster class) for the 2nd gene set name,
    
          
Enter "Union of known genes and genes in current cluster" for new gene set name.
    
          
then press "Ok" in the dialog box.
    
          
this computes the union of the two gene sets into a new gene set
    

step 2: go to Edit: Sets of genes : Set 'User Filter Gene Set'
    
          
this will reset the 'User Filter Gene Set' for the Filter in a pop up dialog box.
    
          
Enter the set number or the beginning of the set name 'Union' that is the 
    
          
set for "Union of known genes and genes in current cluster" just saved.
     

step 3: try saving other Filtered genes sets and doing other
gene set operations.
    



    



    

    A.4 Additional tutorials
    

If you wish to investigate MAExplorer in more detail, try some of the
suggested examples in the      advanced
tutorial (Appendix B) in the reference manual.


    

    


    


    



    







 MAExplorer - Microarray Exploratory Data Analysis



 


    

    

    Appendix B. Advanced tutorial for MAExplorer
    

There are a number of things you may do in this facility. We wrote
this advanced tutorial to help demonstrate some of its capabilities. A
     short tutorial (Appendix A)
is also available and we recommend doing it before attempting the
advanced tutorial. Sources of startup data to use with the tutorials
are listed in the short tutorial. As with all tutorials, they are only
starting points for getting you into the analysis environment - try
out new options on your own, you can't break anything :-).


      

    1. Analyze expression of individual genes

    2. Analyze expression of gene families and clusters

    3. Compare expression patterns in multiple hybridized samples

    



    

    

     NOTE: THIS APPENDIX IS BEING REVISED...
    

    

    


    Here are some things to try 
    


      

    1.  You could click on the Reference manual in the Help menu. That is this
reference manual, but it appears in a new Netscape Web browser window
so you may read it while working on MAExplorer. Delete the window when
you are finished with it.
      


    2.  Click on the Glossary
in the MAExplorer Help menu. This section describes terms used in
MAExplorer. Delete the window when you are finished with it.
      


    3.  Click on the Index. It may be useful in finding things that are
not in the table of contents.


    4.  Note: a hybridized sample is a microarray hybridized with cDNA
derived from the mRNA from a particular experiment sample (see notation defined in the
Overview).  Currently, you may start MAExplorer with preloaded set
of samples by starting the stand-alone MAExplorer on a .mae
file. Alternatively, you may start it with no samples loaded. It
the latter case, you would load samples you are interested in from the
Samples menu.

      


    

When first started, it loads some initial data it needs as well as the
particular hybridized samples you specified.  After MAExplorer starts,
it displays  "Ready - click on a gene to query
database" and the menus becomes active. Here are some things to
try.




      

    1.  Click on different genes (i.e. spots in the  pseudoarray image). The
pseudoarray image may or may not correspond to the actual array -
depending on how the data was derived. Notice the data that gets
displayed in the three text lines above the image.  If the spot you
click on is a named gene (e.g.  [1-A4,3] at row 4 column 3 in Field
1), it will also print out the GeneName of the gene.


    2.  Look at the pull-down menus. They consist of sets of commands
with similar functionality grouped together in sub-menus. In
particular, look at the Analysis
menu. It contains an ordered list of submenus that may be thought of
as the sequence one might perform an analysis. In reality, an analysis
is more complicated and involves iterating various steps (see Figure 3.1).


    3.  Go to the "Samples"
menu. This menu allows selecting hybridized samples for the
HP-X, HP-Y, HP-X and HP-Y 'sets', and the HP-E list. You have several
ways to do this. The easiest way is to use the "Choose HP-X, HP-Y and
HP-E" sample selection wizard. The other alternative is to use one of
the set of cascading menus that may be used to change the selected
hybridized samples. Note that the HP-X and HP-Y submenus assign
samples for subsequent analysis such as X-Y scatter plots.  The HP-E
submenu sets the list of samples for expression profiles.  Note how you
may assign a sample either by going through the cascading menus or from an
alphabetic list of all hybridized samples in the pseudoarray image.
To change the default HP-X (HP-Y) sample, click on the purple "[X]"
("[Y]") box on the left side of the image (above the list of samples)
so it is selected. Then click on the desired purple "*" adjacent to
the samples listed on the left edge of the pseudoarray image. You may
switch between using single and multiple samples (i.e. 'sets') with
HP-X and HP-Y. Note the "HP-X:" and "HP-Y:" labels at top left when
you switch between single and multiple samples.  Go to the HP-X/-Y
'set' and HP-E submenus and list the contents of the respective sets
to see what they contain. Note how one may add or remove samples
from these sets.


    4.  Go to the "Samples"
menu. Then select "Choose named condition list of samples". This lets
you define new condition lists of samples.  Go to the "Edit" menu then "Sets of Conditions
(samples)" to see additional ways to manipulate these condition
sets. For example, you might define a new condition and the assign it
to the working HP-X 'set'.



    5.  Go to the "Samples"
menu. Then select "Choose ordered list of conditions". This lets you
define a new or edit an old Ordered Condition List(OCL).  In the
included MGAP there, there is a  pre-computed
example of an Ordered Condition List using 4 conditions of
replicates of C57B6 (pregnancy day 13, lactation days 1 and 10, and
stat5a(-,-) 15 samples. The database also includes 4 additional
condition sets of this data and an Ordered Condition List of the 4
conditions (in the State/ directory). This may be used to demo the OCL
F-test filter. Go to the "
Filter" menu and select "Filter by current Ordered Condition List
(OCL) F-test [p-Value]". This will popup a p-value slider where you
can adjust the criteria for selecting genes passing the F-test.

      



    6.  Go to the "GeneClass"
submenu in the "Analysis" menu. This is set of cascading menus that
may be used to change the default Gene Class. Different genes belong
to different gene classes and this is a way of sub-setting the data.
You may currently set it to All Genes, All Named Genes, ESTs similar
to genes, ESTs, Good genes, All named genes and ESTs, Replicate genes
(multiple copies on the array), Calibration DNA. Select "ESTs similar
to genes". Notice that the display now only shows the red (white)
circles on the ESTs similar to genes in the intensity (ratio)
pseudoarray image. Look at the circle overlays on the microarray image
- note how they changed. Go back and set the gene class to "All genes"
and then "Calibration DNA". The "Calibration DNA" is the set of spots
that may be used for normalizing data between microarrays. Check out
the "Set gene class subset" submenu. Leave the gene class set to All
Genes.


    7.  If you are connected to the Internet, go to the "View" Menu. Then turn on the
switch "Enable display current gene in popup XXXX Web Browser".
Depending on your database, XXXX may be GenBank, LocusID, UniGene,
dbEST or mAdb Clone DB.  Then click on a gene in the image. It pops up
a Web browser showing the genomic database Web page for that gene (if
any). If you click on a different spot, it will reuse the popup Web
browser with new data. If you don't want it to be active, go back to
the "View Menu" and click on "Enable display current gene ..." again
to disable it.


    8.  Go to the "Normalization" submenu in the
"Analysis" menu. This is used for normalizing data between microarrays
so they may be compared. The default is to scale the data to the
median value for each array. Investigate the other normalization
methods. If your quantified data contains spot background data, you
may also enable background correction that subtracts a microarray
specific overall background intensity value from each intensity value
in each array.


    9.  Go to the "Report"
submenu in the "Analysis" menu. You may generate a report in several
formats "Spreadsheet" or "tab-delimited", the latter being cut and
paste compatible with Excel. In the "Samples Report" sub-submenu in
the "Report" submenu, click on "Hybridized Samples", then switch to
the other Report format and do it again. Click on "Hybridized Sample
Web links". In the "Gene Reports" submenu, try "All named genes". Then
try the "Highest HP-X/HP-Y ratio" in the "Filtered gene reports"
submenu. If you are using a list of HP-E or HP-X/-Y 'sets' of samples,
you might try looking at the expression profile ratios or statistical
data respectively though other Reports.




    10.  Review the data "
Filter" submenu in the "Analysis" menu. Select the "Filter by
expression sliders" [I1:I2]. Expression is intensity in the case of
33P or biotin labeled, or (Cy3/Cy5) in the case of ratio
data. This pops up a state sliders window.  The I1 scroll bar (lower
limit of the gray value intensity) to about 100.  Look at the genes
that were eliminated because they are out of the range.  If you select
the "Filter by spot intensity sliders" [SI1:SI2], it will filter genes
by spot intensity in either F1 or F2 duplicate (if you have
duplicate spots), or the Cy3 or Cy5 spots. You can select which sets
of HPs to use (current HP, HP-X and HP-Y, HP-XY sets, or HP-E). You
might disable the [I1:I2] filter while you experiment with [Si1:SI2]
filter. If you have (F1,F2) or (Cy3,Cy5) data, put up the F1 vs F2 or
Cy3 vs Cy5 scatter plot and you can visualize how the thresholding
works.
      

In the Filter menu, add the "Filter by ratio or Zdiff sliders". Then
the [R1:R2] ratio range sliders are added to the state slider window
and may be used for filtering genes. If the normalization method is
one of the Zscore methods, it filters by the difference of the Zscores
otherwise by the ratio and the [Z1:Z2] range is used. Note that the
genes that pass the filter will appear to have a red (white) circle in
the pseudoarray intensity (ratio) grayscale (pseudocolor), or red "+"
in the scatter plots so you might try moving the controls while in
those plot modes. Try some of the other filters. The spot CV test
removes genes where replicate spot values (F1 and F2 in the case of a
single sample or replicate samples in the case of HP-X and HY-Y 'sets'
or the HP-E' list of genes) are not well correlated. The t-Test filter
may be used with sets of X and Y samples to find genes with a
p-value less than the specified threshold.



    11.  Go to the "Plot"
submenu in the "Analysis" menu. Then got to the "Show Microarray"
submenu, try pseudocolor ratio (or Zscore) modes and finally leave it
in the pseudograyscale image intensity mode. Try using the "Scatter
Plots" and "Histograms" in the Plot menu. Vary the normalization
methods and see how it affects the array image and scatter plots. When
you are in a scatter plot, click on a point. It will display data
similar to when clicking on an image. If UniGene data is available in
your database, Go to the Views menu and set "Enable display current
gene in popup UniGene Web browser" and click on a point again. This
pops up another Web browser window and lookup that gene in the  UniGene database.  Change
the threshold slider and notice how points appear and disappear. Click
on a bin in the ratio histogram, it will filter the genes so that only
the genes that have ratios in that bin are displayed.




    12.  Go to the "Plot" submenu in the "Analysis" Menu and then to
the "Expression
profile" submenu.  Then select the "Display gene expr. profile for
HP-E". It pops up a window with two buttons "Show HP names" and
"Close".  Then click on a gene in the image. It will draw the
expression profile for that gene.  Move the mouse so it is over one of
the vertical bars in the plot to get the data for that particular
HP. If you click on a different spot in the image it will display the
new expression profile in the same window.  Click on the "Show HP
names" button to popup a window with the list of HPs matching the
numbers in the expression profile plot. Now create a scatter plot of
two HPs and then click on a red "+" in the plot.  It will update the
expression profile plot with this gene.  If you want to compare
several expression profile plots, repeated create the "Display gene
expression..." windows from the View menu. Move the windows close to
each other so they are easier to compare. Only the last one you
created allows you to change the gene.  If you don't want a popup
window anymore, click on its "Close" button. You may save the scatter
plot as a GIF file by pressing the "SaveAs" button which will save it
in the Report subdirectory associated with the startup data..




    13.  Next we look at gene
clustering.  Go to the "Plot" submenu in the "Analysis" menu, and
then to the "Cluster plots" submenu, try "Cluster genes with
expression profiles similar to current gene".  This pops up a slider
with the cluster threshold. Then click on a gene in the image. It will
then popup a text window with the genes whose cluster distance is less
than the cluster threshold.  It is sorted by minimum cluster
distance. Notice that some genes in the image have different size blue
boxes around them. The larger the box, the smaller the cluster
distance and the more similar. Move the cluster threshold slider. This
will change the clustering as seen in both the image and in the
cluster popup window. Click on the "Report" button in the text
window. This will popup a report on these genes sorted by minimum
cluster distance.  Then click on the "EP plot".  This pops up a
scrollable list of expression profile plots for the genes you have
filtered by this test on so you can review the actual expression
profiles. Close this window and set the Filter to a small number of
genes such as with "ESTs similar to genes". Note that the genes
passing this filter are saved in the E.C.L. and may be saved as a gene
subset or part of the data Filter.


      
Turn on one or more Filters
to reduce the number of genes to say under 100 (e.g. t-test or spot CV
filters). Then press the "Go 'Cluster all genes'" button in the
cluster window. This is equivalent to invoking the "Cluster
counts of Filtered genes by expression profiles" command from the "Cluster plots" submenu.
Notice the Filtered genes has blue circles of different sizes.  The
larger the circle, the more genes there are that are similar to that
gene. Move the cluster threshold slider and note that the number of
similar genes changes, the size of the blue circles will change.  As
with the other cluster mode, you may generate a report of sorted
cluster counts. Click on a gene with the largest 
green circle. This will then switch you back to single gene
clustering mode where you can investigate that gene in more detail.


    14.  Next we look at  K-means gene
clustering.  Go to the "Plot" menu and in the "Cluster plots"
submenu, try 'Display K-means gene expression profiles for Filtered
HP-E' (similar to K-means clustering). This pops up a scroller for
"N-clusters", the number of clusters desired, with a default of 6
clusters. It will then popup a K-means report window with various
controls. The centers of the N clusters are indicated with magenta
circles where the size corresponds to the number of genes in the
cluster. If you click on a gene in the array, it will draw all members
of its cluster as green numbers (try this with the scatter plot
present as well). Press "EP plot" to scroll through a list of
expression profiles of the genes sorted by cluster. Press "Mean EP
plot" to scroll through the summary expression profiles of the
clusters. Press "Cluster-Report" and "Mn-Cluster-Report" to