Class ClusterGenes

java.lang.Object
  |
  +--ClusterGenes

class ClusterGenes

extends java.lang.Object

Class to generate gene clusters from working data Filtered gene list. This class represents an individual ClusterGenes entry. The data structure is shared between the different clustering methods. Only one cluster method may be active at any one time.

This work was produced by Peter Lemkin of the National Cancer Institute, an agency of the United States Government. As a work of the United States Government there is no associated copyright. It is offered as open source software under the Mozilla Public License (version 1.1) subject to the limitations noted in the accompanying LEGAL file. This notice must be included with the code. The MAExplorer Mozilla and Legal files are available on http://maexplorer.sourceforge.net/.

Version:

$Date: 2003/07/07 21:40:41 $ $Revision: 1.8 $

Author:

P. Lemkin (NCI), G. Thornwall (SAIC), NCI-Frederick, Frederick, MD

See Also:

MAExplorer Home, Gene, GeneList, HierClustNode

Inner Class Summary

(package private) class

ClusterGenes.Cluster [FUTURE] Class Cluster holds an ordered list of genes for a particular cluster.

Field Summary

(package private) static boolean	activeClusterMethod active instance, GC others
(package private) int[]	bestKmeansForGene [0:nClist-1] best Kmeans node cluster in [1:maxKmeansNodes] for all genes in complexClusterCL
(package private) static GeneGeneDist	ccd link to global GeneGeneDist instance
(package private) float[]	ccDist1D Gene-Gene distance matrix is a lower-diagonal ccDist[i][j] that is equivalent to ccDist1D[addr1D(i,j)]
private static Config	cfg link to global Config instance
(package private) static GeneList	complexClusterCL ARG: list of genes to be clustered
(package private) static GeneList	curClusterCL ARG: current genes being clustered
(package private) static Gene	curGene current gene being clustered
(package private) static float	curGeneDistThr Threshold distance for current gene cluster
private static float[]	cv1Tmp EP vector for CV1 computations
private static float[]	cv1TmpEP EP vector for CV1 computations
private static float[]	cv2Tmp EP vector for CV2 computations
private static float[]	cv2TmpEP EP vector for CV2 computations
(package private) static int	DEF_NBR_RESAMPLE_RUNS Default number of resampling runs
(package private) static int	DEFAULT_N_KMEANS_NODES Default # of K-means nodes
(package private) static int	DEFAULT_N_SOM_NODES1 Default # of SOM map nodes
(package private) static int	DEFAULT_N_SOM_NODES2 Default # of SOM map nodes
private static Filter	fc link to global Filter instance
private static FileIO	fio link to global FileIO instance
private static GeneClass	gct link to global GeneClass instance
(package private) static ShowStringPopup	geneClustersPopup for updateable String Cluster Report if used
(package private) float[][]	geneEPvector normalized quantitation vector for all genes of size [0:nClist-1][0:nEPmsList-1] and also [0:nObj-1][0:nDataVect-1]
(package private) static HierClustNode	hcn hierarchical clustering tree
(package private) static ShowPlotPopup	hierClusterGramPopup plot popup for ClusterGram display for hierarchical clustering
(package private) float[][]	hpDataMnA opt Mean HP-E quant data [nKmeansNodes][nEPmsList]
(package private) int[]	hpDataNbrA opt # genes/K-means-cluster for nEPmsList HP-Es [nKmeansNodes]
(package private) float[][]	hpDataSDA opt S.D.
(package private) Gene	initialSeedGene initial seed gene - either 1st or current Obj gene
(package private) static ShowExprProfilesPopup	KmeansClustersExprProfPopup
(package private) static ShowStringPopup	KmeansClustersPopup string report exists if not null
(package private) float[]	kMeansDist [1:maxKmeansNodes] distance between K-means primary clusters
(package private) int[]	kMeansList [1:maxKmeansNodes] index of gene as K-means cluster node
(package private) float[]	kMeansMaxDist [1:maxKmeansNodes] max distance within each K-means primary clusters
private static MAExplorer	mae link to global MAExplorer instance
(package private) static int	maxGenesForKmeansClustering max # filtered genes to do K-means clustering
(package private) static int	maxGenesInCurCluster max # filtered genes to do curClusterCL clustering
(package private) float	maxGlobalDist maximum global distance seen for all distances
(package private) int	maxKmeansNodes max # of K-means primary nodes will be set by thresholding slider
(package private) static int	maxNgeneClusterCnt max # of clusters found
(package private) float[][]	meanClusterVector mean normalized quantitation vector for K-means nodes [1:nKmeansNodes][0:nMSList-1]
(package private) int	method Current clustering method.
(package private) static int	METHOD_CLUSTER_KMEANS Method: find most orthogonal clusters and then subset rest of genes to those.
(package private) static int	METHOD_HIERARCHICAL_CLUSTERING Method: Numerical Taxonomy, Sneath&Sokol, 1973, W.H.
(package private) static int	METHOD_SIMILAR_GENE_COUNTS_CLUSTERING Method: Numerical Taxonomy, Sneath&Sokol, 1973, W.H.
(package private) static int	METHOD_SIMILAR_GENES_CLUSTERING Method: Numerical Taxonomy, Sneath&Sokol, 1973, W.H.
(package private) static int	METHOD_SOM_CLUSTERING Method: SOM - Self-Organizing-Maps, Tamayo (1999) PNAS USA 96:2907-2912
(package private) GeneList	mnClustersCL artificial mean K-means gene list
(package private) float[]	mnWithinClusterDist [1:maxKmeansNodes] mean within-cluster distance between K-means clusters
(package private) MaHybridSample[]	msEPlist list of expression profile samples [1:nEPmsList] where genes are to be checked normally this is mae.msListE[1:mae.nHP_E].
(package private) int	nClist i.e size of complexClusterCL.length
(package private) int[][]	nearestGenesIdx [0:nClist-1] [0:nClist-1] sorted index of nearest genes
(package private) int	nEPmsList size of geneEPvector[][0:nMSList-1]
(package private) int	nKmeansNodes # of unique K-means nodes found
(package private) static int	prevMethod previous clustering method.
(package private) float[]	sdWithinClusterDist [1:maxKmeansNodes] StdDev within-cluster distance between K-means clusters
(package private) static int	UPDATE bit property: cause PopupRegistry to force a update of a cluster window

Constructor Summary

(package private)	ClusterGenes(GeneList complexClusterCL, MaHybridSample[] msEPlist, int nEPmsList) ClusterGenes() - constructor to create instance of new cluster method
(package private)	ClusterGenes(MAExplorer mae) ClusterGenes() - constructor to create instance of new cluster method

Method Summary

private int	addr1D(int x, int y) addr1D() - lookup lower-diagonal addr1D(x,y) [y' + x'*(x'+1)/2] where:
private void	assignGeneListToBestKmeansNodes(boolean useMeanClusterVectors) assignGeneListToBestKmeansNodes() - assign genes for all complexClusterCL genes to the best N primary Nodes clusters.
private float	calcMeanClusterClusterDist(int n, int m) calcMeanClusterClusterDist() - m,n cluster-cluster Euclidean distances.
private void	calcMeanClusterVectors() calcMeanClusterVectors() - compute meanClusterVector Kmeans nodes.
private void	calcMedianClusterVectors() calcMedianClusterVectors() - compute meanClusterVector as K-median nodes.
private void	calcNearestGenes() calcNearestGenes() - compute rank ordered sorted list of nearest genes
(package private) static void	chkIfAbort() chkIfAbort() - check if abort, then shut down clustering.
(package private) static void	cleanup() cleanup() - close up what needs to be closed and GC all structures.
private float	computeGeneToMeanClusterDistance(int fk, int n) computeGeneToMeanClusterDistance() - gene to Mn-Cluster Euclidean distances
(package private) static boolean	copyCurClusterToEditedGeneList() copyCurClusterToEditedGeneList() - set EGL to K-means curCluster genes for the most similar genes.
(package private) boolean	createClusterMethod(GeneList complexClusterCL, int nbrOfClusters, MaHybridSample[] msEPlist, int nEPmsList, int method, boolean resetFlag) createClusterMethod() - create instance of new cluster method
private boolean	createKmeansNodesPartition(int maxKmeansNodes) createKmeansNodesPartition() - setup primary K-means nodes partition.
(package private) void	createSimilarGeneCountsClusters() createSimilarGeneCountsClusters() - create similar gene counts clusters
(package private) void	createSimilarGenedCluster() createSimilarGenedCluster() - create cluster of similar genes
(package private) static void	findAllGeneClusterCounts(MAExplorer maE, float clusterDistThr) findAllGeneClusterCounts() - # genes such that geneDist LEQ geneDistThr.
(package private) static int	findClustersOfGene(MAExplorer maE, Gene geneToTest, float clusterDistThr, boolean doSortFlag) findClustersOfGene() - find all HP-E gene clusters LT clusterDist.
private int[]	getSortedListOfNearestGenesIdx(int cIdx) getSortedListOfNearestGenesIdx()- get sorted list of nearest genes[] of cIdx.
(package private) void	initData() initData() - init data
private boolean	reassignKmeansNodes() reassignKmeansNodes() - reassign nodes based on closest to new mean clusters.
(package private) boolean	removePreviousClusterMethod(int method) removePreviousClusterMethod() - remove any popups and data structures from previous cluster method if it was active.
private void	reportHierClusterTree() reportHierClusterTree() - popup report of hierarchical clusters tree.
private void	reportKmeansNodes() reportKmeansNodes() - popup expression profile report of K-means nodes
(package private) boolean	saveKmeansClustersAsGeneSets() saveKmeansClustersAsGeneSets() - save K-means clusters in named Gene BitSets.
(package private) boolean	setupKmeansHPstats() setupKmeansHPstats() - compute summary K-means node HP statistics for each HP sample in each Node and assign mnClustersCL to the nKmeansNodes primary nodes.
(package private) static void	showReducedFilteredGenesMsg(java.lang.String optMsg2, java.lang.String optMsg3, int maxAllowed) showReducedFilteredGenesMsg() - show 'reduce the# of filtered genes' msg
(package private) void	sortKmeansClusterList(GeneList srcCL, GeneList dstCL) sortKmeansClusterList() - sort K-means srcCL to dstCL by Node number.
private void	sortNearestGenes(int c) sortNearestGenes() - sort nearest gene list for gene # c by min.
(package private) static void	updateAllClustersGeneReport() updateAllClustersGeneReport() - update all filtered clusters gene report
(package private) static void	updateGeneClustersOfCurrentGeneReport() updateGeneClustersOfCurrentGeneReport() - update current gene cluster report
private void	updateHierarchicalClusters() updateHierarchicalClusters() - create or update Hierarchical clusters
(package private) void	updateKmeansClusters() updateKmeansClusters() - update an existing Kmeans cluster window
(package private) void	updateSimilarGenesListCounts() updateSimilarGenesListCounts() - this updates the cluster counts for this cycle of clustering.
private void	updateSOMclusters() updateSOMclusters() - [FUTURE] create or update Self Organizing Maps clusters.
(package private) boolean	withinClusterKmeansStatistics() withinClusterKmeansStatistics() - compute mean & StdDev within cluster stats

Methods inherited from class java.lang.Object

, clone, equals, finalize, getClass, hashCode, notify, notifyAll, registerNatives, toString, wait, wait, wait

Field Detail

mae

private static MAExplorer mae

link to global MAExplorer instance

fc

private static Filter fc

link to global Filter instance

fio

private static FileIO fio

link to global FileIO instance

cfg

private static Config cfg

link to global Config instance

ccd

static GeneGeneDist ccd

link to global GeneGeneDist instance

gct

private static GeneClass gct

link to global GeneClass instance

activeClusterMethod

static boolean activeClusterMethod

active instance, GC others

METHOD_SOM_CLUSTERING

static final int METHOD_SOM_CLUSTERING

Method: SOM - Self-Organizing-Maps, Tamayo (1999) PNAS USA 96:2907-2912

METHOD_CLUSTER_KMEANS

static final int METHOD_CLUSTER_KMEANS

Method: find most orthogonal clusters and then subset rest of genes to those. K-means has N Primary Nodes

METHOD_HIERARCHICAL_CLUSTERING

static final int METHOD_HIERARCHICAL_CLUSTERING

Method: Numerical Taxonomy, Sneath&Sokol, 1973, W.H. Freeman Co.

METHOD_SIMILAR_GENES_CLUSTERING

static final int METHOD_SIMILAR_GENES_CLUSTERING

Method: Numerical Taxonomy, Sneath&Sokol, 1973, W.H. Freeman Co.

METHOD_SIMILAR_GENE_COUNTS_CLUSTERING

static final int METHOD_SIMILAR_GENE_COUNTS_CLUSTERING

Method: Numerical Taxonomy, Sneath&Sokol, 1973, W.H. Freeman Co.

UPDATE

static final int UPDATE

bit property: cause PopupRegistry to force a update of a cluster window

DEFAULT_N_SOM_NODES1

static final int DEFAULT_N_SOM_NODES1

Default # of SOM map nodes

DEFAULT_N_SOM_NODES2

static final int DEFAULT_N_SOM_NODES2

Default # of SOM map nodes

DEFAULT_N_KMEANS_NODES

static final int DEFAULT_N_KMEANS_NODES

Default # of K-means nodes

DEF_NBR_RESAMPLE_RUNS

static int DEF_NBR_RESAMPLE_RUNS

Default number of resampling runs

method

int method

Current clustering method. Note: there may possibly be multiple cluster methods if for example we used some for the data filter as well as the popup cluster windows.

prevMethod

static int prevMethod

previous clustering method. We at most remove one previous cluster method popups.

maxGenesInCurCluster

static int maxGenesInCurCluster

max # filtered genes to do curClusterCL clustering

curClusterCL

static GeneList curClusterCL

ARG: current genes being clustered

curGene

static Gene curGene

current gene being clustered

curGeneDistThr

static float curGeneDistThr

Threshold distance for current gene cluster

complexClusterCL

static GeneList complexClusterCL

ARG: list of genes to be clustered

nClist

int nClist

i.e size of complexClusterCL.length

msEPlist

MaHybridSample[] msEPlist

list of expression profile samples [1:nEPmsList] where genes are to be checked normally this is mae.msListE[1:mae.nHP_E].

nEPmsList

int nEPmsList

size of geneEPvector[][0:nMSList-1]

cv1Tmp

private static float[] cv1Tmp

EP vector for CV1 computations

cv2Tmp

private static float[] cv2Tmp

EP vector for CV2 computations

cv1TmpEP

private static float[] cv1TmpEP

EP vector for CV1 computations

cv2TmpEP

private static float[] cv2TmpEP

EP vector for CV2 computations

geneEPvector

float[][] geneEPvector

normalized quantitation vector for all genes of size [0:nClist-1][0:nEPmsList-1] and also [0:nObj-1][0:nDataVect-1]

ccDist1D

float[] ccDist1D

 Gene-Gene distance matrix is a lower-diagonal ccDist[i][j]
 that is equivalent to ccDist1D[addr1D(i,j)]
 where:
     i' = (i>j) ? i : j
    j' = (i>j) ? j : i
    addr1D(i,j)= j' + i'*(i'+1)/2

 This computes LSQ euclidean distance of the gene pattern vector
 from another gene using the current distance metric.

geneClustersPopup

static ShowStringPopup geneClustersPopup

for updateable String Cluster Report if used

hcn

static HierClustNode hcn

hierarchical clustering tree

hierClusterGramPopup

static ShowPlotPopup hierClusterGramPopup

plot popup for ClusterGram display for hierarchical clustering

nearestGenesIdx

int[][] nearestGenesIdx

[0:nClist-1] [0:nClist-1] sorted index of nearest genes

maxGenesForKmeansClustering

static int maxGenesForKmeansClustering

max # filtered genes to do K-means clustering

maxNgeneClusterCnt

static int maxNgeneClusterCnt

max # of clusters found

initialSeedGene

Gene initialSeedGene

initial seed gene - either 1st or current Obj gene

bestKmeansForGene

int[] bestKmeansForGene

[0:nClist-1] best Kmeans node cluster in [1:maxKmeansNodes] for all genes in complexClusterCL

maxGlobalDist

float maxGlobalDist

maximum global distance seen for all distances

meanClusterVector

float[][] meanClusterVector

mean normalized quantitation vector for K-means nodes [1:nKmeansNodes][0:nMSList-1]

kMeansDist

float[] kMeansDist

[1:maxKmeansNodes] distance between K-means primary clusters

kMeansMaxDist

float[] kMeansMaxDist

[1:maxKmeansNodes] max distance within each K-means primary clusters

mnWithinClusterDist

float[] mnWithinClusterDist

[1:maxKmeansNodes] mean within-cluster distance between K-means clusters

sdWithinClusterDist

float[] sdWithinClusterDist

[1:maxKmeansNodes] StdDev within-cluster distance between K-means clusters

kMeansList

int[] kMeansList

[1:maxKmeansNodes] index of gene as K-means cluster node

mnClustersCL

GeneList mnClustersCL

artificial mean K-means gene list

hpDataNbrA

int[] hpDataNbrA

opt # genes/K-means-cluster for nEPmsList HP-Es [nKmeansNodes]

hpDataMnA

float[][] hpDataMnA

opt Mean HP-E quant data [nKmeansNodes][nEPmsList]

hpDataSDA

float[][] hpDataSDA

opt S.D. HP-E quant data [nKmeansNodes][nEPmsList]

nKmeansNodes

int nKmeansNodes

# of unique K-means nodes found

maxKmeansNodes

int maxKmeansNodes

max # of K-means primary nodes will be set by thresholding slider

KmeansClustersExprProfPopup

static ShowExprProfilesPopup KmeansClustersExprProfPopup

KmeansClustersPopup

static ShowStringPopup KmeansClustersPopup

string report exists if not null

Constructor Detail

ClusterGenes

ClusterGenes(MAExplorer mae)

ClusterGenes() - constructor to create instance of new cluster method

Parameters:

mae - is the MAExplorer instance

ClusterGenes

ClusterGenes(GeneList complexClusterCL,
             MaHybridSample[] msEPlist,
             int nEPmsList)

ClusterGenes() - constructor to create instance of new cluster method

Parameters:

complexClusterCL - is the list of genes to cluster by sample

msEPlist - is the list of sample HP's to cluster in the expr vector

nEPmsList - is the # HP's to cluster

Method Detail

createClusterMethod

boolean createClusterMethod(GeneList complexClusterCL,
                            int nbrOfClusters,
                            MaHybridSample[] msEPlist,
                            int nEPmsList,
                            int method,
                            boolean resetFlag)

createClusterMethod() - create instance of new cluster method

Parameters:

complexClusterCL - is the list of genes to cluster by sample

nbrOfClusters - is the # of clusters to find for K-means

msEPlist - is the list of sample HP's to cluster in the expr vector

nEPmsList - is the # HP's to cluster

method - is the clusterMETHOD_xxx to use

resetFlag - is true if reset the data structures.

Returns:

true if succeed.

See Also:

MenuBarFrame.setSTOPbuttonState(boolean, boolean), showReducedFilteredGenesMsg(java.lang.String, java.lang.String, int), updateHierarchicalClusters(), updateKmeansClusters(), updateSOMclusters()

initData

void initData()

initData() - init data

removePreviousClusterMethod

boolean removePreviousClusterMethod(int method)

removePreviousClusterMethod() - remove any popups and data structures from previous cluster method if it was active. It will not remove the previous method (prevMethod) if it is the same as the new method. Note: a prevMethod value of 0 indicates that there was no previous method. The activeClusterMethod flag is set when the previous method is active.

Parameters:

method - is the new method. If it is 0, then just remove the previous method.

Returns:

true if remove a previous cluster method

createSimilarGenedCluster

void createSimilarGenedCluster()

createSimilarGenedCluster() - create cluster of similar genes

createSimilarGeneCountsClusters

void createSimilarGeneCountsClusters()

createSimilarGeneCountsClusters() - create similar gene counts clusters

updateHierarchicalClusters

private void updateHierarchicalClusters()

updateHierarchicalClusters() - create or update Hierarchical clusters

See Also:

EventMenu.setClusterDisplayState(java.awt.CheckboxMenuItem, boolean), GeneGeneDist.calcNormGeneVectors(GeneList, int), HierClustNode, HierClustNode.calcHierCluster(float[][], java.lang.String[], int[], int), Util.showMsg3(java.lang.String), chkIfAbort(), reportHierClusterTree()

updateSOMclusters

private void updateSOMclusters()

updateSOMclusters() - [FUTURE] create or update Self Organizing Maps clusters. [TODO] migrate SOM code into a MAEPlugin.

See Also:

GeneGeneDist.calcGeneGeneDists(float[], int, int), GeneGeneDist.calcNormGeneVectors(GeneList, int), calcNearestGenes()

updateKmeansClusters

void updateKmeansClusters()

updateKmeansClusters() - update an existing Kmeans cluster window

See Also:

Filter.showNbrFilteredGenes(), GeneGeneDist.calcNormGeneVectors(GeneList, int), GeneGeneDist.calcGeneGeneDists(float[], int, int), Component.repaint(), Util.showMsg3(java.lang.String), assignGeneListToBestKmeansNodes(boolean), calcMeanClusterVectors(), calcMedianClusterVectors(), createKmeansNodesPartition(int), reassignKmeansNodes(), reportKmeansNodes(), setupKmeansHPstats(), withinClusterKmeansStatistics()

updateSimilarGenesListCounts

void updateSimilarGenesListCounts()

updateSimilarGenesListCounts() - this updates the cluster counts for this cycle of clustering. Check all genes in the same cluster and add them to the similar genes list for each gene in the cluster. Do this for all clusters.

addr1D

private final int addr1D(int x,
                         int y)

addr1D() - lookup lower-diagonal addr1D(x,y) [y' + x'*(x'+1)/2] where:

    x' = (x>y) ? x : y,
    y' = (x>y) ? y : x.

Return the computed 1D address for the 2D (x,y) address.

showReducedFilteredGenesMsg

static void showReducedFilteredGenesMsg(java.lang.String optMsg2,
                                        java.lang.String optMsg3,
                                        int maxAllowed)

showReducedFilteredGenesMsg() - show 'reduce the# of filtered genes' msg

Parameters:

optMsg2 - is cluster message for msg2

optMsg3 - is cluster message for msg2

maxAllowed - is # of genes that can be clustered max (if a problem)

See Also:

Filter.showNbrFilteredGenes(), StateScrollers.regenerateScrollers(boolean), Util.showMsg2(java.lang.String), Util.showMsg3(java.lang.String), Util.sleepMsec(int)

calcMeanClusterClusterDist

private float calcMeanClusterClusterDist(int n,
                                         int m)

calcMeanClusterClusterDist() - m,n cluster-cluster Euclidean distances.

Parameters:

n - is cluster n

m - is cluster m

Returns:

distance between mean cluster vectors for clusters n and m

computeGeneToMeanClusterDistance

private float computeGeneToMeanClusterDistance(int fk,
                                               int n)

computeGeneToMeanClusterDistance() - gene to Mn-Cluster Euclidean distances

Parameters:

fk - is index of gene

n - is cluster n

Returns:

distance between gene fk and mean cluster vector for cluster n.

sortNearestGenes

private void sortNearestGenes(int c)

sortNearestGenes() - sort nearest gene list for gene # c by min. distance. The genes are sorted by minimum geneGeneDist[c][] from nearest to furthest. NOTE: This is NOT a symmetric array!

Parameters:

c - is gene number

See Also:

addr1D(int, int)

calcNearestGenes

private void calcNearestGenes()

calcNearestGenes() - compute rank ordered sorted list of nearest genes

See Also:

sortNearestGenes(int)

getSortedListOfNearestGenesIdx

private int[] getSortedListOfNearestGenesIdx(int cIdx)

getSortedListOfNearestGenesIdx()- get sorted list of nearest genes[] of cIdx. The list should be sorted already.

Parameters:

cIdx - is gene index of getSortedListOfNearestGenes

Returns:

list of nearest genes indices

findAllGeneClusterCounts

static void findAllGeneClusterCounts(MAExplorer maE,
                                     float clusterDistThr)

findAllGeneClusterCounts() - # genes such that geneDist LEQ geneDistThr. The genes being tested are in the fc.workingCL.

Parameters:

maE - is the MAExplorer instance

clusterDistThr - is threshold distance to set

See Also:

Filter.showNbrFilteredGenes(), MenuBarFrame.setSTOPbuttonState(boolean, boolean), Util.showMsg3(java.lang.String), Gene.setGeneData(float), Gene.sortGeneList(Gene[], int, boolean), findClustersOfGene(MAExplorer, Gene, float, boolean), showReducedFilteredGenesMsg(java.lang.String, java.lang.String, int), updateAllClustersGeneReport()

findClustersOfGene

static int findClustersOfGene(MAExplorer maE,
                              Gene geneToTest,
                              float clusterDistThr,
                              boolean doSortFlag)

findClustersOfGene() - find all HP-E gene clusters LT clusterDist. Save results in curClusterCL. Also copy curClusterCL to the E.C.L as a side effect.

Parameters:

maE - is the MAExplorer instance

geneToTest - is the gene to test

clusterDistThr - is threshold distance to set

doSortFlag -

Returns:

the number of genes found

See Also:

Gene.setGeneDist(float), Gene.sortGeneList(Gene[], int, boolean), GeneList.copy(GeneList, GeneList), GeneGeneDist.clusterDistance(Gene, Gene), GeneList, GeneList.addGene(Gene), GeneList.copy(GeneList, GeneList)

updateGeneClustersOfCurrentGeneReport

static void updateGeneClustersOfCurrentGeneReport()

updateGeneClustersOfCurrentGeneReport() - update current gene cluster report

See Also:

Gene, MAExplorer.logDRYROTerr(java.lang.String), ShowStringPopup.updateText(java.lang.String), Util.cvf2s(float, int), Util.cvtValueToStars(float, float, int, boolean)

updateAllClustersGeneReport

static void updateAllClustersGeneReport()

updateAllClustersGeneReport() - update all filtered clusters gene report

See Also:

Gene, Gene.setGeneData(float), Gene.sortGeneList(Gene[], int, boolean), MAExplorer.logDRYROTerr(java.lang.String), ShowStringPopup.updateText(java.lang.String)

createKmeansNodesPartition

private boolean createKmeansNodesPartition(int maxKmeansNodes)

createKmeansNodesPartition() - setup primary K-means nodes partition. First find most orthogonal clusters and then assign rest of genes to these sets by picking the first gene idx (i.e. current gene) for node [0]. Then put next furthest gene idx in [1], etc. until fill up the kMeansDist[maxKmeansNodes]. The actual count is nKmeansNodes.

Parameters:

maxKmeansNodes - is the number of K means nodes to use

Returns:

true if successful.

See Also:

CompositeDatabase.setObjCoordFromMID(int, java.lang.Object), Gene, GeneGeneDist.findGeneWithLeastSumDistances(float[], int), GeneList, GeneList.clear(), GeneList.addGene(Gene), Util.showMsg2(java.lang.String), addr1D(int, int)

reassignKmeansNodes

private boolean reassignKmeansNodes()

reassignKmeansNodes() - reassign nodes based on closest to new mean clusters. The actual count is nKmeansNodes.

Returns:

true if successful.

See Also:

computeGeneToMeanClusterDistance(int, int)

calcMeanClusterVectors

private void calcMeanClusterVectors()

calcMeanClusterVectors() - compute meanClusterVector Kmeans nodes. Note: the mean vectors are saved in the global variable meanClusterVecto[maxKmeansNodes+1][nEPmsList]

calcMedianClusterVectors

private void calcMedianClusterVectors()

calcMedianClusterVectors() - compute meanClusterVector as K-median nodes. Note: the median vectors are saved in the global variable meanClusterVecto[maxKmeansNodes+1][nEPmsList]

See Also:

Statistics.calcHistStats(java.lang.String, int, float[], int)

assignGeneListToBestKmeansNodes

private void assignGeneListToBestKmeansNodes(boolean useMeanClusterVectors)

assignGeneListToBestKmeansNodes() - assign genes for all complexClusterCL genes to the best N primary Nodes clusters.

Parameters:

useMeanClusterVectors - to use mean cluster vectors else vector of current centroid gene.

See Also:

calcMeanClusterClusterDist(int, int), Gene.clearGeneProperty(int), Gene.isGeneProperty(int), Gene.setGeneProperty(int), addr1D(int, int), computeGeneToMeanClusterDistance(int, int)

sortKmeansClusterList

void sortKmeansClusterList(GeneList srcCL,
                           GeneList dstCL)

sortKmeansClusterList() - sort K-means srcCL to dstCL by Node number. Generate the cluster table in dstCL from srcCL. foreach n in Kmeans

   1. Put Kmeans node first.
   2. Sort members of Kmeans n clusters by distance to cluster centroid.
   3. Then output members of that Kmeans set.
 Generate and ordered newCL which will then be used
 to make the sorted Reports.

Note: requires that the genes in srcCL be assigned to the best K-means node.

Parameters:

srcCL - is the gene list to sort (source)

dstCL - is the resulting sorted gene list (destination)

See Also:

GeneList, GeneList.addGene(Gene), GeneList.clear(), GeneList.clearNull(), Gene.sortGeneList(Gene[], int, boolean)

withinClusterKmeansStatistics

boolean withinClusterKmeansStatistics()

withinClusterKmeansStatistics() - compute mean & StdDev within cluster stats

Returns:

true if successful.

saveKmeansClustersAsGeneSets

boolean saveKmeansClustersAsGeneSets()

saveKmeansClustersAsGeneSets() - save K-means clusters in named Gene BitSets. The named sets are "Cluster #i" where i is 1 to nKmeansNodes.

Returns:

true if succeed.

See Also:

GeneBitSet.addItem(int), GeneBitSet.clearNull(), GeneBitSet.lookupOrMakeNewGeneBitSet(java.lang.String, GeneBitSet)

reportKmeansNodes

private void reportKmeansNodes()

reportKmeansNodes() - popup expression profile report of K-means nodes

See Also:

Component.repaint(), Gene.isGeneProperty(int), ShowStringPopup, ShowStringPopup.updateText(java.lang.String), Util.cvtValueToStars(float, float, int, boolean), sortKmeansClusterList(GeneList, GeneList)

reportHierClusterTree

private void reportHierClusterTree()

reportHierClusterTree() - popup report of hierarchical clusters tree. This includes the expression profiles.

See Also:

GeneList, GeneList.addGene(Gene), GeneList.clear(), HierClustNode, HierClustNode.enumerateLeafNodes(), HierClustNode.setEnumerateNodes(), Util.showMsg(java.lang.String), Util.showMsg2(java.lang.String), ShowPlotPopup

copyCurClusterToEditedGeneList

static boolean copyCurClusterToEditedGeneList()

copyCurClusterToEditedGeneList() - set EGL to K-means curCluster genes for the most similar genes.

Returns:

true if succeed

See Also:

GeneList.addGene(Gene), GeneList.clear()

setupKmeansHPstats

boolean setupKmeansHPstats()

setupKmeansHPstats() - compute summary K-means node HP statistics for each HP sample in each Node and assign mnClustersCL to the nKmeansNodes primary nodes.

Returns:

true if successful.

See Also:

Gene, Gene.isGeneProperty(int), GeneList, GeneList.addGene(Gene)

chkIfAbort

static void chkIfAbort()

chkIfAbort() - check if abort, then shut down clustering. [TODO] buggy - needs to be put on separate thread.

See Also:

EventMenu.setClusterDisplayState(java.awt.CheckboxMenuItem, boolean), Component.repaint(), MenuBarFrame.setSTOPbuttonState(boolean, boolean), Util.showMsg3(java.lang.String)

cleanup

static void cleanup()

cleanup() - close up what needs to be closed and GC all structures. Reset to initial state so can restart it...

See Also:

MenuBarFrame.setSTOPbuttonState(boolean, boolean)