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MicroArray Explorer tool for Data Mining Gene Expression Patterns

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Welcome To MAExplorer
Microarray Explorer (MAExplorer) is a Java-based data-mining facility for cDNA or oligonucleiotide microarray databases. It may be downloaded and run as a stand-alone application on your computer. Its exploratory data analysis environment provides tools for the data-mining of quantitative expression profiles across multiple microarrays. Read the Introduction for an overview.

  • Analyzes data (after arrays have been scanned and spots quantified)
  • Handles multiple cDNA or oligo array samples with replicate spots
  • Manages replicate samples, named condition sets of samples, and lists of condition sets
  • Manages named subsets of genes
  • Handles intensity or ratio (Cy3/Cy5) quantified array data
  • Analyzes data for 2-conditions and N-condition expression profiles including ANOVA on any number of conditions of replicate samples
  • Data-filters gene sets by statistics, clustering, gene set membership
  • Provides direct data manipulation in graphics, spreadsheets and sample management
  • Accesses genomic Web servers from plots and reports
  • Converts your data using Cvt2Mae data conversion wizard
  • Both MAExplorer and Cvt2Mae are written in Java for portability with download installers making it easy to run on any system
  • Users may update these programs, once installed, by downloading just the Java jar file(s) using update commands.
  • May be extended using new analytic methods written as either
    1. Java plug-ins using the MAEPlugin Open Java API, or
    2. R statistics and graphics language plug-ins methods called RLOs using the RtestPlugin tool

MAExplorer reference manual We have a new open source project, Open2Dprot on It is an Open2Dprot project is a community effort to create an open source 2D protein expression data analysis system. It will be downloadable and could be used for data mining protein expression across sets of 2D data from research experiments. In the initial phase, modules will be created for 2D-dimensional data including 2D-PAGE (polyacrylamide gel electrophoresis) and initial support for 2D LC-MS and other data. In the second phase, it will be expanded to handle data from other 2D protein separation methods.
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