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Welcome To MAExplorer |
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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.
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MAExplorer:
- 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
- Java plug-ins using the
MAEPlugin Open Java API, or
- R statistics and graphics language plug-ins methods called
RLOs using the
RtestPlugin tool
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We have a new open source project, Open2Dprot on
open2dprot.sourceforge.net. 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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