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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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Modular analysis of gene expression data with R.

Gábor Csárdi1, Zoltán Kutalik, Sven Bergmann

  • 1Department of Medical Genetics, University of Lausanne, Rue de Bugnon 27, CH-1005 Lausanne, Switzerland.

Bioinformatics (Oxford, England)
|April 8, 2010
PubMed
Summary
This summary is machine-generated.

The Iterative Signature Algorithm (ISA) simplifies complex gene expression data by identifying gene expression modules. New R packages, isa2 and eisa, offer optimized tools for ISA analysis and visualization.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Analyzing large biological datasets, such as gene expression profiles, necessitates advanced analytical tools.
  • The Iterative Signature Algorithm (ISA) is a biclustering method designed for data complexity reduction.

Purpose of the Study:

  • To introduce two GNU R software packages, isa2 and eisa, for implementing and utilizing the Iterative Signature Algorithm.
  • To provide users with optimized tools for ISA analysis, visualization, and biological interpretation of biclusters.

Main Methods:

  • Implementation of an optimized Iterative Signature Algorithm (ISA) in the isa2 R package.
  • Development of the eisa R package for user-friendly ISA execution, output visualization, and biological context integration.
  • Utilizing biclustering to identify subsets of genes with coherent expression patterns across subsets of experiments.

Main Results:

  • The isa2 package offers an optimized implementation of the ISA algorithm.
  • The eisa package provides a convenient interface for running ISA, visualizing results, and contextualizing biclusters.
  • These packages facilitate the decomposition of large datasets into biologically relevant modules.

Conclusions:

  • The isa2 and eisa packages offer powerful and accessible tools for researchers working with large-scale gene expression data.
  • These R packages enable the identification and interpretation of gene expression modules using the Iterative Signature Algorithm.
  • The tools are suitable for R and BioConductor users dealing with tabular biological data, particularly gene expression profiles.