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Gene Ontology analysis in multiple gene clusters under multiple hypothesis testing framework.

Sheng Zhong1, Dan Xie

  • 1Department of Bioengineering, University of Illinois at Urbana Champaign, IL 61801, United States. szhong@uiuc.edu

Artificial Intelligence in Medicine
|October 5, 2007
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Summary
This summary is machine-generated.

GoSurfer software analyzes multiple gene clusters for enriched Gene Ontology (GO) terms, addressing challenges in hypothesis testing and cluster comparison for accurate functional analysis.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene Ontology (GO) is crucial for gene list functional analysis.
  • Existing tools face challenges in handling correlated multiple hypothesis testing and comparing GO enrichment across multiple gene clusters.
  • There's a need for robust statistical methods and software to address these limitations.

Purpose of the Study:

  • To develop a statistical procedure for analyzing Gene Ontology (GO) enrichment in multiple gene clusters.
  • To create a user-friendly software tool, GoSurfer, for applying this procedure.
  • To rigorously address multiple hypothesis testing and identify GO terms specific to individual gene clusters.

Main Methods:

  • Extended a previously developed two-group comparison statistical procedure to handle any number of gene lists/clusters.
  • Implemented the procedure into the GoSurfer software for user-friendly analysis.
  • Developed a novel false discovery rate (FDR) estimation that does not assume independence or complete null hypotheses, providing mildly conservative estimates.

Main Results:

  • GoSurfer software was developed and implemented for GO analysis in multiple gene clusters.
  • Applied to embryonic stem cell differentiation data, GoSurfer identified significant GO terms for different gene clusters (e.g., cell adhesion, muscle contraction, amino acid metabolism, RNA processing).
  • Demonstrated GoSurfer's ability to pinpoint specific functional enrichments within distinct gene expression patterns over time.

Conclusions:

  • GoSurfer provides a robust solution for analyzing GO enrichment across multiple gene clusters.
  • The software effectively identifies GO terms enriched in specific clusters while controlling for multiple testing.
  • GoSurfer is available at www.gosurfer.org for broader research application.