Functional Analysis Talk

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Why do functional analysis?

  • Statistical significance is not the same as biological significance
  • Variety of methods available to compare functional annotation to gene data
  • General aim is to identify gene functions/categories that show an interesting expression profile
  • Potentially identify genes with smaller but biologically significant changes.

Functional analysis methods

  • Positional Gene Enrichment – looks for regions of chromosome showing changed expression.
  • The Ingenuity Pathways Analysis (IPA)
  • Several methods use Gene Ontology (GO) terms or other "gene sets"
  • GO is the means by which we identify function.
  • Gene Set Enrichment Analysis (GSEA)
  • GOAL: Gene Ontology AnaLyzer
  • GOrilla

A functional analysis method

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First order by measure of DE

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Compare to list

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Move through each gene 1

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Move through each gene 2

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Highest Level identified

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Repetition of Process

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Further shufflings

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Gene Set Enrichment Analysis

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  • Example GSEA report of a gene set found to be enriched among down regulated genes in cancer samples

Conclusions

  • A wide variety of methods are available for functional analysis of expression data
  • Aids biological interpretation of the data
  • Different types of annotation can be compared to expression data.
  • Many methods do not require user specified thresholds

Further reading

  • Eden et al "GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene" BMC Bioinformatics 2009
  • Volinia et al "GOAL: a software tool for assessing biological significance of genes groups" Nucleic Acids Res 2004
  • Tamayo, et al."Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles" PNAS 2005
  • Preter et al "Positional gene enrichment analysis of gene sets for high-resolution of overrepresented chromosomal regions" Nucleic Acid Research 2008
  • http://www.ingenuity.com/products/ipa