Functional Analysis Talk

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

First order by measure of DE

Compare to list

Move through each gene 1

Move through each gene 2

Highest Level identified

Repetition of Process

Gene Set Enrichment Analysis

Gene Set Enrichment Analysis

[[File::gseawind.png]] 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