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Gene Set Enrichment Analysis

Gene Set Enrichment Analysis. Genome 559: Introduction to Statistical and Computational Genomics Elhanan Borenstein. A quick review. Gene expression profiling Which molecular processes/functions are involved in a certain phenotype (e.g., disease, stress response , etc.)

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Gene Set Enrichment Analysis

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  1. Gene SetEnrichment Analysis Genome 559: Introduction to Statistical and Computational Genomics Elhanan Borenstein

  2. A quick review • Gene expression profiling • Which molecular processes/functionsare involved in a certain phenotype (e.g., disease, stress response, etc.) • The Gene Ontology (GO) Project • Provides shared vocabulary/annotation • GO terms are linked in a complex structure • Enrichment analysis: • Find the “most” differentially expressed genes • Identify functional annotations that are over-represented • Modified Fisher's exact test

  3. A quick review:Modified Fisher's exact test • Genes/balls • Differentially expressed (DE) genes/balls • 10 out of 50 • 4 out of 8 • Do I have a surprisingly high number of blue genes? • Null model: the 8 genes/balls are selected randomly • … • 2 out of 8 • 1 out of 8 • 2 out of 8 • 5 out of 8 • 3 out of 8 • 4 out of 8 • 2 out of 8 • So, if you have 50 balls, 10 of them are blue, and you pick 8 balls randomly, what is the probability that k of them are blue?

  4. A quick review:Modified Fisher's exact test Hypergeometricdistribution 0.30 Probability 0.15 m=50, mt=10, n=8 0 0 1 2 3 4 5 6 7 8 k So … do I have a surprisinglyhigh number of blue genes? Can such high numbers (4 or above) occur by change? What is the probability of getting at least 4 blue genes in the null model? P(σt >=4)

  5. Enrichment Analysis ClassAClassB Biologicalfunction? Cutoff Genes ranked by expression correlation to Class A

  6. Enrichment Analysis ClassAClassB Biologicalfunction? Function 1(e.g., metabolism) Function 3(e.g., regulation) Function 2(e.g., signaling) 2 / 10 3 / 10 5 / 11 Cutoff Genes ranked by expression correlation to Class A

  7. Problems with cutoff-based analysis • After correcting for multiple hypotheses testing, no individual gene may meet the threshold due to noise. • Alternatively, one may be left with a long list of significant genes without any unifying biological theme. • The cutoff value is often arbitrary! • We are really examining only a handful of genes, totally ignoringmuch of the data

  8. Gene Set Enrichment Analysis • MIT, Broad Institute • V 2.0 available since Jan 2007 (Subramanian et al. PNAS. 2005.)

  9. GSEA key features • Calculates a score for the enrichment of a entire set of genes rather than single genes! • Does not require setting a cutoff! • Identifies the set of relevant genes as part of the analysis! • Provides a more robust statistical framework!

  10. Gene Set Enrichment Analysis ClassAClassB Biologicalfunction? Function 1(e.g., metabolism) Function 3(e.g., regulation) Function 2(e.g., signaling) 2 / 10 3 / 10 5 / 11 Cutoff Genes ranked by expression correlation to Class A

  11. Gene Set Enrichment Analysis ClassAClassB Function 1(e.g., metabolism) Function 3(e.g., regulation) Function 2(e.g., signaling) Genes ranked by expression correlation to Class A Running sum:Increase when gene is in setDecrease otherwise

  12. Gene Set Enrichment Analysis What would you expect if the hits were randomly distributed? What would you expect if most of the hits cluster at the top of the list?

  13. Gene Set Enrichment Analysis • Enrichment score (ES) = max deviation from 0 Running sum Leading Edge genes Genes within functional set (hits)

  14. Gene Set Enrichment Analysis Low ES (evenly distributed) ES = 0.43 ES = -0.45

  15. Gene Set Enrichment Analysis Ducrayet al.Molecular Cancer 2008 7:41

  16. GSEA Steps • Calculation of an enrichment score(ES) for each functional category • Estimation of significance level of the ES • An empirical permutation test • Phenotype labels are shuffled and the ES for this functional set is recomputed. Repeat 1000 times. • Generating a null distribution • Adjustment for multiple hypotheses testing • Necessary if comparing multiple gene sets (i.e.,functions) • Computes FDR (false discovery rate)

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