Functional and PathwayAnalysis

Stewart MacArthur

Bioinformatics Core

March 18th, 2010

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 1 / 19

Introduction The Problem

The Problem• High-throughput genomics methods:

• microarrays• next generation sequencing

• Generate large lists of “interesting” genes

• How to we summarize?• What are the themes of the lists?

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 2 / 19

Introduction The Solution

The Solution

• Functional Analysis• Determine common

functions• Find groups of functionally

related genes• Pathways Analysis

• Determine commonpathways

• Determine potentialup/down stream regulators

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 3 / 19

Enrichment Analysis Methods

The Methods

Enrichment AnalysisAre there more of the genes in my list in functional category X than we couldexpect by chance?

• SEA - Singular Enrichment Analysis

• MEA - Modular Enrichment Analysis

• GSEA - Gene Set Enrichment Analysis

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 4 / 19

Enrichment Analysis Methods Hypergeometric

Brief Aside: Hypergeometric TestThe hypergeometric test calculates the probability that the number ofgenes in our gene list that are in functional category/pathway Xoccured by chance

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 5 / 19

Enrichment Analysis Methods Hypergeometric

Brief Aside: Hypergeometric TestThe hypergeometric test calculates the probability that the number ofgenes in our gene list that are in functional category/pathway Xoccured by chance

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 5 / 19

Enrichment Analysis Methods Hypergeometric

Brief Aside: Hypergeometric TestThe hypergeometric test calculates the probability that the number ofgenes in our gene list that are in functional category/pathway Xoccured by chance

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 5 / 19

Enrichment Analysis Methods Hypergeometric

Brief Aside: Hypergeometric TestThe hypergeometric test calculates the probability that the number ofgenes in our gene list that are in functional category/pathway Xoccured by chance

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 5 / 19

Enrichment Analysis Methods Hypergeometric

Brief Aside: Hypergeometric TestThe hypergeometric test calculates the probability that the number ofgenes in our gene list that are in functional category/pathway Xoccured by chance

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 5 / 19

Enrichment Analysis Methods Hypergeometric

Brief Aside: Hypergeometric TestThe hypergeometric test calculates the probability that the number ofgenes in our gene list that are in functional category/pathway Xoccured by chance

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 5 / 19

Enrichment Analysis Methods Hypergeometric

Brief Aside: Hypergeometric TestThe hypergeometric test calculates the probability that the number ofgenes in our gene list that are in functional category/pathway Xoccured by chance

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 5 / 19

Enrichment Analysis Methods Hypergeometric

Brief Aside: Hypergeometric TestThe hypergeometric test calculates the probability that the number ofgenes in our gene list that are in functional category/pathway Xoccured by chance

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 5 / 19

Enrichment Analysis Methods Hypergeometric

Brief Aside: Hypergeometric TestThe hypergeometric test calculates the probability that the number ofgenes in our gene list that are in functional category/pathway Xoccured by chance

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 5 / 19

Enrichment Analysis Methods SEA - Singular Enrichment Analysis

SEA - Singular Enrichment AnalysisInputs:

• List of “interesting” genes, e.g. DE genes• List of functional annotations e.g. GO annotations

Method:For each annotation

• Are more of the genes in our list present than would be expected bychance

• Calculate p-value

Next annotation

• Correction for multiple testing

Output:

• Ranked list of annotations

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 6 / 19

Enrichment Analysis Methods SEA - Singular Enrichment Analysis

SEA - Singular Enrichment AnalysisInputs:

• List of “interesting” genes, e.g. DE genes• List of functional annotations e.g. GO annotations

Method:For each annotation

• Are more of the genes in our list present than would be expected bychance

• Calculate p-value

Next annotation

• Correction for multiple testing

Output:

• Ranked list of annotations

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 6 / 19

Enrichment Analysis Methods SEA - Singular Enrichment Analysis

SEA - Singular Enrichment AnalysisInputs:

• List of “interesting” genes, e.g. DE genes• List of functional annotations e.g. GO annotations

Method:For each annotation

• Are more of the genes in our list present than would be expected bychance

• Calculate p-value

Next annotation

• Correction for multiple testing

Output:

• Ranked list of annotations

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 6 / 19

Enrichment Analysis Methods MEA - Modular Enrichment Analysis

MEA - Modular Enrichment Analysis

• Extension of SEA• Incorporates network discovery algorithms• Considers term-to-term relationships

• Terms not treated as separate tests• Uses co-occurrences of terms

• More closely related to biology• Based on assumption that related functional groups have similar

member genes

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 7 / 19

Enrichment Analysis Methods GSEA -Gene Set Enrichment Analysis

GSEA

No cutoff, uses all genes rankede.g. microarray experiment ranked by fold change or differential expressionFor each functional annotation

• Are genes randomly distributed in ranked list?

or

• Are genes distributed towards the top/bottom of the list?

• Calculate enrichment score (ES)

• Calculate significance of ES

Next annotation

• Correct for multiple testing

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 8 / 19

Enrichment Analysis Methods GSEA -Gene Set Enrichment Analysis

GSEA

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 9 / 19

Enrichment Analysis Methods GSEA -Gene Set Enrichment Analysis

GSEA

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 9 / 19

Enrichment Analysis Methods GSEA -Gene Set Enrichment Analysis

GSEA

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 9 / 19

Enrichment Analysis Methods GSEA -Gene Set Enrichment Analysis

GSEA

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 9 / 19

Annotation Resources

Annotation Resources

Where do the gene sets come from?• GO - Gene Ontology• KEGG - Kyoto Encyclopedia of Genes and Genomes• MSigDB - Molecular Signatures Database• Pathway Commons• ...• ...

Choice of annotation often dictated by choice of tool

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 10 / 19

Web based tools

Tools

• Approximately 68 enrichment tools

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Web based tools

Tools• Here they are:

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 11 / 19

Web based tools

Tools• Mainly Web based

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 11 / 19

Web based tools

Tools

• Mainly Hypergeometric based

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 11 / 19

Web based tools

Recommended Tools

• SEA - ClueGO, GOStat,• MEA - DAVID, GOToolBox• GSEA - GeneTrail, FatiScan (Babelomics)

See Bioinformatics Core Wiki Page for more toolshttp://criwiki.cancerresearchuk.org/

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 12 / 19

Web based tools David

DAVID http://david.abcc.ncifcrf.gov

The Database for Annotation, Visualization and Integrated Discovery

• Over 1,600 DAVID citations• 37 nature-branded citations to

date• Daily Usage: 1200 gene

lists/sublists• Daily Usage: 400 unique

researchers.

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 13 / 19

Web based tools David

DAVID http://david.abcc.ncifcrf.gov

The Database for Annotation, Visualization and Integrated Discovery

• Identify enriched biological themes

• Discover enriched functional-related gene groups

• Cluster redundant annotation terms

• Visualize genes on BioCarta & KEGG pathway maps

• Search for other functionally related genes not in the list

• Convert gene identifiers from one type to another.

• And more

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 14 / 19

Web based tools GeneTrail

GeneTrail

Annotations include• KEGG• TRANSPATH• TRANSFAC• GO

Methods:• Over-Representation Analysis (ORA)• Gene Set Enrichment Analysis (GSEA)

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 15 / 19

Commercial Tools

Ingenuity Pathways Analysis (IPA)

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 16 / 19

Commercial Tools

GeneGo MetaCore

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 17 / 19

Commercial Tools

Suraj - GeneGO Demo

Stewart MacArthur (Bioinformatics Core) Functional and Pathway Analysis March 18th, 2010 18 / 19

Cytoscape

Cytoscape

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