Gene Set Analysis using R and Bioconductor Daniel Gusenleitner

Why Gene Sets? Phenotypic characteristics or clinical diseases can only rarely be defined by one single gene Most diseases, are complex and involve multiple genes Genes usually do not work independently; they work as parts of a functional unit

Genes and Proteins talk in Pathways

Definition of Gene Sets Gene sets are loosely defined as groups of genes that share biological mechanisms or characteristic They represent the distilled base of biological knowledge and act as an aid for theoretical and experimental research

There are different kinds of gene sets Data-driven gene sets usually use high-throughput experiments in order to derive and identify sets of related genes. Knowledge-driven gene sets require expert knowledge to construct gene sets. These are usually specific to domains of interest.

Resources for Gene Sets

2. Extract gene signatures from tables figures or supplement 1. SearchPubmedwith pre- defined search criteria 3. Annotate each gene signature4. Map allmappableidentifiers to genome to create standardized gene lists NameDescription PMIDPubmedidentifier TissueName of search term set used to searchPubMED. OrganismSpecies common name (human, mouse, etc) Platform Name of microarray or other experimental technique used to derive gene signature Platform Description Description of platform Genes Articles Number of genes in gene signature Sig ID Signature identifier, in the format PMID-XXX, where XXX is the gene signature table, figure or supplementary file e.g Table3 Sig Name Name of gene signature, in the formatTissue_AuthorYear_ NumberofGenes_Description. Description is optional. e.g. Breast_Bertucci08_75genes Sig Description Description of gene signature, typically extracted from table or figure legend (free text) File Associated Name of tab delimited file gene signature file. Format is SigID.txt URLURL from where gene signature was downloaded Column Mappings Content of each column in gene signature file (selection from constrained list in Table 1b)

initiative to unify the representation of gene and gene product attributes across all species Maintains a controlled vocabulary of gene and gene product attributes Provides an annotation for genes and gene products Provides tools for easy access to all aspects of the data provided by the project

cellular components, the parts of a cell or its extracellular environment molecular function, the elemental activities of a gene product at the molecular level, such as binding or catalysis biological process, operations or sets of molecular events with a defined beginning and end, pertinent to the functioning of integrated living units

connects known information on molecular interaction networks It contains: genes and proteins biochemical compounds and reactions pathways and complexes

Version 3.0 (September 2010) Warehouse of 6769 annotated gene sets

Divided in 5 major collections: – C1: positional gene sets for each human chromosome and cytogenetic band (326 gene sets) – C2: curated gene sets from online pathway databases, publications in PubMed, and knowledge of domain experts (3272 gene sets) – C3: motif gene sets based on conserved cis-regulatory motifs (836 gene sets) – C4: computational gene sets defined by mining large collections of cancer-oriented microarray data (881 gene sets) – C5: GO gene sets consists of genes annotated by the same GO terms (1454 gene sets)

Gene Set Analysis

Statistical Methods Fisher’s Exact Test EASE: the Expression Analysis Systematic Explorer

Gene Set Analysis (GSA) using Gene Expression Data shifts the analyses more towards biology- driven approaches utilizes functional related groups of genes in order to analyze gene expression datasets more robust than single gene analyses

Competitive vs. Self-Contained Hypothesis GSA differ in the definition of the null hypothesis: Self-contained tests just compare the gene expression within the gene set across the given samples Comparative tests compare differential expression of the gene set to either all or the complement of the genes represented on a microarray

Gene Set Enrichment Analysis (GSEA) Mootha et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes, Nature Genetics, 2003, 34-3 Subramanian et al. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles, PNAS, 2005, Oron et al. Gene set enrichment analysis using linear models and diagnostics, Bioinformatics, 2008, Bioconductor Package: GSEAlm - Linear Model Toolset for Gene Set Enrichment Analysis

Aims of a Gene Set Enrichment Analysis Looking for up or down regulated gene sets between two tested classes Testing if a gene set of interest is differentially regulated between two tested phenotypes

Testing different Phenotypes Pair-wise Tests: Normal versus Low grade Normal versus High grade Low grade versus High grade SampleDisease Type S1Normal breast tissue S2Normal breast tissue S3Normal breast tissue S4Low grade cancer (Luminal A) S5Low grade cancer (Luminal A) S6Low grade cancer (Luminal A) S7High grade cancer (Basal) S8High grade cancer (Basal) S9High grade cancer (Basal) Gene Expression Data Clinical Data Combined Tests: Normal versus Low/High grade Normal/low grade versus High grade

I.) Ranking the genes according to differential expression using t-test or linear models Gene Set Enrichment Analysis (GSEA)

II.) Include gene set membership information

Enrichment Score (ES) reflects the degree to which a set S is overrepresented at the extremes of the entire ranked list L. The score is calculated by walking down the list L The enrichment score is the maximum deviation from zero encountered in the random walk; It corresponds to a weighted Kolmogorov–Smirnov-like statistic

Gene Set Enrichment Analysis (GSEA)

Subramanian A et al. PNAS 2005;102:

Permutation test to estimate the significance The significance of the ES has to be estimated Class label permutation versus gene label permutation Calculation the ES of the gene set for the permuted data, which generates a null distribution for the ES The empirical, nominal P value of the observed ES is then calculated relative to this null distribution

Gene Set Enrichment Analysis (GSEA)

Interpretation of the results

Correction for Multiple Testing When an entire database of gene sets is evaluated, we have to adjust the estimated significance level to account for multiple hypothesis testing Control for false discovery rate (FDR) The FDR is the estimated probability that a set with a given ES represents a false positive finding

Interpretation of the Results

Tutorial