Significant over-representation of pathway gene sets for a given gene list
Colon Adenocarcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1H70F5R
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2016): Significant over-representation of pathway gene sets for a given gene list. Broad Institute of MIT and Harvard. doi:10.7908/C1H70F5R
Overview
Introduction

This pipeline inspects significant overlapping pathway gene sets for a given gene list using a hypergeometric test. For the gene set database, we uses GSEA MSigDB Class2: Canonical Pathways DB as a gene set data. Further details about the MsigDB gene sets, please visit The Broad Institute GSEA MsigDB

Summary

For a given gene list, a hypergeometric test was tried to find significant overlapping canonical pathways using 1320 gene sets. In terms of FDR adjusted p.values, top 5 significant overlapping gene sets are listed as below.

  • KEGG_PATHWAYS_IN_CANCER, REACTOME_IMMUNE_SYSTEM, REACTOME_CYTOKINE_SIGNALING_IN_IMMUNE_SYSTEM, REACTOME_HEMOSTASIS, REACTOME_SIGNALING_BY_GPCR

Results
For a given gene list, top significant overlapping canonical pathway gene sets

Table 1.  Get Full Table This table shows significant gene sets in which at least one gene is found and its FDR adjusted p.value is smaller than 0.3. the hypergeometric p-value is a probability of randomly drawing x or more successes(gene overlaps in gene set database) from the population (gene universe consisting of N number of genes) in k total draws(the number of input genes). The hypergeometric test is identical to the corresponding one-tailed version of Fisher's exact test. That is, P(X=x) = f(x| N,m,k). The FDR q.value was obtained for 1320 multiple comparison.

GS(gene set) pathway name gene.list GS size (m) n.NotInGS (n) Gene universe (N) n.drawn (k) n.found (x) p.value (p(X>=x)) FDR (q.value)
KEGG PATHWAYS IN CANCER gene.list 328 45628 45956 2320 93 1.632e-43 2.154e-40
REACTOME IMMUNE SYSTEM gene.list 933 45023 45956 2320 155 1.572e-39 1.037e-36
REACTOME CYTOKINE SIGNALING IN IMMUNE SYSTEM gene.list 270 45686 45956 2320 64 1.476e-25 6.496e-23
REACTOME HEMOSTASIS gene.list 466 45490 45956 2320 85 5.634e-25 1.859e-22
REACTOME SIGNALING BY GPCR gene.list 920 45036 45956 2320 126 2.699e-24 7.125e-22
KEGG MAPK SIGNALING PATHWAY gene.list 267 45689 45956 2320 61 1.586e-23 3.489e-21
KEGG ADHERENS JUNCTION gene.list 75 45881 45956 2320 33 3.270e-23 6.166e-21
KEGG T CELL RECEPTOR SIGNALING PATHWAY gene.list 108 45848 45956 2320 36 2.601e-20 4.292e-18
REACTOME GPCR DOWNSTREAM SIGNALING gene.list 805 45151 45956 2320 107 7.936e-20 1.164e-17
KEGG FOCAL ADHESION gene.list 201 45755 45956 2320 48 1.126e-19 1.486e-17
KEGG PROSTATE CANCER gene.list 89 45867 45956 2320 32 2.383e-19 2.859e-17
KEGG COLORECTAL CANCER gene.list 62 45894 45956 2320 27 4.165e-19 4.254e-17
KEGG ENDOMETRIAL CANCER gene.list 52 45904 45956 2320 25 4.239e-19 4.254e-17
KEGG CHRONIC MYELOID LEUKEMIA gene.list 73 45883 45956 2320 29 4.511e-19 4.254e-17
KEGG ERBB SIGNALING PATHWAY gene.list 87 45869 45956 2320 31 1.172e-18 1.031e-16
KEGG REGULATION OF ACTIN CYTOSKELETON gene.list 216 45740 45956 2320 48 2.683e-18 2.213e-16
REACTOME SIGNALLING BY NGF gene.list 217 45739 45956 2320 48 3.278e-18 2.545e-16
REACTOME DEVELOPMENTAL BIOLOGY gene.list 396 45560 45956 2320 66 1.329e-17 9.743e-16
REACTOME ADAPTIVE IMMUNE SYSTEM gene.list 539 45417 45956 2320 79 2.097e-17 1.457e-15
KEGG TIGHT JUNCTION gene.list 134 45822 45956 2320 36 7.016e-17 4.630e-15

Figure 1.  Get High-res Image This figure is an event heatmap indicating gene matches across gene sets

Methods & Data
Input

  • Gene set database = c2.cp.v4.0.symbols.gmt

Hypergeometric Test

For a given gene list, it uses a hypergeometric test to get a significance of each overlapping pathway gene set. The hypergeometric p-value is obtained by R library function phyper() and is defined as a probability of randomly drawing x or more successes(gene matches) from the population consisting N genes in k(the input genes) total draws.

  • a cumulative p-value using the R function phyper():

    • ex). a probability to see at least x genes in the group is defined as p(X>=x) = 1 - p(X<=x)= 1 - phyper(x-1, m, n, k, lower.tail=FALSE, log.p=FALSE) that is, f(x| N, m, k) = (m) C (k) * ((N-m) C (n-k)) / ((N) C (n))

  • The hypergeometric test is identical to the corresponding one-tailed version of Fisher's exact test.

    • ex). Fisher' exact test = matrix(c(n.Found, n.GS-n.Found, n.drawn-n.Found, n.NotGS- (n.drawn-n.Found)), nrow=2, dimnames = list(inputGenes = c("Found", "NotFound"),GeneUniverse = c("GS", "nonGS")) )

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

References
[1] Johnson, N.L., et al, Univariate Discrete Distributions, Second Edition, Wiley (1992)
[2] Berkopec, Aleš, HyperQuick algorithm for discrete hypergeometric distribution, Journal of Discrete Algorithms:341-347 (2007)
[3] Tamayo, et al, Molecular Signatures Database, MSigDB, PNAS:15545-15550 (2005)
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