Significant over-representation of pathway gene sets for a given gene list
Stomach and Esophageal carcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C11N80M1
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/C11N80M1
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, KEGG_ENDOMETRIAL_CANCER, REACTOME_CYTOKINE_SIGNALING_IN_IMMUNE_SYSTEM, KEGG_MELANOMA

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 1603 58 2.002e-24 2.643e-21
REACTOME IMMUNE SYSTEM gene.list 933 45023 45956 1603 100 3.465e-23 2.287e-20
KEGG ENDOMETRIAL CANCER gene.list 52 45904 45956 1603 19 4.682e-15 2.060e-12
REACTOME CYTOKINE SIGNALING IN IMMUNE SYSTEM gene.list 270 45686 45956 1603 40 1.404e-14 4.633e-12
KEGG MELANOMA gene.list 71 45885 45956 1603 20 2.611e-13 6.893e-11
KEGG COLORECTAL CANCER gene.list 62 45894 45956 1603 17 2.575e-11 4.856e-09
KEGG PROSTATE CANCER gene.list 89 45867 45956 1603 20 2.525e-11 4.856e-09
REACTOME ADAPTIVE IMMUNE SYSTEM gene.list 539 45417 45956 1603 52 5.693e-11 9.394e-09
KEGG BLADDER CANCER gene.list 42 45914 45956 1603 14 7.893e-11 1.158e-08
KEGG CELL CYCLE gene.list 128 45828 45956 1603 23 1.066e-10 1.407e-08
KEGG ADHERENS JUNCTION gene.list 75 45881 45956 1603 17 6.729e-10 7.401e-08
KEGG REGULATION OF ACTIN CYTOSKELETON gene.list 216 45740 45956 1603 29 6.252e-10 7.401e-08
REACTOME HEMOSTASIS gene.list 466 45490 45956 1603 45 1.065e-09 1.082e-07
KEGG MAPK SIGNALING PATHWAY gene.list 267 45689 45956 1603 32 1.527e-09 1.343e-07
REACTOME CELL CYCLE gene.list 421 45535 45956 1603 42 1.440e-09 1.343e-07
KEGG PANCREATIC CANCER gene.list 70 45886 45956 1603 16 1.868e-09 1.541e-07
PID P53DOWNSTREAMPATHWAY gene.list 137 45819 45956 1603 22 2.469e-09 1.810e-07
REACTOME DEVELOPMENTAL BIOLOGY gene.list 396 45560 45956 1603 40 2.460e-09 1.810e-07
KEGG NEUROTROPHIN SIGNALING PATHWAY gene.list 126 45830 45956 1603 21 2.834e-09 1.969e-07
KEGG NON SMALL CELL LUNG CANCER gene.list 54 45902 45956 1603 14 3.275e-09 2.161e-07

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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