Significant over-representation of pathway gene sets for a given gene list
Brain Lower Grade Glioma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1Q52P27
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/C1Q52P27
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_GLIOMA, KEGG_MELANOMA, KEGG_PATHWAYS_IN_CANCER, KEGG_PROSTATE_CANCER, KEGG_NON_SMALL_CELL_LUNG_CANCER

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 GLIOMA gene.list 65 45891 45956 110 9 5.277e-14 6.966e-11
KEGG MELANOMA gene.list 71 45885 45956 110 8 7.805e-12 5.152e-09
KEGG PATHWAYS IN CANCER gene.list 328 45628 45956 110 12 2.691e-11 1.184e-08
KEGG PROSTATE CANCER gene.list 89 45867 45956 110 8 5.000e-11 1.580e-08
KEGG NON SMALL CELL LUNG CANCER gene.list 54 45902 45956 110 7 5.986e-11 1.580e-08
PID ERBB1 RECEPTOR PROXIMAL PATHWAY gene.list 35 45921 45956 110 6 2.513e-10 5.528e-08
REACTOME SIGNALING BY CONSTITUTIVELY ACTIVE EGFR gene.list 18 45938 45956 110 5 5.990e-10 1.130e-07
KEGG SMALL CELL LUNG CANCER gene.list 84 45872 45956 110 7 1.443e-09 2.382e-07
KEGG ENDOMETRIAL CANCER gene.list 52 45904 45956 110 6 3.049e-09 4.472e-07
KEGG NEUROTROPHIN SIGNALING PATHWAY gene.list 126 45830 45956 110 7 2.478e-08 2.973e-06
KEGG CHRONIC MYELOID LEUKEMIA gene.list 73 45883 45956 110 6 2.448e-08 2.973e-06
PID PDGFRBPATHWAY gene.list 129 45827 45956 110 7 2.916e-08 3.208e-06
BIOCARTA ARF PATHWAY gene.list 17 45939 45956 110 4 7.219e-08 6.813e-06
REACTOME SIGNALING BY FGFR MUTANTS gene.list 44 45912 45956 110 5 7.226e-08 6.813e-06
PID PI3KCIPATHWAY gene.list 49 45907 45956 110 5 1.257e-07 9.758e-06
REACTOME SIGNALING BY FGFR1 FUSION MUTANTS gene.list 19 45937 45956 110 4 1.171e-07 9.758e-06
REACTOME DOWNSTREAM SIGNAL TRANSDUCTION gene.list 95 45861 45956 110 6 1.198e-07 9.758e-06
PID ECADHERIN KERATINOCYTE PATHWAY gene.list 21 45935 45956 110 4 1.802e-07 1.252e-05
REACTOME SIGNALING BY ERBB2 gene.list 101 45855 45956 110 6 1.726e-07 1.252e-05
PID PDGFRAPATHWAY gene.list 22 45934 45956 110 4 2.198e-07 1.451e-05

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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