Significant over-representation of pathway gene sets for a given gene list
Testicular Germ Cell Tumors (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C10K282W
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/C10K282W
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_RENAL_CELL_CARCINOMA, PID_ERBB2ERBB3PATHWAY, PID_PI3KCIPATHWAY, KEGG_ACUTE_MYELOID_LEUKEMIA, KEGG_VEGF_SIGNALING_PATHWAY

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 RENAL CELL CARCINOMA gene.list 70 45886 45956 64 5 5.040e-08 6.653e-05
PID ERBB2ERBB3PATHWAY gene.list 44 45912 45956 64 4 4.452e-07 2.938e-04
PID PI3KCIPATHWAY gene.list 49 45907 45956 64 4 6.912e-07 3.041e-04
KEGG ACUTE MYELOID LEUKEMIA gene.list 60 45896 45956 64 4 1.573e-06 5.190e-04
KEGG VEGF SIGNALING PATHWAY gene.list 76 45880 45956 64 4 4.069e-06 7.838e-04
KEGG B CELL RECEPTOR SIGNALING PATHWAY gene.list 75 45881 45956 64 4 3.859e-06 7.838e-04
KEGG FC EPSILON RI SIGNALING PATHWAY gene.list 79 45877 45956 64 4 4.750e-06 7.838e-04
REACTOME SIGNALING BY SCF KIT gene.list 78 45878 45956 64 4 4.515e-06 7.838e-04
KEGG PATHWAYS IN CANCER gene.list 328 45628 45956 64 6 6.683e-06 9.802e-04
PID ERBB1 DOWNSTREAM PATHWAY gene.list 105 45851 45956 64 4 1.471e-05 1.942e-03
PID ERBB1 RECEPTOR PROXIMAL PATHWAY gene.list 35 45921 45956 64 3 1.633e-05 1.960e-03
KEGG NEUROTROPHIN SIGNALING PATHWAY gene.list 126 45830 45956 64 4 3.013e-05 2.295e-03
PID EPHBFWDPATHWAY gene.list 40 45916 45956 64 3 2.453e-05 2.295e-03
PID PDGFRBPATHWAY gene.list 129 45827 45956 64 4 3.304e-05 2.295e-03
PID ERBB1 INTERNALIZATION PATHWAY gene.list 41 45915 45956 64 3 2.644e-05 2.295e-03
PID CXCR3PATHWAY gene.list 43 45913 45956 64 3 3.055e-05 2.295e-03
REACTOME SIGNALING BY THE B CELL RECEPTOR BCR gene.list 126 45830 45956 64 4 3.013e-05 2.295e-03
REACTOME SIGNALING BY FGFR MUTANTS gene.list 44 45912 45956 64 3 3.275e-05 2.295e-03
REACTOME IL 2 SIGNALING gene.list 41 45915 45956 64 3 2.644e-05 2.295e-03
KEGG NATURAL KILLER CELL MEDIATED CYTOTOXICITY gene.list 137 45819 45956 64 4 4.180e-05 2.759e-03

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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