Significant over-representation of pathway gene sets for a given gene list
Pheochromocytoma and Paraganglioma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
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/C1BC3Z0P
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_THYROID_CANCER, PID_SYNDECAN_2_PATHWAY, PID_RAS_PATHWAY, PID_RET_PATHWAY, KEGG_PATHWAYS_IN_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 THYROID CANCER gene.list 29 45927 45956 10 2 1.725e-05 0.009865
PID SYNDECAN 2 PATHWAY gene.list 33 45923 45956 10 2 2.242e-05 0.009865
PID RAS PATHWAY gene.list 30 45926 45956 10 2 1.848e-05 0.009865
PID RET PATHWAY gene.list 39 45917 45956 10 2 3.144e-05 0.010380
KEGG PATHWAYS IN CANCER gene.list 328 45628 45956 10 3 4.166e-05 0.010520
PID AJDISS 2PATHWAY gene.list 48 45908 45956 10 2 4.781e-05 0.010520
KEGG RENAL CELL CARCINOMA gene.list 70 45886 45956 10 2 1.021e-04 0.019250
KEGG ENDOCYTOSIS gene.list 183 45773 45956 10 2 6.949e-04 0.114700
KEGG MAPK SIGNALING PATHWAY gene.list 267 45689 45956 10 2 1.467e-03 0.123100
BIOCARTA AT1R PATHWAY gene.list 36 45920 45956 10 1 7.807e-03 0.123100
BIOCARTA SPPA PATHWAY gene.list 22 45934 45956 10 1 4.777e-03 0.123100
BIOCARTA BCR PATHWAY gene.list 37 45919 45956 10 1 8.023e-03 0.123100
BIOCARTA CDMAC PATHWAY gene.list 16 45940 45956 10 1 3.476e-03 0.123100
BIOCARTA CCR3 PATHWAY gene.list 23 45933 45956 10 1 4.994e-03 0.123100
BIOCARTA CXCR4 PATHWAY gene.list 24 45932 45956 10 1 5.211e-03 0.123100
BIOCARTA EGF PATHWAY gene.list 31 45925 45956 10 1 6.726e-03 0.123100
BIOCARTA EPO PATHWAY gene.list 19 45937 45956 10 1 4.127e-03 0.123100
BIOCARTA ECM PATHWAY gene.list 24 45932 45956 10 1 5.211e-03 0.123100
BIOCARTA ERK PATHWAY gene.list 28 45928 45956 10 1 6.077e-03 0.123100
BIOCARTA GH PATHWAY gene.list 28 45928 45956 10 1 6.077e-03 0.123100

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)