Significant over-representation of pathway genesets for a given gene list
Lung Squamous Cell Carcinoma (Primary solid tumor)
21 August 2015  |  analyses__2015_08_21
Maintainer Information
Citation Information
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Significant over-representation of pathway genesets for a given gene list. Broad Institute of MIT and Harvard. doi:10.7908/C1W37VJB
Overview
Introduction

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

Summary

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

  • KEGG_PATHWAYS_IN_CANCER, KEGG_GLIOMA, KEGG_PROSTATE_CANCER, KEGG_MELANOMA, KEGG_CHRONIC_MYELOID_LEUKEMIA

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

Table 1.  Get Full Table This table shows significant genesets in which at least one gene is found and its FDR adjusted p.value is smaller than 0.05. the hypergeometric p-value is a probability of randomly drawing x or more successes(gene overlaps in geneset databse) from the population (gene universe consisiting 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).

GS(geneset) 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 387 45569 45956 18 6 5.847e-09 5.906e-07
KEGG GLIOMA gene.list 387 45569 45956 18 6 5.847e-09 5.906e-07
KEGG PROSTATE CANCER gene.list 387 45569 45956 18 6 5.847e-09 5.906e-07
KEGG MELANOMA gene.list 387 45569 45956 18 6 5.847e-09 5.906e-07
KEGG CHRONIC MYELOID LEUKEMIA gene.list 387 45569 45956 18 5 3.231e-07 2.176e-05
KEGG NON SMALL CELL LUNG CANCER gene.list 387 45569 45956 18 5 3.231e-07 2.176e-05
BIOCARTA CTCF PATHWAY gene.list 387 45569 45956 18 4 1.380e-05 4.646e-04
BIOCARTA ARF PATHWAY gene.list 387 45569 45956 18 4 1.380e-05 4.646e-04
KEGG CELL CYCLE gene.list 387 45569 45956 18 4 1.380e-05 4.646e-04
KEGG PANCREATIC CANCER gene.list 387 45569 45956 18 4 1.380e-05 4.646e-04
KEGG BLADDER CANCER gene.list 387 45569 45956 18 4 1.380e-05 4.646e-04
KEGG SMALL CELL LUNG CANCER gene.list 387 45569 45956 18 4 1.380e-05 4.646e-04
BIOCARTA G1 PATHWAY gene.list 387 45569 45956 18 3 4.402e-04 9.359e-03
BIOCARTA RACCYCD PATHWAY gene.list 387 45569 45956 18 3 4.402e-04 9.359e-03
BIOCARTA PPARA PATHWAY gene.list 387 45569 45956 18 3 4.402e-04 9.359e-03
BIOCARTA HER2 PATHWAY gene.list 387 45569 45956 18 3 4.402e-04 9.359e-03
KEGG P53 SIGNALING PATHWAY gene.list 387 45569 45956 18 3 4.402e-04 9.359e-03
KEGG HUNTINGTONS DISEASE gene.list 387 45569 45956 18 3 4.402e-04 9.359e-03
KEGG ENDOMETRIAL CANCER gene.list 387 45569 45956 18 3 4.402e-04 9.359e-03

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

Methods & Data
Input
  • Gene set database = c2.cp.v3.0-2.symbols.gmt

  • Input gene list = sig_genes.txt

Hypergeometric Test

For a given gene list, it uses a hypergeometric test to get a significance of each overlapping pathway geneset. The hypergeometric p-value is obtained by R library function phyer() 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.val with lower tail==T in phyer():

    • ex). a probability to see at least 3 genes in the group is p(x>=3) = 1 - p(x<=2)= 1 - phyer(2, lower.tail=T) that is, f(x| N, m, k) = mCk * ((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)