Significant over-representation of pathway gene sets for a given gene list
Pancreatic Adenocarcinoma (Primary solid tumor)
28 January 2016  |  analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1CJ8CXP
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/C1CJ8CXP
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.

  • REACTOME_DEVELOPMENTAL_BIOLOGY, REACTOME_AXON_GUIDANCE, REACTOME_CELL_CYCLE, KEGG_AXON_GUIDANCE, PID_INTEGRIN3_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)
REACTOME DEVELOPMENTAL BIOLOGY gene.list 396 45560 45956 759 29 3.543e-11 4.677e-08
REACTOME AXON GUIDANCE gene.list 251 45705 45956 759 21 1.685e-09 1.112e-06
REACTOME CELL CYCLE gene.list 421 45535 45956 759 27 3.042e-09 1.338e-06
KEGG AXON GUIDANCE gene.list 129 45827 45956 759 14 3.388e-08 8.944e-06
PID INTEGRIN3 PATHWAY gene.list 43 45913 45956 759 9 2.978e-08 8.944e-06
REACTOME MEIOSIS gene.list 116 45840 45956 759 13 7.108e-08 1.564e-05
REACTOME TRANSMEMBRANE TRANSPORT OF SMALL MOLECULES gene.list 413 45543 45956 759 24 1.432e-07 2.700e-05
KEGG PATHWAYS IN CANCER gene.list 328 45628 45956 759 21 1.747e-07 2.882e-05
KEGG CALCIUM SIGNALING PATHWAY gene.list 178 45778 45956 759 15 3.209e-07 4.707e-05
KEGG MAPK SIGNALING PATHWAY gene.list 267 45689 45956 759 18 6.200e-07 8.185e-05
REACTOME HEMOSTASIS gene.list 466 45490 45956 759 24 1.242e-06 1.490e-04
PID INTEGRIN1 PATHWAY gene.list 66 45890 45956 759 9 1.394e-06 1.533e-04
PID FRA PATHWAY gene.list 37 45919 45956 759 7 2.182e-06 2.057e-04
REACTOME GENERIC TRANSCRIPTION PATHWAY gene.list 352 45604 45956 759 20 2.175e-06 2.057e-04
REACTOME MEIOTIC SYNAPSIS gene.list 73 45883 45956 759 9 3.299e-06 2.903e-04
BIOCARTA TEL PATHWAY gene.list 18 45938 45956 759 5 8.693e-06 7.172e-04
PID SYNDECAN 4 PATHWAY gene.list 32 45924 45956 759 6 1.250e-05 9.708e-04
PID ATM PATHWAY gene.list 34 45922 45956 759 6 1.804e-05 1.253e-03
PID MAPKTRKPATHWAY gene.list 34 45922 45956 759 6 1.804e-05 1.253e-03
PID AR TF PATHWAY gene.list 53 45903 45956 759 7 2.598e-05 1.715e-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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