Significant over-representaion of pathway genesets for a given gene list
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
02 April 2015 | analyses__2015_04_02
Maintainer Information
Citation Information
doi:10.7908/C1JM28P8
Maintained by Juok Cho (Broad Institute)
Cite as Broad Institute TCGA Genome Data Analysis Center (2015): Significant over-representaion of pathway genesets for a given gene list. Broad Institute of MIT and Harvard. doi:10.7908/C1JM28P8
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_ACUTE_MYELOID_LEUKEMIA, KEGG_CELL_CYCLE, KEGG_CHRONIC_MYELOID_LEUKEMIA, KEGG_NEUROTROPHIN_SIGNALING_PATHWAY

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 | 30 | 7 | 4.902e-09 | 1.980e-06
KEGG ACUTE MYELOID LEUKEMIA | gene.list | 387 | 45569 | 45956 | 30 | 6 | 1.717e-07 | 3.469e-05
KEGG CELL CYCLE | gene.list | 387 | 45569 | 45956 | 30 | 5 | 4.945e-06 | 4.995e-04
KEGG CHRONIC MYELOID LEUKEMIA | gene.list | 387 | 45569 | 45956 | 30 | 5 | 4.945e-06 | 4.995e-04
KEGG NEUROTROPHIN SIGNALING PATHWAY | gene.list | 387 | 45569 | 45956 | 30 | 4 | 1.141e-04 | 9.220e-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.

- Analysis Results (MD5 checksum)

- Auxiliary Data (MD5 checksum)

- MAGE-TAB File (MD5 checksum)

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)
[4] The GSEA MSigDB Compute overlaps
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