Significant over-representation of pathway gene sets for a given gene list
Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)
28 January 2016 | analyses__2016_01_28
Maintainer Information
Citation Information
doi:10.7908/C1MK6C9C
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/C1MK6C9C
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_ACUTE_MYELOID_LEUKEMIA, KEGG_PATHWAYS_IN_CANCER, KEGG_CHRONIC_MYELOID_LEUKEMIA, REACTOME_CHROMOSOME_MAINTENANCE, KEGG_CELL_CYCLE

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 ACUTE MYELOID LEUKEMIA | gene.list | 60 | 45896 | 45956 | 24 | 6 | 5.059e-13 | 6.678e-10
KEGG PATHWAYS IN CANCER | gene.list | 328 | 45628 | 45956 | 24 | 7 | 2.760e-10 | 1.607e-07
KEGG CHRONIC MYELOID LEUKEMIA | gene.list | 73 | 45883 | 45956 | 24 | 5 | 3.652e-10 | 1.607e-07
REACTOME CHROMOSOME MAINTENANCE | gene.list | 122 | 45834 | 45956 | 24 | 5 | 4.955e-09 | 1.635e-06
KEGG CELL CYCLE | gene.list | 128 | 45828 | 45956 | 24 | 5 | 6.311e-09 | 1.666e-06
REACTOME CELL CYCLE | gene.list | 421 | 45535 | 45956 | 24 | 6 | 6.677e-08 | 1.259e-05
REACTOME MEIOTIC SYNAPSIS | gene.list | 73 | 45883 | 45956 | 24 | 4 | 6.076e-08 | 1.259e-05
PID CMYB PATHWAY | gene.list | 84 | 45872 | 45956 | 24 | 4 | 1.073e-07 | 1.771e-05
REACTOME MITOTIC PROMETAPHASE | gene.list | 87 | 45869 | 45956 | 24 | 4 | 1.237e-07 | 1.814e-05
REACTOME MEIOSIS | gene.list | 116 | 45840 | 45956 | 24 | 4 | 3.938e-07 | 5.198e-05
KEGG THYROID CANCER | gene.list | 29 | 45927 | 45956 | 24 | 3 | 4.532e-07 | 5.438e-05
KEGG NEUROTROPHIN SIGNALING PATHWAY | gene.list | 126 | 45830 | 45956 | 24 | 4 | 5.485e-07 | 6.034e-05
PID ERBB1 RECEPTOR PROXIMAL PATHWAY | gene.list | 35 | 45921 | 45956 | 24 | 3 | 8.100e-07 | 8.225e-05
PID GMCSF PATHWAY | gene.list | 37 | 45919 | 45956 | 24 | 3 | 9.610e-07 | 9.061e-05
KEGG BLADDER CANCER | gene.list | 42 | 45914 | 45956 | 24 | 3 | 1.417e-06 | 1.247e-04
PID ERBB2ERBB3PATHWAY | gene.list | 44 | 45912 | 45956 | 24 | 3 | 1.634e-06 | 1.348e-04
REACTOME MITOTIC M M G1 PHASES | gene.list | 172 | 45784 | 45956 | 24 | 4 | 1.899e-06 | 1.474e-04
KEGG ENDOMETRIAL CANCER | gene.list | 52 | 45904 | 45956 | 24 | 3 | 2.719e-06 | 1.994e-04
KEGG NON SMALL CELL LUNG CANCER | gene.list | 54 | 45902 | 45956 | 24 | 3 | 3.050e-06 | 2.013e-04
REACTOME DNA REPLICATION | gene.list | 192 | 45764 | 45956 | 24 | 4 | 2.939e-06 | 2.013e-04

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

- Input gene list = MutSig2CV.input.genenames.txt

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.

- 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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