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
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.
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KEGG_PATHWAYS_IN_CANCER, KEGG_PROSTATE_CANCER, KEGG_GLIOMA, KEGG_MELANOMA, KEGG_MAPK_SIGNALING_PATHWAY
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 | 24 | 7 | 8.704e-10 | 3.516e-07 |
| KEGG PROSTATE CANCER | gene.list | 387 | 45569 | 45956 | 24 | 6 | 4.062e-08 | 8.206e-06 |
| KEGG GLIOMA | gene.list | 387 | 45569 | 45956 | 24 | 5 | 1.538e-06 | 1.553e-04 |
| KEGG MELANOMA | gene.list | 387 | 45569 | 45956 | 24 | 5 | 1.538e-06 | 1.553e-04 |
| KEGG MAPK SIGNALING PATHWAY | gene.list | 387 | 45569 | 45956 | 24 | 4 | 4.605e-05 | 1.860e-03 |
| KEGG PANCREATIC CANCER | gene.list | 387 | 45569 | 45956 | 24 | 4 | 4.605e-05 | 1.860e-03 |
| KEGG ENDOMETRIAL CANCER | gene.list | 387 | 45569 | 45956 | 24 | 4 | 4.605e-05 | 1.860e-03 |
| KEGG BLADDER CANCER | gene.list | 387 | 45569 | 45956 | 24 | 4 | 4.605e-05 | 1.860e-03 |
| KEGG CHRONIC MYELOID LEUKEMIA | gene.list | 387 | 45569 | 45956 | 24 | 4 | 4.605e-05 | 1.860e-03 |
| KEGG NON SMALL CELL LUNG CANCER | gene.list | 387 | 45569 | 45956 | 24 | 4 | 4.605e-05 | 1.860e-03 |
| BIOCARTA PML PATHWAY | gene.list | 387 | 45569 | 45956 | 24 | 3 | 1.052e-03 | 2.023e-02 |
| BIOCARTA ATRBRCA PATHWAY | gene.list | 387 | 45569 | 45956 | 24 | 3 | 1.052e-03 | 2.023e-02 |
| BIOCARTA TEL PATHWAY | gene.list | 387 | 45569 | 45956 | 24 | 3 | 1.052e-03 | 2.023e-02 |
| KEGG CELL CYCLE | gene.list | 387 | 45569 | 45956 | 24 | 3 | 1.052e-03 | 2.023e-02 |
| KEGG TIGHT JUNCTION | gene.list | 387 | 45569 | 45956 | 24 | 3 | 1.052e-03 | 2.023e-02 |
| KEGG LONG TERM POTENTIATION | gene.list | 387 | 45569 | 45956 | 24 | 3 | 1.052e-03 | 2.023e-02 |
| KEGG NEUROTROPHIN SIGNALING PATHWAY | gene.list | 387 | 45569 | 45956 | 24 | 3 | 1.052e-03 | 2.023e-02 |
| KEGG MELANOGENESIS | gene.list | 387 | 45569 | 45956 | 24 | 3 | 1.052e-03 | 2.023e-02 |
| KEGG RENAL CELL CARCINOMA | gene.list | 387 | 45569 | 45956 | 24 | 3 | 1.052e-03 | 2.023e-02 |
| KEGG THYROID CANCER | gene.list | 387 | 45569 | 45956 | 24 | 3 | 1.052e-03 | 2.023e-02 |
Figure 1. Get High-res Image This figure is an event heatmap indicating gene matches across genesets
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Gene set database = c2.cp.v3.0-2.symbols.gmt
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Input gene list = sig_genes.txt
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.
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a cumulative p.val with lower tail==T in phyer():
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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))
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The hypergeometric test is identical to the corresponding one-tailed version of Fisher's exact test.
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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")) )
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.