For comparing the impact of mutations across different pathway methods, we used TCGA cohorts where tissue-matched controls were available, leaving 6549 samples across 13 cancer types. For mutated genes, we considered all genes that had a change of coding sequence (SNP, small indels in MAF files) as mutated and all others as not mutated. For copy number alterations (CNAs), we used the thresholded GISTIC 33 scores, where we considered homozygous deletions (-2) and strong amplifications (2) as altered, no change (0) as basal and discarded intermediate values (-1, 1) from our analysis. We focussed our analysis of the mutations and copy number alterations on the subset of 464 driver genes that were also used in the GDSC. We used the sets of mutations and CNAs to compute the linear associations between samples for all different methods we looked at. We did not regress out the cancer type in order to keep associations where mutations/CNAs are highly correlated with it, but highlighted all associations that passed the significance threshold of FDR<5% (for each pathway method individually) after such a correction.
- snp_drivers.r, mutations_annotated{..}.txt - use SNVs of known cancer drivers that are linked to at least one pathway
- snp_all.r - do not rely on cancer driver list, but use all mutations that occur above a certain frequency
- cna_gistic.r, cna.txt - use CNAs instead of SNVs as input; only use homozygous alterations (this may be subject to change)