MEXCOwalk: mutual exclusion and coverage based random walk to identify cancer modules
Özet
Motivation: Genomic analyses from large cancer cohorts have revealed the mutational heterogeneity problem
which hinders the identification of driver genes based only on mutation profiles. One way to tackle this problem is to
incorporate the fact that genes act together in functional modules. The connectivity knowledge present in existing
protein–protein interaction (PPI) networks together with mutation frequencies of genes and the mutual exclusivity of
cancer mutations can be utilized to increase the accuracy of identifying cancer driver modules.
Results: We present a novel edge-weighted random walk-based approach that incorporates connectivity information
in the form of protein–protein interactions (PPIs), mutual exclusivity and coverage to identify cancer driver modules.
MEXCOwalk outperforms several state-of-the-art computational methods on TCGA pan-cancer data in terms of
recovering known cancer genes, providing modules that are capable of classifying normal and tumor samples and
that are enriched for mutations in specific cancer types. Furthermore, the risk scores determined with output modules can stratify patients into low-risk and high-risk groups in multiple cancer types. MEXCOwalk identifies modules
containing both well-known cancer genes and putative cancer genes that are rarely mutated in the pan-cancer data.
The data, the source code and useful scripts are available at: https://github.com/abu-compbio/MEXCOwalk.
