A forward selection algorithm to identify mutually exclusive alterations in cancer studies

Mutual exclusivity analyses provide an effective tool to identify driver genes from passenger genes for cancer studies. Various algorithms have been developed for the detection of mutual exclusivity, but controlling false positive and improving accuracy remain challenging. We propose a forward selection algorithm for identification of mutually exclusive gene sets (FSME) in this paper. The method includes an initial search of seed pair of mutually exclusive (ME) genes and subsequently including more genes into the current ME set. Simulations demonstrated that, compared to recently published approaches (i.e., CoMEt, WExT, and MEGSA), FSME could provide higher precision or recall rate to identify ME gene sets, and had superior control of false positive rates. With application to TCGA real data sets for AML, BRCA, and GBM, we confirmed that FSME can be utilized to discover cancer driver genes.

Automated summary

Mutual exclusivity analyses are effective in identifying driver genes for cancer studies, but controlling false positives and improving accuracy is challenging. A forward selection algorithm (FSME) proposed in this study for identifying mutually exclusive gene sets shows higher precision and recall rates compared to other approaches such as CoMEt, WExT, and MEGSA. Application to TCGA real datasets confirms FSME's utility in discovering cancer driver genes.