A Greedy Algorithm for Detecting Mutually Exclusive Patterns in Cancer Mutation Data

Some somatic mutations are reported to present mutually exclusive patterns. It is a basic computational problem to efficiently extracting mutually exclusive patterns from cancer mutation data. In this article, we focus on the inter-set mutual exclusion problem, which is to group the genes into at least two sets, with the mutations in the different sets mutually exclusive. The proposed algorithm improves the calculation of the score of mutual exclusion. The improved measurement considers the percentage of supporting cases, the approximate exclusivity degree and the pair-wise similarities of two genes. Moreover, the proposed algorithm adopts a greedy strategy to generate the sets of genes. Different from the existing approaches, the greedy strategy considers the scores of mutual exclusion between both the genes and virtual genes, which benefits the selection with the size restrictions. We conducted a series of experiments to verify the performance on simulation datasets and TCGA dataset consisting of 477 real cases with more than 10 million mutations within 28507 genes. According to the results, our algorithm demonstrated good performance under different simulation configurations. In addition, it outperformed CoMEt, a widely-accepted algorithm, in recall rates and accuracies on simulation datasets. Moreover, some of the exclusive patterns detected from TCGA dataset were supported by published literatures.