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.

互斥分析提供了一种有效的工具，可以从乘客基因中识别用于癌症研究的驱动基因。已经开发了各种算法来检测互斥性，但控制误报和提高准确性仍然具有挑战性。我们在本文中提出了一种用于识别互斥基因集（FSME）的前向选择算法。该方法包括对互斥 (ME) 基因的种子对的初始搜索，随后将更多基因包括到当前 ME 集合中。模拟表明，与最近发表的方法（即 CoMEt、WExT 和 MEGSA）相比，FSME 可以提供更高的准确率或召回率来识别 ME 基因集，并且对假阳性率有更好的控制。应用于 AML、BRCA 和 GBM 的 TCGA 真实数据集，