Computational Biology Research Centre, Human Technopole, 20157 Milano, Italy.
Institute for Applied Mathematics "Mauro Picone", National Research Council (IAC-CNR), 80131 Napoli, Italy.
Bioinformatics. 2023 Jan 1;39(1). doi: 10.1093/bioinformatics/btad016.
Binary (or Boolean) matrices provide a common effective data representation adopted in several domains of computational biology, especially for investigating cancer and other human diseases. For instance, they are used to summarize genetic aberrations-copy number alterations or mutations-observed in cancer patient cohorts, effectively highlighting combinatorial relations among them. One of these is the tendency for two or more genes not to be co-mutated in the same sample or patient, i.e. a mutual-exclusivity trend. Exploiting this principle has allowed identifying new cancer driver protein-interaction networks and has been proposed to design effective combinatorial anti-cancer therapies rationally. Several tools exist to identify and statistically assess mutual-exclusive cancer-driver genomic events. However, these tools need to be equipped with robust/efficient methods to sort rows and columns of a binary matrix to visually highlight possible mutual-exclusivity trends.
Here, we formalize the mutual-exclusivity-sorting problem and present MutExMatSorting: an R package implementing a computationally efficient algorithm able to sort rows and columns of a binary matrix to highlight mutual-exclusivity patterns. Particularly, our algorithm minimizes the extent of collective vertical overlap between consecutive non-zero entries across rows while maximizing the number of adjacent non-zero entries in the same row. Here, we demonstrate that existing tools for mutual-exclusivity analysis are suboptimal according to these criteria and are outperformed by MutExMatSorting.
https://github.com/AleVin1995/MutExMatSorting.
Supplementary data are available at Bioinformatics online.
二进制（或布尔）矩阵提供了一种在计算生物学多个领域中常用的有效数据表示方法，特别是用于研究癌症和其他人类疾病。例如，它们用于总结癌症患者队列中观察到的遗传异常（拷贝数改变或突变），有效地突出了它们之间的组合关系。其中之一是两个或多个基因在同一样本或患者中不共同突变的趋势，即互斥趋势。利用这一原理，可以识别新的癌症驱动蛋白相互作用网络，并被提议合理地设计有效的组合抗癌疗法。有几种工具可用于识别和统计评估互斥的癌症驱动基因组事件。然而，这些工具需要配备强大/高效的方法来对二进制矩阵的行和列进行排序，以直观地突出可能的互斥趋势。
在这里，我们形式化了互斥排序问题，并提出了 MutExMatSorting：一个实现了一种计算效率高的算法的 R 包，能够对二进制矩阵的行和列进行排序，以突出互斥模式。特别是，我们的算法通过最小化连续非零项在同一行中垂直相邻的程度，同时最大化同一行中相邻非零项的数量，来最大化集体垂直重叠程度。在这里，我们证明了现有的互斥分析工具根据这些标准是次优的，并且被 MutExMatSorting 所超越。
https://github.com/AleVin1995/MutExMatSorting。
补充数据可在生物信息学在线获得。
