Affiliation:
1. Computational Biology Research Centre, Human Technopole , 20157 Milano, Italy
2. Institute for Applied Mathematics “Mauro Picone”, National Research Council (IAC-CNR) , 80131 Napoli, Italy
Abstract
Abstract
Motivation
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.
Results
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.
Availability and implementation
https://github.com/AleVin1995/MutExMatSorting.
Supplementary information
Supplementary data are available at Bioinformatics online.
Publisher
Oxford University Press (OUP)
Subject
Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability
Cited by
1 articles.
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