A heuristic algorithm solving the mutual-exclusivity-sorting problem.
Bioinformatics
; 39(1)2023 01 01.
Article
en En
| MEDLINE
| ID: mdl-36669133
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.
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Heurística
/
Neoplasias
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Bioinformatics
Asunto de la revista:
INFORMATICA MEDICA
Año:
2023
Tipo del documento:
Article
País de afiliación:
Italia