Dimensionality reduction methods for extracting functional networks from large-scale CRISPR screens.

Hassan, Arshia Zernab; Ward, Henry N; Rahman, Mahfuzur; Billmann, Maximilian; Lee, Yoonkyu; Myers, Chad L

Hassan, Arshia Zernab; Ward, Henry N; Rahman, Mahfuzur; Billmann, Maximilian; Lee, Yoonkyu; Myers, Chad L.

Afiliación

Hassan AZ; Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN, USA.
Ward HN; Bioinformatics and Computational Biology Graduate Program, University of Minnesota - Twin Cities, Minneapolis, MN, USA.
Rahman M; Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN, USA.
Billmann M; Department of Computer Science and Engineering, University of Minnesota - Twin Cities, Minneapolis, MN, USA.
Lee Y; Institute of Human Genetics, University of Bonn, School of Medicine and University Hospital Bonn, Bonn, Germany.
Myers CL; Bioinformatics and Computational Biology Graduate Program, University of Minnesota - Twin Cities, Minneapolis, MN, USA.

Mol Syst Biol ; 19(11): e11657, 2023 Nov 09.

Article en En | MEDLINE | ID: mdl-37750448

RESUMEN

CRISPR-Cas9 screens facilitate the discovery of gene functional relationships and phenotype-specific dependencies. The Cancer Dependency Map (DepMap) is the largest compendium of whole-genome CRISPR screens aimed at identifying cancer-specific genetic dependencies across human cell lines. A mitochondria-associated bias has been previously reported to mask signals for genes involved in other functions, and thus, methods for normalizing this dominant signal to improve co-essentiality networks are of interest. In this study, we explore three unsupervised dimensionality reduction methods-autoencoders, robust, and classical principal component analyses (PCA)-for normalizing the DepMap to improve functional networks extracted from these data. We propose a novel "onion" normalization technique to combine several normalized data layers into a single network. Benchmarking analyses reveal that robust PCA combined with onion normalization outperforms existing methods for normalizing the DepMap. Our work demonstrates the value of removing low-dimensional signals from the DepMap before constructing functional gene networks and provides generalizable dimensionality reduction-based normalization tools.

Asunto(s)

Redes Reguladoras de Genes; Oncogenes; Humanos; Línea Celular Tumoral; Sistemas CRISPR-Cas/genética

Palabras clave

auto-encoder; gene co-essentiality network; normalization; robust principal component analysis; unsupervised dimensionality reduction

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Oncogenes / Redes Reguladoras de Genes Tipo de estudio: Clinical_trials Límite: Humans Idioma: En Revista: Mol Syst Biol Asunto de la revista: BIOLOGIA MOLECULAR / BIOTECNOLOGIA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido

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