{"doi":"10.15252/msb.202311657","title":"Dimensionality reduction methods for extracting functional networks from large‐scale CRISPR screens","abstract":"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.","journal":"Molecular Systems Biology","year":2023,"id":350154,"datarank":0.3596842909197557,"base_score":2.3978952727983707,"endowment":2.3978952727983707,"self_citation_contribution":0.3596842909197557,"citation_network_contribution":0.0,"self_endowment_contribution":0.3596842909197557,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":10,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9354,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":711189,"name":"Henry N. Ward","orcid":"0000-0002-9869-9790","position":1,"is_corresponding":false},{"id":94091,"name":"Mahfuzur Rahman","orcid":"0000-0002-9226-3988","position":2,"is_corresponding":false},{"id":94090,"name":"Maximilian Billmann","orcid":"0000-0002-6556-9594","position":3,"is_corresponding":false},{"id":1094070,"name":"Yoonkyu Lee","orcid":"0000-0003-3011-4714","position":4,"is_corresponding":false},{"id":52090,"name":"Chad L. Myers","orcid":"0000-0002-1026-5972","position":5,"is_corresponding":false},{"id":1094069,"name":"Arshia Zernab Hassan","orcid":"0000-0002-5773-9186","position":0,"is_corresponding":true}],"reference_count":45,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T01:12:23.769679Z","pmid":"37750448","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}