{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T16:14:30Z","timestamp":1777047270780,"version":"3.51.4"},"reference-count":78,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2023,10,28]],"date-time":"2023-10-28T00:00:00Z","timestamp":1698451200000},"content-version":"vor","delay-in-days":36,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R35GM138143"],"award-info":[{"award-number":["R35GM138143"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,9,22]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Gene regulatory networks (GRNs) drive organism structure and functions, so the discovery and characterization of GRNs is a major goal in biological research. However, accurate identification of causal regulatory connections and inference of GRNs using gene expression datasets, more recently from single-cell RNA-seq (scRNA-seq), has been challenging. Here we employ the innovative method of Causal Inference Using Composition of Transactions (CICT) to uncover GRNs from scRNA-seq data. The basis of CICT is that if all gene expressions were random, a non-random regulatory gene should induce its targets at levels different from the background random process, resulting in distinct patterns in the whole relevance network of gene\u2013gene associations. CICT proposes novel network features derived from a relevance network, which enable any machine learning algorithm to predict causal regulatory edges and infer GRNs. We evaluated CICT using simulated and experimental scRNA-seq data in a well-established benchmarking pipeline and showed that CICT outperformed existing network inference methods representing diverse approaches with many-fold higher accuracy. Furthermore, we demonstrated that GRN inference with CICT was robust to different levels of sparsity in scRNA-seq data, the characteristics of data and ground truth, the choice of association measure and the complexity of the supervised machine learning algorithm. Our results suggest aiming at directly predicting causality to recover regulatory relationships in complex biological networks substantially improves accuracy in GRN inference.<\/jats:p>","DOI":"10.1093\/bib\/bbad370","type":"journal-article","created":{"date-parts":[[2023,10,28]],"date-time":"2023-10-28T14:27:13Z","timestamp":1698503233000},"source":"Crossref","is-referenced-by-count":10,"title":["Robust discovery of gene regulatory networks from single-cell gene expression data by Causal Inference Using Composition of Transactions"],"prefix":"10.1093","volume":"24","author":[{"given":"Abbas","family":"Shojaee","sequence":"first","affiliation":[{"name":"Center for Genomics and Systems Biology , Department of Biology, , New York, NY 10003 , USA"},{"name":"New York University , Department of Biology, , New York, NY 10003 , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shao-shan Carol","family":"Huang","sequence":"additional","affiliation":[{"name":"Center for Genomics and Systems Biology , Department of Biology, , New York, NY 10003 , USA"},{"name":"New York University , Department of Biology, , New York, NY 10003 , USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"2023102814265178300_ref1","doi-asserted-by":"crossref","DOI":"10.1093\/gigascience\/giy118","article-title":"Extensive evaluation of the generalized relevance network approach to inferring gene regulatory networks","volume":"7","author":"Kuzmanovski","year":"2018","journal-title":"GigaScience"},{"key":"2023102814265178300_ref2","doi-asserted-by":"crossref","first-page":"R36","DOI":"10.1186\/gb-2006-7-5-r36","article-title":"The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo","volume":"7","author":"Bonneau","year":"2006","journal-title":"Genome 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