{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T13:20:35Z","timestamp":1740144035756,"version":"3.37.3"},"reference-count":73,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T00:00:00Z","timestamp":1672099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["31671342","31871331","91940304"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,19]]},"abstract":"Abstract<\/jats:title>Single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) has been a powerful technology for transcriptome analysis. However, the systematic validation of diverse computational tools used in scRNA-seq analysis remains challenging. Here, we propose a novel simulation tool, termed as Simulation of Cellular Heterogeneity (SimCH), for the flexible and comprehensive assessment of scRNA-seq computational methods. The Gaussian Copula framework is recruited to retain gene coexpression of experimental data shown to be associated with cellular heterogeneity. The synthetic count matrices generated by suitable SimCH modes closely match experimental data originating from either homogeneous or heterogeneous cell populations and either unique molecular identifier (UMI)-based or non-UMI-based techniques. We demonstrate how SimCH can benchmark several types of computational methods, including cell clustering, discovery of differentially expressed genes, trajectory inference, batch correction and imputation. Moreover, we show how SimCH can be used to conduct power evaluation of cell clustering methods. Given these merits, we believe that SimCH can accelerate single-cell research.<\/jats:p>","DOI":"10.1093\/bib\/bbac590","type":"journal-article","created":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T04:41:55Z","timestamp":1672202515000},"source":"Crossref","is-referenced-by-count":2,"title":["SimCH: simulation of single-cell RNA sequencing data by modeling cellular heterogeneity at gene expression level"],"prefix":"10.1093","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8954-683X","authenticated-orcid":false,"given":"Lei","family":"Sun","sequence":"first","affiliation":[{"name":"Yangzhou University School of Information Engineering, , Yangzhou, P.R . China"},{"name":"Yangzhou University School of Artificial Intelligence, , Yangzhou, P.R . China"},{"name":"Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation CAS Key Laboratory of Genome Sciences and Information, , Beijing, P.R . China"}]},{"given":"Gongming","family":"Wang","sequence":"additional","affiliation":[{"name":"Yangzhou University School of Information Engineering, , Yangzhou, P.R . China"},{"name":"Yangzhou University School of Artificial Intelligence, , Yangzhou, P.R . China"},{"name":"China Unicom Software Research Institute Jinan Branch , Jinan, P.R . China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7706-9247","authenticated-orcid":false,"given":"Zhihua","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Genomics, Chinese Academy of Sciences, and China National Center for Bioinformation CAS Key Laboratory of Genome Sciences and Information, , Beijing, P.R . China"},{"name":"University of Chinese Academy of Sciences School of Life Science, , Beijing, P.R . China"}]}],"member":"286","published-online":{"date-parts":[[2022,12,27]]},"reference":[{"key":"2023011917101481300_ref1","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/s13059-020-1926-6","article-title":"Eleven grand challenges in single-cell data science","volume":"21","author":"L\u00e4hnemann","year":"2020","journal-title":"Genome Biol"},{"key":"2023011917101481300_ref2","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1038\/s41581-018-0021-7","article-title":"Single-cell RNA sequencing for the study of development, physiology and disease","volume":"14","author":"Potter","year":"2018","journal-title":"Nat Rev Nephrol"},{"key":"2023011917101481300_ref3","doi-asserted-by":"crossref","first-page":"28784","DOI":"10.1073\/pnas.2005990117","article-title":"Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign","volume":"117","author":"Chen","year":"2020","journal-title":"Proc Natl Acad 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