{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T14:48:49Z","timestamp":1769611729560,"version":"3.49.0"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"22","license":[{"start":{"date-parts":[[2021,5,28]],"date-time":"2021-05-28T00:00:00Z","timestamp":1622160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"First Affiliated Hospital"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,11,18]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Single-cell RNA sequencing (scRNA-seq) has enabled the characterization of different cell types in many tissues and tumor samples. Cell type identification is essential for single-cell RNA profiling, currently transforming the life sciences. Often, this is achieved by searching for combinations of genes that have previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other scRNA-seq studies. Batch effects and different data platforms greatly decrease the predictive performance in inter-laboratory and different data type validation.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here, we present a new ensemble learning method named as \u2018scDetect\u2019 that combines gene expression rank-based analysis and a majority vote ensemble machine-learning probability-based prediction method capable of highly accurate classification of cells based on scRNA-seq data by different sequencing platforms. Because of tumor heterogeneity, in order to accurately predict tumor cells in the single-cell RNA-seq data, we have also incorporated cell copy number variation consensus clustering and epithelial score in the classification. We applied scDetect to scRNA-seq data from pancreatic tissue, mononuclear cells and tumor biopsies cells and show that scDetect classified individual cells with high accuracy and better than other publicly available tools.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>scDetect is an open source software. Source code and test data is freely available from Github (https:\/\/github.com\/IVDgenomicslab\/scDetect\/) and Zenodo (https:\/\/zenodo.org\/record\/4764132#.YKCOlrH5AYN). The examples and tutorial page is at https:\/\/ivdgenomicslab.github.io\/scDetect-Introduction\/. And scDetect will be available from Bioconductor.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btab410","type":"journal-article","created":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T12:46:35Z","timestamp":1622119595000},"page":"4115-4122","source":"Crossref","is-referenced-by-count":7,"title":["scDetect: a rank-based ensemble learning algorithm for cell type identification of single-cell RNA sequencing in cancer"],"prefix":"10.1093","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2720-724X","authenticated-orcid":false,"given":"Yifei","family":"Shen","sequence":"first","affiliation":[{"name":"China Department of Laboratory Medicine, First Affiliated Hospital, College of Medicine, Zhejiang University , Hangzhou 310003, China"},{"name":"China Key Laboratory of Clinical In Vitro Diagnostic Techniques of Zhejiang Province , Hangzhou 310003, China"},{"name":"China Institute of Laboratory Medicine, Zhejiang University , Hangzhou 310003, China"}]},{"given":"Qinjie","family":"Chu","sequence":"additional","affiliation":[{"name":"China Institute of Bioinformatics, Zhejiang University , Hangzhou 310058, China"}]},{"given":"Michael P","family":"Timko","sequence":"additional","affiliation":[{"name":"USA Departments of Biology and Public Health Sciences, University of Virginia , Charlottesville, VA 22903, USA"}]},{"given":"Longjiang","family":"Fan","sequence":"additional","affiliation":[{"name":"China Institute of Bioinformatics, Zhejiang University , Hangzhou 310058, China"},{"name":"China Department of Medical Oncology, First Affiliated Hospital, School of Medicine, Zhejiang University , Hangzhou 310003, China"}]}],"member":"286","published-online":{"date-parts":[[2021,5,28]]},"reference":[{"key":"2023051607092771200_btab410-B1","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1038\/nmeth.4463","article-title":"SCENIC: single-cell regulatory network inference and clustering","volume":"14","author":"Aibar","year":"2017","journal-title":"Nat. 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