{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T09:34:21Z","timestamp":1776159261047,"version":"3.50.1"},"reference-count":13,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T00:00:00Z","timestamp":1646697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Swiss National Science Foundation (SNF) Ambizione","award":["180010 to S.J.C."],"award-info":[{"award-number":["180010 to S.J.C."]}]},{"name":"National Scientific and Technical Research Council of Argentina"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,4,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Summary<\/jats:title>\n                    <jats:p>A common bioinformatics task in single-cell data analysis is to purify a cell type or cell population of interest from heterogeneous datasets. Here, we present scGate, an algorithm that automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. scGate purifies a cell population of interest using a set of markers organized in a hierarchical structure, akin to gating strategies employed in flow cytometry. scGate outperforms state-of-the-art single-cell classifiers and it can be applied to multiple modalities of single-cell data (e.g. RNA-seq, ATAC-seq, CITE-seq). scGate is implemented as an R package and integrated with the Seurat framework, providing an intuitive tool to isolate cell populations of interest from heterogeneous single-cell datasets.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>scGate is available as an R package at https:\/\/github.com\/carmonalab\/scGate (https:\/\/doi.org\/10.5281\/zenodo.6202614). Several reproducible workflows describing the main functions and usage of the package on different single-cell modalities, as well as the code to reproduce the benchmark, can be found at https:\/\/github.com\/carmonalab\/scGate.demo (https:\/\/doi.org\/10.5281\/zenodo.6202585) and https:\/\/github.com\/carmonalab\/scGate.benchmark. Test data are available at https:\/\/doi.org\/10.6084\/m9.figshare.16826071.<\/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\/btac141","type":"journal-article","created":{"date-parts":[[2022,3,4]],"date-time":"2022-03-04T07:10:05Z","timestamp":1646377805000},"page":"2642-2644","source":"Crossref","is-referenced-by-count":85,"title":["scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8036-2647","authenticated-orcid":false,"given":"Massimo","family":"Andreatta","sequence":"first","affiliation":[{"name":"Ludwig Institute for Cancer Research, Lausanne Branch, and Department of Oncology, CHUV and University of Lausanne , 1011 Lausanne, Switzerland"},{"name":"Swiss Institute of Bioinformatics , 1015 Lausanne, Switzerland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8540-5389","authenticated-orcid":false,"given":"Ariel J","family":"Berenstein","sequence":"additional","affiliation":[{"name":"Laboratorio de Biolog\u00eda Molecular, Divisi\u00f3n Patolog\u00eda, Instituto Multidisciplinario de Investigaciones en Patolog\u00edas Pedi\u00e1tricas (IMIPP), CONICET-GCBA , Buenos Aires C1425EFD, Argentina"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2495-0671","authenticated-orcid":false,"given":"Santiago J","family":"Carmona","sequence":"additional","affiliation":[{"name":"Ludwig Institute for Cancer Research, Lausanne Branch, and Department of Oncology, CHUV and University of Lausanne , 1011 Lausanne, Switzerland"},{"name":"Swiss Institute of Bioinformatics , 1015 Lausanne, Switzerland"}]}],"member":"286","published-online":{"date-parts":[[2022,3,8]]},"reference":[{"key":"2023041402564253000_","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1186\/s13059-019-1795-z","article-title":"A comparison of automatic cell identification methods for single-cell RNA sequencing data","volume":"20","author":"Abdelaal","year":"2019","journal-title":"Genome Biol"},{"key":"2023041402564253000_","doi-asserted-by":"crossref","first-page":"3796","DOI":"10.1016\/j.csbj.2021.06.043","article-title":"UCell: robust and scalable single-cell gene signature scoring","volume":"19","author":"Andreatta","year":"2021","journal-title":"Comput. 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