{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T06:17:21Z","timestamp":1772173041950,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1010097","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,6,15]],"date-time":"2022-06-15T00:00:00Z","timestamp":1655251200000}}],"reference-count":26,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T00:00:00Z","timestamp":1654214400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Zurich, University Hospital Zurich, Swiss Federal Institute of Technology in Zurich, University Hospital Basel, and F. Hoffmann-La Roche AG"},{"name":"Personalized Health and Related Technologies","award":["PHRT-510"],"award-info":[{"award-number":["PHRT-510"]}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technique to decipher tissue composition at the single-cell level and to inform on disease mechanisms, tumor heterogeneity, and the state of the immune microenvironment. Although multiple methods for the computational analysis of scRNA-seq data exist, their application in a clinical setting demands standardized and reproducible workflows, targeted to extract, condense, and display the clinically relevant information. To this end, we designed scAmpi (\n                    <jats:bold>S<\/jats:bold>\n                    ingle\n                    <jats:bold>C<\/jats:bold>\n                    ell\n                    <jats:bold>A<\/jats:bold>\n                    nalysis\n                    <jats:bold>m<\/jats:bold>\n                    RNA\n                    <jats:bold>pi<\/jats:bold>\n                    peline), a workflow that facilitates scRNA-seq analysis from raw read processing to informing on sample composition, clinically relevant gene and pathway alterations, and\n                    <jats:italic>in silico<\/jats:italic>\n                    identification of personalized candidate drug treatments. We demonstrate the value of this workflow for clinical decision making in a molecular tumor board as part of a clinical study.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1010097","type":"journal-article","created":{"date-parts":[[2022,6,3]],"date-time":"2022-06-03T13:56:08Z","timestamp":1654264568000},"page":"e1010097","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":12,"title":["scAmpi\u2014A versatile pipeline for single-cell RNA-seq analysis from basics to 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Lourdes","family":"Rosano-Gonz\u00e1lez","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5357-2705","authenticated-orcid":true,"given":"Jack","family":"Kuipers","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3163-3161","authenticated-orcid":true,"given":"Daniel Johannes","family":"Stekhoven","sequence":"additional","affiliation":[]},{"name":"Tumor Profiler 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