{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T01:19:51Z","timestamp":1775524791116,"version":"3.50.1"},"reference-count":21,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T00:00:00Z","timestamp":1711497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"New Zealand\u2013China Non-Communicable Diseases Research","award":["UOOX1601"],"award-info":[{"award-number":["UOOX1601"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>In recent years, improvements in throughput of single-cell RNA-seq have resulted in a significant increase in the number of cells profiled. The generation of single-cell RNA-seq datasets comprising &amp;gt;1 million cells is becoming increasingly common, giving rise to demands for more efficient computational workflows.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We present an update to our single-cell RNA-seq analysis web server application, ICARUS (available at https:\/\/launch.icarus-scrnaseq.cloud.edu.au) that allows effective analysis of large-scale single-cell RNA-seq datasets. ICARUS v3 utilizes the geometric cell sketching method to subsample cells from the overall dataset for dimensionality reduction and clustering that can be then projected to the large dataset. We then extend this functionality to select a representative subset of cells for downstream data analysis applications including differential expression analysis, gene co-expression network construction, gene regulatory network construction, trajectory analysis, cell\u2013cell communication inference, and cell cluster associations to GWAS traits. We demonstrate analysis of single-cell RNA-seq datasets using ICARUS v3 of 1.3 million cells completed within the hour.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>ICARUS is available at https:\/\/launch.icarus-scrnaseq.cloud.edu.au.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae167","type":"journal-article","created":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T18:57:39Z","timestamp":1711393059000},"source":"Crossref","is-referenced-by-count":6,"title":["ICARUS v3, a massively scalable web server for single-cell RNA-seq analysis of millions of cells"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7267-013X","authenticated-orcid":false,"given":"Andrew","family":"Jiang","sequence":"first","affiliation":[{"name":"Applied Translational Genetics Group, School of Biological Sciences, The University of Auckland , Auckland 1142, New Zealand"}]},{"given":"Russell G","family":"Snell","sequence":"additional","affiliation":[{"name":"Applied Translational Genetics Group, School of Biological Sciences, The University of Auckland , Auckland 1142, New Zealand"}]},{"given":"Klaus","family":"Lehnert","sequence":"additional","affiliation":[{"name":"Applied Translational Genetics Group, School of Biological Sciences, The University of Auckland , Auckland 1142, New Zealand"}]}],"member":"286","published-online":{"date-parts":[[2024,3,27]]},"reference":[{"key":"2024041105303492000_btae167-B1","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1038\/s41586-020-2496-1","article-title":"A single-cell transcriptomic atlas characterizes ageing tissues in the mouse","volume":"583","author":"Almanzar","year":"2020","journal-title":"Nature"},{"key":"2024041105303492000_btae167-B2","doi-asserted-by":"crossref","first-page":"821","DOI":"10.1002\/hep4.1854","article-title":"Single-cell, single-nucleus, and spatial RNA sequencing of the human liver identifies cholangiocyte and mesenchymal heterogeneity","volume":"6","author":"Andrews","year":"2022","journal-title":"Hepatol Commun"},{"key":"2024041105303492000_btae167-B3","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1038\/s41590-018-0276-y","article-title":"Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage","volume":"20","author":"Aran","year":"2019","journal-title":"Nat Immunol"},{"key":"2024041105303492000_btae167-B4","doi-asserted-by":"crossref","first-page":"1355","DOI":"10.1038\/s41592-023-01938-4","article-title":"SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks","volume":"20","author":"Bravo 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