{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T02:56:17Z","timestamp":1783479377365,"version":"3.55.0"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"W1","license":[{"start":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T00:00:00Z","timestamp":1652140800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"New Zealand Ministry of Business Innovation and Employment","award":["UOOX1601"],"award-info":[{"award-number":["UOOX1601"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Here we present ICARUS, a web server to enable users without experience in R to undertake single cell RNA-seq analysis. The focal point of ICARUS is its intuitive tutorial-style user interface, designed to guide logical navigation through the multitude of pre-processing, analysis and visualization steps. ICARUS is easily accessible through a dedicated web server (https:\/\/launch.icarus-scrnaseq.cloud.edu.au\/) and avoids installation of software on the user's computer. Notable features include the facility to apply quality control thresholds and adjust dimensionality reduction and cell clustering parameters. Data is visualized through 2D\/3D UMAP and t-SNE plots and may be curated to remove potential confounders such as cell cycle heterogeneity. ICARUS offers flexible differential expression analysis with user-defined cell groups and gene set enrichment analysis to identify likely affected biological pathways. Eleven organisms including human, dog, mouse, rat, zebrafish, fruit fly, nematode, yeast, cattle, chicken\u00a0and pig are currently supported. Visualization of multimodal data including those generated by CITE-seq and the 10X Genomics Multiome kit is included. ICARUS incorporates a function to save the current state of analysis avoiding computationally intensive steps during repeat analysis. The complete analysis of a typical single cell RNA-seq dataset by inexperienced users may be achieved in 1\u20132 h.<\/jats:p>","DOI":"10.1093\/nar\/gkac322","type":"journal-article","created":{"date-parts":[[2022,4,21]],"date-time":"2022-04-21T11:17:40Z","timestamp":1650539860000},"page":"W427-W433","source":"Crossref","is-referenced-by-count":46,"title":["ICARUS, an interactive web server for single cell RNA-seq analysis"],"prefix":"10.1093","volume":"50","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 , New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klaus","family":"Lehnert","sequence":"additional","affiliation":[{"name":"Applied Translational Genetics Group, School of Biological Sciences, The University of Auckland , Auckland , New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2569-0269","authenticated-orcid":false,"given":"Linya","family":"You","sequence":"additional","affiliation":[{"name":"Department of Human Anatomy & Histoembryology, School of Basic Medical Sciences, Fudan University , Shanghai , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Russell G","family":"Snell","sequence":"additional","affiliation":[{"name":"Applied Translational Genetics Group, School of Biological Sciences, The University of Auckland , Auckland , New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,5,10]]},"reference":[{"key":"2022070423592942400_B1","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.1016\/j.cell.2021.04.048","article-title":"Integrated analysis of multimodal single-cell data","volume":"184","author":"Hao","year":"2021","journal-title":"Cell"},{"key":"2022070423592942400_B2","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1038\/nbt.4096","article-title":"Integrating single-cell transcriptomic data across different conditions, technologies, and species","volume":"36","author":"Butler","year":"2018","journal-title":"Nat. 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