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dc.contributor.authorHie, Brian
dc.contributor.authorPeters, Joshua
dc.contributor.authorNyquist, Sarah K
dc.contributor.authorShalek, Alex K
dc.contributor.authorBerger, Bonnie
dc.contributor.authorBryson, Bryan D
dc.date.accessioned2021-10-27T20:24:36Z
dc.date.available2021-10-27T20:24:36Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/1721.1/135681
dc.description.abstract<jats:p> Single-cell RNA sequencing (scRNA-seq) has provided a high-dimensional catalog of millions of cells across species and diseases. These data have spurred the development of hundreds of computational tools to derive novel biological insights. Here, we outline the components of scRNA-seq analytical pipelines and the computational methods that underlie these steps. We describe available methods, highlight well-executed benchmarking studies, and identify opportunities for additional benchmarking studies and computational methods. As the biochemical approaches for single-cell omics advance, we propose coupled development of robust analytical pipelines suited for the challenges that new data present and principled selection of analytical methods that are suited for the biological questions to be addressed. </jats:p>
dc.language.isoen
dc.publisherAnnual Reviews
dc.relation.isversionof10.1146/ANNUREV-BIODATASCI-012220-100601
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/
dc.sourceMIT web domain
dc.titleComputational Methods for Single-Cell RNA Sequencing
dc.typeArticle
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
dc.contributor.departmentMassachusetts Institute of Technology. Department of Biological Engineering
dc.contributor.departmentRagon Institute of MGH, MIT and Harvard
dc.contributor.departmentMassachusetts Institute of Technology. Computational and Systems Biology Program
dc.contributor.departmentMassachusetts Institute of Technology. Department of Chemistry
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Medical Engineering & Science
dc.contributor.departmentKoch Institute for Integrative Cancer Research at MIT
dc.contributor.departmentMassachusetts Institute of Technology. Department of Mathematics
dc.relation.journalAnnual Review of Biomedical Data Science
dc.eprint.versionAuthor's final manuscript
dc.type.urihttp://purl.org/eprint/type/JournalArticle
eprint.statushttp://purl.org/eprint/status/PeerReviewed
dc.date.updated2021-05-17T18:00:50Z
dspace.orderedauthorsHie, B; Peters, J; Nyquist, SK; Shalek, AK; Berger, B; Bryson, BD
dspace.date.submission2021-05-17T18:00:51Z
mit.journal.volume3
mit.journal.issue1
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Needed


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