dc.contributor.author | Hie, Brian | |
dc.contributor.author | Peters, Joshua | |
dc.contributor.author | Nyquist, Sarah K | |
dc.contributor.author | Shalek, Alex K | |
dc.contributor.author | Berger, Bonnie | |
dc.contributor.author | Bryson, Bryan D | |
dc.date.accessioned | 2021-10-27T20:24:36Z | |
dc.date.available | 2021-10-27T20:24:36Z | |
dc.date.issued | 2020 | |
dc.identifier.uri | https://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.iso | en | |
dc.publisher | Annual Reviews | |
dc.relation.isversionof | 10.1146/ANNUREV-BIODATASCI-012220-100601 | |
dc.rights | Creative Commons Attribution-Noncommercial-Share Alike | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/4.0/ | |
dc.source | MIT web domain | |
dc.title | Computational Methods for Single-Cell RNA Sequencing | |
dc.type | Article | |
dc.contributor.department | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Biological Engineering | |
dc.contributor.department | Ragon Institute of MGH, MIT and Harvard | |
dc.contributor.department | Massachusetts Institute of Technology. Computational and Systems Biology Program | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Chemistry | |
dc.contributor.department | Massachusetts Institute of Technology. Institute for Medical Engineering & Science | |
dc.contributor.department | Koch Institute for Integrative Cancer Research at MIT | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Mathematics | |
dc.relation.journal | Annual Review of Biomedical Data Science | |
dc.eprint.version | Author's final manuscript | |
dc.type.uri | http://purl.org/eprint/type/JournalArticle | |
eprint.status | http://purl.org/eprint/status/PeerReviewed | |
dc.date.updated | 2021-05-17T18:00:50Z | |
dspace.orderedauthors | Hie, B; Peters, J; Nyquist, SK; Shalek, AK; Berger, B; Bryson, BD | |
dspace.date.submission | 2021-05-17T18:00:51Z | |
mit.journal.volume | 3 | |
mit.journal.issue | 1 | |
mit.license | OPEN_ACCESS_POLICY | |
mit.metadata.status | Authority Work and Publication Information Needed | |