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dc.contributor.authorVans, E
dc.contributor.authorPatil, A
dc.contributor.authorSharma, A
dc.date.accessioned2021-08-30T06:07:31Z
dc.date.available2021-08-30T06:07:31Z
dc.date.issued2021
dc.identifier.issn1467-5463
dc.identifier.doi10.1093/bib/bbaa306
dc.identifier.urihttp://hdl.handle.net/10072/407386
dc.description.abstractMOTIVATION: Advances in next-generation sequencing have made it possible to carry out transcriptomic studies at single-cell resolution and generate vast amounts of single-cell RNA sequencing (RNA-seq) data rapidly. Thus, tools to analyze this data need to evolve as well as to improve accuracy and efficiency. RESULTS: We present FEATS, a Python software package, that performs clustering on single-cell RNA-seq data. FEATS is capable of performing multiple tasks such as estimating the number of clusters, conducting outlier detection and integrating data from various experiments. We develop a univariate feature selection-based approach for clustering, which involves the selection of top informative features to improve clustering performance. This is motivated by the fact that cell types are often manually determined using the expression of only a few known marker genes. On a variety of single-cell RNA-seq datasets, FEATS gives superior performance compared with the current tools, in terms of adjusted Rand index and estimating the number of clusters. It achieves a 22% improvement in clustering and more accurately estimates the number of clusters when compared with other tools. In addition to cluster estimation, FEATS also performs outlier detection and data integration while giving an excellent computational performance. Thus, FEATS is a comprehensive clustering tool capable of addressing the challenges during the clustering of single-cell RNA-seq data. AVAILABILITY: The installation instructions and documentation of FEATS is available at https://edwinv87.github.io/feats/. SUPPLEMENTARY DATA: Supplementary data are available online at https://academic.oup.com/bib.
dc.description.peerreviewedYes
dc.languageeng
dc.publisherOxford University Press (OUP)
dc.relation.ispartofpagefrombbaa306
dc.relation.ispartofissue4
dc.relation.ispartofjournalBriefings in bioinformatics
dc.relation.ispartofvolume22
dc.subject.fieldofresearchBiochemistry and cell biology
dc.subject.fieldofresearchTheory of computation
dc.subject.fieldofresearchOther information and computing sciences
dc.subject.fieldofresearchcode3101
dc.subject.fieldofresearchcode4613
dc.subject.fieldofresearchcode4699
dc.subject.keywordsfeature selection
dc.subject.keywordshierarchical clustering
dc.subject.keywordssingle cell RNA-sequencing
dc.titleFEATS: feature selection-based clustering of single-cell RNA-seq data
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationVans, E; Patil, A; Sharma, A, FEATS: feature selection-based clustering of single-cell RNA-seq data, Briefings in bioinformatics, 2021, 22 (4), pp. bbaa306
dcterms.dateAccepted2020-10-11
dc.date.updated2021-08-30T04:35:01Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2021 Oxford University Press. This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Briefings in Bioinformatics following peer review. The definitive publisher-authenticated version FEATS: feature selection-based clustering of single-cell RNA-seq data, Briefings in bioinformatics, 2021, 22 (4), pp. bbaa306 is available online at: https://doi.org/10.1093/bib/bbaa306.
gro.hasfulltextFull Text
gro.griffith.authorSharma, Alok


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