{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T17:20:00Z","timestamp":1781112000011,"version":"3.54.1"},"reference-count":36,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2016,9,23]],"date-time":"2016-09-23T00:00:00Z","timestamp":1474588800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"funder":[{"name":"German Ministry for Education and Research"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,1,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Single-cell transcriptome data provide unprecedented resolution to study heterogeneity in cell populations and present a challenge for unsupervised classification. Popular methods, like principal component analysis (PCA), often suffer from the high level of noise in the data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Here we adapt Nonnegative Matrix Factorization (NMF) to study the problem of identifying subpopulations in single-cell transcriptome data. In contrast to the conventional gene-centered view of NMF, identifying metagenes, we used NMF in a cell-centered direction, identifying cell subtypes (\u2018metacells\u2019). Using three different datasets (based on RT-qPCR and single cell RNA-seq data, respectively), we show that NMF outperforms PCA in identifying subpopulations in an accurate and robust way, without the need for prior feature selection; moreover, NMF successfully recovered the broad classes on a large dataset (thousands of single-cell transcriptomes), as identified by a computationally sophisticated method. NMF allows to identify feature genes in a direct, unbiased manner. We propose novel approaches for determining a biologically meaningful number of subpopulations based on minimizing the ambiguity of classification. In conclusion, our study shows that NMF is a robust, informative and simple method for the unsupervised learning of cell subtypes from single-cell gene expression data.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and Implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/ccshao\/nimfa<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btw607","type":"journal-article","created":{"date-parts":[[2016,9,24]],"date-time":"2016-09-24T01:11:09Z","timestamp":1474679469000},"page":"235-242","source":"Crossref","is-referenced-by-count":113,"title":["Robust classification of single-cell transcriptome data by nonnegative matrix factorization"],"prefix":"10.1093","volume":"33","author":[{"given":"Chunxuan","family":"Shao","sequence":"first","affiliation":[{"name":"Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany"},{"name":"Bioquant Center, University of Heidelberg, Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"H\u00f6fer","sequence":"additional","affiliation":[{"name":"Division of Theoretical Systems Biology, German Cancer Research Center (DKFZ), Heidelberg, Germany"},{"name":"Bioquant Center, University of Heidelberg, Heidelberg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2016,9,23]]},"reference":[{"key":"2023020204303387600_btw607-B1","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1038\/ni.2842","article-title":"Early specification of CD8+ T lymphocyte fates during adaptive immunity revealed by single-cell gene-expression analyses","volume":"15","author":"Arsenio","year":"2014","journal-title":"Nat. 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