{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T04:23:58Z","timestamp":1773980638038,"version":"3.50.1"},"reference-count":40,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2020,3,2]],"date-time":"2020-03-02T00:00:00Z","timestamp":1583107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11971039"],"award-info":[{"award-number":["11971039"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["11471022"],"award-info":[{"award-number":["11471022"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71532001"],"award-info":[{"award-number":["71532001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Basic Research Project of China","award":["2016YFC0207705"],"award-info":[{"award-number":["2016YFC0207705"]}]},{"name":"Recruitment Program of Global Youth Experts of China"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,5,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Single cell RNA-sequencing (scRNA-seq) technology enables whole transcriptome profiling at single cell resolution and holds great promises in many biological and medical applications. Nevertheless, scRNA-seq often fails to capture expressed genes, leading to the prominent dropout problem. These dropouts cause many problems in down-stream analysis, such as significant increase of noises, power loss in differential expression analysis and obscuring of gene-to-gene or cell-to-cell relationship. Imputation of these dropout values can be beneficial in scRNA-seq data analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In this article, we model the dropout imputation problem as robust matrix decomposition. This model has minimal assumptions and allows us to develop a computational efficient imputation method called scRMD. Extensive data analysis shows that scRMD can accurately recover the dropout values and help to improve downstream analysis such as differential expression analysis and clustering analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The R package scRMD is available at https:\/\/github.com\/XiDsLab\/scRMD.<\/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\/btaa139","type":"journal-article","created":{"date-parts":[[2020,2,26]],"date-time":"2020-02-26T15:18:20Z","timestamp":1582730300000},"page":"3156-3161","source":"Crossref","is-referenced-by-count":51,"title":["scRMD: imputation for single cell RNA-seq data via robust matrix decomposition"],"prefix":"10.1093","volume":"36","author":[{"given":"Chong","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Probability and Statistics , School of Mathematical Sciences, Peking University, Beijing 100871, China"},{"name":"Damo Academy , Alibaba Group, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changjing","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Probability and Statistics , School of Mathematical Sciences, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linjie","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Probability and Statistics , School of Mathematical Sciences, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaochen","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Probability and Statistics , School of Mathematical Sciences, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9143-1898","authenticated-orcid":false,"given":"Minghua","family":"Deng","sequence":"additional","affiliation":[{"name":"Department of Probability and Statistics , School of Mathematical Sciences, Peking University, Beijing 100871, China"},{"name":"Center for Statistical Sciences , Peking University, Beijing 100871, China"},{"name":"Center for Quantitative Biology , Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7545-7361","authenticated-orcid":false,"given":"Ruibin","family":"Xi","sequence":"additional","affiliation":[{"name":"Department of Probability and Statistics , School of Mathematical Sciences, Peking University, Beijing 100871, China"},{"name":"Center for Statistical Sciences , Peking University, Beijing 100871, China"},{"name":"Department of Biostatistics , Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2020,3,2]]},"reference":[{"key":"2023013111534277700_btaa139-B1","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1186\/s12915-017-0383-5","article-title":"Cell fixation and preservation for droplet-based single-cell transcriptomics","volume":"15","author":"Alles","year":"2017","journal-title":"BMC Biol"},{"key":"2023013111534277700_btaa139-B2","doi-asserted-by":"crossref","first-page":"1740","DOI":"10.12688\/f1000research.16613.1","article-title":"False signals induced by single-cell imputation","volume":"7","author":"Andrews","year":"2018","journal-title":"F1000Research"},{"key":"2023013111534277700_btaa139-B3","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1038\/nmeth.4263","article-title":"SCnorm: robust normalization of single-cell RNA-seq data","volume":"14","author":"Bacher","year":"2017","journal-title":"Nat. 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