{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T06:18:34Z","timestamp":1772173114693,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1009849","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T00:00:00Z","timestamp":1646179200000}}],"reference-count":37,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T00:00:00Z","timestamp":1645056000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"cologne graduate school of ageing research","award":["EXC 2030\/1 - 390661388"],"award-info":[{"award-number":["EXC 2030\/1 - 390661388"]}]},{"DOI":"10.13039\/501100001809","name":"national natural science foundation of china","doi-asserted-by":"publisher","award":["61871463"],"award-info":[{"award-number":["61871463"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"natural science foundation of fujian province","doi-asserted-by":"publisher","award":["2017J01068"],"award-info":[{"award-number":["2017J01068"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (\u2018dropout imputation\u2019). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Further, it is unknown if all genes equally benefit from imputation or which imputation method works best for a given gene. Here, we show that a transcriptional regulatory network learned from external, independent gene expression data improves dropout imputation. Using a variety of human scRNA-seq datasets we demonstrate that our network-based approach outperforms published state-of-the-art methods. The network-based approach performs particularly well for lowly expressed genes, including cell-type-specific transcriptional regulators. Further, the cell-to-cell variation of 11.3% to 48.8% of the genes could not be adequately imputed by any of the methods that we tested. In those cases gene expression levels were best predicted by the mean expression across all cells, i.e. assuming no measurable expression variation between cells. These findings suggest that different imputation methods are optimal for different genes. We thus implemented an R-package called ADImpute (available via Bioconductor\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/bioconductor.org\/packages\/release\/bioc\/html\/ADImpute.html\" xlink:type=\"simple\">https:\/\/bioconductor.org\/packages\/release\/bioc\/html\/ADImpute.html<\/jats:ext-link>\n                    ) that automatically determines the best imputation method for each gene in a dataset. Our work represents a paradigm shift by demonstrating that there is no single best imputation method. Instead, we propose that imputation should maximally exploit external information and be adapted to gene-specific features, such as expression level and expression variation across cells.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1009849","type":"journal-article","created":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T13:50:16Z","timestamp":1645105816000},"page":"e1009849","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":12,"title":["Regulatory network-based imputation of dropouts in single-cell RNA sequencing data"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0879-328X","authenticated-orcid":true,"given":"Ana Carolina","family":"Leote","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0356-7785","authenticated-orcid":true,"given":"Xiaohui","family":"Wu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3891-2123","authenticated-orcid":true,"given":"Andreas","family":"Beyer","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2022,2,17]]},"reference":[{"key":"pcbi.1009849.ref001","doi-asserted-by":"crossref","first-page":"2789","DOI":"10.1016\/j.csbj.2020.09.014","article-title":"Naught all zeros in sequence count data are the same","volume":"18","author":"JD Silverman","year":"2020","journal-title":"Comput Struct Biotechnol J"},{"issue":"4","key":"pcbi.1009849.ref002","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaa314","article-title":"Goals and approaches for each processing step for single-cell RNA sequencing data","volume":"22","author":"Z Zhang","year":"2021","journal-title":"Brief Bioinform"},{"issue":"1740","key":"pcbi.1009849.ref003","first-page":"7","article-title":"False signals induced by single-cell imputation [version 2; peer review: 4 approved].","author":"T Andrews","year":"2019","journal-title":"F1000Research [Internet]."},{"issue":"3","key":"pcbi.1009849.ref004","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1016\/j.cell.2018.05.061","article-title":"Recovering Gene Interactions from Single-Cell Data Using Data Diffusion","volume":"174","author":"D van Dijk","year":"2018","journal-title":"Cell"},{"issue":"Mar1","key":"pcbi.1009849.ref005","first-page":"997","article-title":"An accurate and robust imputation method scImpute for single-cell RNA-seq data","volume":"9","author":"WV Li","year":"2018","journal-title":"Nat Commun."},{"issue":"1","key":"pcbi.1009849.ref006","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1186\/s12859-018-2226-y","article-title":"DrImpute: imputing dropout events in single cell RNA sequencing data","volume":"19","author":"W Gong","year":"2018","journal-title":"BMC Bioinformatics"},{"issue":"8","key":"pcbi.1009849.ref007","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1089\/cmb.2018.0236","article-title":"Locality Sensitive Imputation for Single Cell RNA-Seq Data","volume":"26","author":"M Moussa","year":"2019","journal-title":"J Comput Biol"},{"key":"pcbi.1009849.ref008","first-page":"217737","article-title":"K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq data","author":"F Wagner","year":"2018","journal-title":"bioRxiv"},{"issue":"7","key":"pcbi.1009849.ref009","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1038\/s41592-018-0033-z","article-title":"SAVER: gene expression recovery for single-cell RNA sequencing","volume":"15","author":"M Huang","year":"2018","journal-title":"Nat Methods"},{"issue":"5","key":"pcbi.1009849.ref010","doi-asserted-by":"crossref","first-page":"e1009029","DOI":"10.1371\/journal.pcbi.1009029","article-title":"G2S3: A gene graph-based imputation method for single-cell RNA sequencing data","volume":"17","author":"W Wu","year":"2021","journal-title":"PLOS Comput Biol"},{"issue":"1","key":"pcbi.1009849.ref011","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1186\/s13059-019-1681-8","article-title":"SCRABBLE: single-cell RNA-seq imputation constrained by bulk RNA-seq data","volume":"20","author":"T Peng","year":"2019","journal-title":"Genome Biol"},{"key":"pcbi.1009849.ref012","doi-asserted-by":"crossref","DOI":"10.21236\/ADA472998","author":"N Meinshausen","year":"2006","journal-title":"Lasso-type recovery of sparse representations for high-dimensional data"},{"issue":"4","key":"pcbi.1009849.ref013","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1111\/j.1467-9868.2010.00740.x","article-title":"Stability selection","volume":"72","author":"N Meinshausen","year":"2010","journal-title":"J R Stat Soc Ser B Stat Methodol"},{"issue":"1","key":"pcbi.1009849.ref014","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1186\/s13059-016-1058-1","article-title":"Importance of rare gene copy number alterations for personalized tumor characterization and survival analysis","volume":"17","author":"M Seifert","year":"2016","journal-title":"Genome Biol"},{"issue":"6","key":"pcbi.1009849.ref015","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1038\/s41587-020-0497-0","article-title":"Single-cell RNA counting at allele and isoform resolution using Smart-seq3","volume":"38","author":"M Hagemann-Jensen","year":"2020","journal-title":"Nat Biotechnol"},{"issue":"1","key":"pcbi.1009849.ref016","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1186\/s13059-016-1033-x","article-title":"Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm","volume":"17","author":"L-F Chu","year":"2016","journal-title":"Genome Biol"},{"issue":"7628","key":"pcbi.1009849.ref017","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1038\/nature20123","article-title":"Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma","volume":"539","author":"I Tirosh","year":"2016","journal-title":"Nature"},{"issue":"7835","key":"pcbi.1009849.ref018","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1038\/s41586-020-2922-4","article-title":"A molecular cell atlas of the human lung from single-cell RNA sequencing","volume":"587","author":"KJ Travaglini","year":"2020","journal-title":"Nature"},{"issue":"5","key":"pcbi.1009849.ref019","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1016\/j.ccell.2021.02.015","article-title":"Tumor and immune reprogramming during immunotherapy in advanced renal cell carcinoma","volume":"39","author":"K Bi","year":"2021","journal-title":"Cancer Cell"},{"key":"pcbi.1009849.ref020","doi-asserted-by":"crossref","unstructured":"Zhang L, Zhang S. 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