{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T19:44:45Z","timestamp":1740167085521,"version":"3.37.3"},"reference-count":29,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2018,1,3]],"date-time":"2018-01-03T00:00:00Z","timestamp":1514937600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001656","name":"Helmholtz-Gemeinschaft","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001656","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["f1000research.com"],"crossmark-restriction":false},"short-container-title":["F1000Res"],"abstract":"Single cell RNA-seq (scRNA-seq) experiments suffer from a range of characteristic technical biases, such as dropouts (zero or near zero counts) and high variance. Current analysis methods rely on imputing missing values by various means of local averaging or regression, often amplifying biases inherent in the data. We present netSmooth, a network-diffusion based method that uses priors for the covariance structure of gene expression profiles on scRNA-seq experiments in order to smooth expression values. We demonstrate that netSmooth improves clustering results of scRNA-seq experiments from distinct cell populations, time-course experiments, and cancer genomics. We provide an R package for our method, available at: https:\/\/github.com\/BIMSBbioinfo\/netSmooth.<\/ns4:p>","DOI":"10.12688\/f1000research.13511.1","type":"journal-article","created":{"date-parts":[[2018,1,3]],"date-time":"2018-01-03T10:47:37Z","timestamp":1514976457000},"page":"8","update-policy":"https:\/\/doi.org\/10.12688\/f1000research.crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["netSmooth: Network-smoothing based imputation for single cell RNA-seq"],"prefix":"10.12688","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3980-6469","authenticated-orcid":false,"given":"Jonathan","family":"Ronen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0468-0117","authenticated-orcid":false,"given":"Altuna","family":"Akalin","sequence":"additional","affiliation":[]}],"member":"2560","published-online":{"date-parts":[[2018,1,3]]},"reference":[{"key":"ref-1","doi-asserted-by":"publisher","first-page":"1145-1160","DOI":"10.1038\/nbt.3711","article-title":"Revealing the vectors of cellular identity with single-cell genomics.","volume":"34","author":"A Wagner","year":"2016","journal-title":"Nat Biotechnol."},{"key":"ref-2","doi-asserted-by":"publisher","first-page":"740-742","DOI":"10.1038\/nmeth.2967","article-title":"Bayesian approach to single-cell differential expression analysis.","volume":"11","author":"P Kharchenko","year":"2014","journal-title":"Nat 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Bioconductor package for differential expression analysis of digital gene expression data.","volume":"26","author":"M Robinson","year":"2010","journal-title":"Bioinformatics."},{"key":"ref-18","doi-asserted-by":"publisher","first-page":"193-196","DOI":"10.1126\/science.1245316","article-title":"Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells.","volume":"343","author":"Q Deng","year":"2014","journal-title":"Science."},{"key":"ref-19","doi-asserted-by":"publisher","first-page":"1396-1401","DOI":"10.1126\/science.1254257","article-title":"Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma.","volume":"344","author":"A Patel","year":"2014","journal-title":"Science."},{"key":"ref-20","doi-asserted-by":"publisher","first-page":"D746-D752","DOI":"10.1093\/nar\/gkv1045","article-title":"Expression Atlas update--an integrated database of gene and protein expression in humans, animals and plants.","volume":"44","author":"R Petryszak","year":"2016","journal-title":"Nucleic Acids Research."},{"key":"ref-21","doi-asserted-by":"publisher","first-page":"1109-1121","DOI":"10.1101\/gr.118992.110","article-title":"Prioritizing candidate disease genes by network-based boosting of genome-wide association data.","volume":"21","author":"I Lee","year":"2011","journal-title":"Genome Res."},{"key":"ref-22","doi-asserted-by":"publisher","first-page":"1179-1186","DOI":"10.1093\/bioinformatics\/btw777","article-title":"Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R.","volume":"33","author":"D McCarthy","year":"2017","journal-title":"Bioinformatics."},{"key":"ref-23","doi-asserted-by":"crossref","DOI":"10.1007\/978-0-387-21606-5","article-title":"The Elements of Statistical Learning","author":"T Hastie","year":"2001"},{"key":"ref-24","first-page":"2579-2605","article-title":"Visualizing high-dimensional data using t-SNE.","volume":"9","author":"L van der Maaten","year":"2008","journal-title":"J Mach Learn Res."},{"article-title":"entropy: Estimation of Entropy, Mutual Information and Related Quantities","year":"2014","author":"J Hausser","key":"ref-25"},{"key":"ref-26","first-page":"2837-2854","article-title":"Information theoretic measures for clusterings comparison: Variants, properties, normalization and correction for chance.","volume":"11","author":"N Vinh","year":"2010","journal-title":"J Mach Learn Res."},{"key":"ref-27","doi-asserted-by":"publisher","first-page":"207-210","DOI":"10.1093\/nar\/30.1.207","article-title":"Gene Expression Omnibus: NCBI gene expression and hybridization array data repository.","volume":"30","author":"R Edgar","year":"2002","journal-title":"Nucleic Acids Res."},{"key":"ref-28","doi-asserted-by":"publisher","DOI":"10.1101\/143289","article-title":"Bias, robustness and scalability in differential expression analysis of single-cell rna-seq data.","author":"C Soneson","year":"2017","journal-title":"bioRxiv."},{"key":"ref-29","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.1119064","article-title":"BIMSBbioinfo\/netSmooth: first release for zenodo (Version v0.1.0).","author":"J Ronen","year":"2017","journal-title":"Zenodo."}],"updated-by":[{"updated":{"date-parts":[[2018,1,24]],"date-time":"2018-01-24T00:00:00Z","timestamp":1516752000000},"DOI":"10.12688\/f1000research.13511.2","type":"new_version","source":"publisher","label":"New version"},{"updated":{"date-parts":[[2018,7,10]],"date-time":"2018-07-10T00:00:00Z","timestamp":1531180800000},"DOI":"10.12688\/f1000research.13511.3","type":"new_version","source":"publisher","label":"New 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The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","order":0,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","order":0,"name":"copyright-info","label":"Copyright"}]}}