{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T19:07:17Z","timestamp":1781118437516,"version":"3.54.1"},"reference-count":104,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T00:00:00Z","timestamp":1607990400000},"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\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFC0910405"],"award-info":[{"award-number":["2018YFC0910405"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61922020"],"award-info":[{"award-number":["61922020"]}],"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":["61771331"],"award-info":[{"award-number":["61771331"]}],"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":["91935302"],"award-info":[{"award-number":["91935302"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at the cellular level. However, due to the extremely low levels of transcripts in a single cell and technical losses during reverse transcription, gene expression at a single-cell resolution is usually noisy and highly dimensional; thus, statistical analyses of single-cell data are a challenge. Although many scRNA-seq data analysis tools are currently available, a gold standard pipeline is not available for all datasets. Therefore, a general understanding of bioinformatics and associated computational issues would facilitate the selection of appropriate tools for a given set of data. In this review, we provide an overview of the goals and most popular computational analysis tools for the quality control, normalization, imputation, feature selection and dimension reduction of scRNA-seq data.<\/jats:p>","DOI":"10.1093\/bib\/bbaa314","type":"journal-article","created":{"date-parts":[[2020,12,12]],"date-time":"2020-12-12T12:39:12Z","timestamp":1607776752000},"source":"Crossref","is-referenced-by-count":43,"title":["Goals and approaches for each processing step for single-cell RNA sequencing data"],"prefix":"10.1093","volume":"22","author":[{"given":"Zilong","family":"Zhang","sequence":"first","affiliation":[{"name":"University of Electronic Science and Technology of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feifei","family":"Cui","sequence":"additional","affiliation":[{"name":"University of Tokyo, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2808-3771","authenticated-orcid":false,"given":"Lingling","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6406-1142","authenticated-orcid":false,"given":"Quan","family":"Zou","sequence":"additional","affiliation":[{"name":"Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,12,15]]},"reference":[{"issue":"1","key":"2021072117012032000_ref1","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1038\/nrg2484","article-title":"RNA-Seq: a revolutionary tool for transcriptomics","volume":"10","author":"Wang","year":"2009","journal-title":"Nat Rev Genet"},{"issue":"8","key":"2021072117012032000_ref2","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1038\/nbt.2282","article-title":"Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells","volume":"30","author":"Ramsk\u00f6ld","year":"2012","journal-title":"Nat Biotechnol"},{"issue":"6233","key":"2021072117012032000_ref3","doi-asserted-by":"crossref","first-page":"aaa6090","DOI":"10.1126\/science.aaa6090","article-title":"RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells","volume":"348","author":"Chen","year":"2015","journal-title":"Science"},{"issue":"10","key":"2021072117012032000_ref4","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1038\/nmeth.4407","article-title":"Massively parallel single-nucleus RNA-seq with DroNc-seq","volume":"14","author":"Habib","year":"2017","journal-title":"Nat Methods"},{"issue":"6335","key":"2021072117012032000_ref5","doi-asserted-by":"crossref","DOI":"10.1126\/science.aah4573","article-title":"Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors","volume":"356","author":"Villani","year":"2017","journal-title":"Science"},{"issue":"12","key":"2021072117012032000_ref6","doi-asserted-by":"crossref","first-page":"1860","DOI":"10.1101\/gr.192237.115","article-title":"Single-cell RNA-seq reveals changes in cell cycle and differentiation programs upon aging of hematopoietic stem cells","volume":"25","author":"Kowalczyk","year":"2015","journal-title":"Genome Res"},{"issue":"7","key":"2021072117012032000_ref7","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.1016\/j.cell.2016.11.039","article-title":"Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-Seq","volume":"167","author":"Jaitin","year":"2016","journal-title":"Cell"},{"issue":"3","key":"2021072117012032000_ref8","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.cels.2016.08.010","article-title":"Single-cell transcriptomics reveals that differentiation and spatial signatures shape epidermal and hair follicle heterogeneity","volume":"3","author":"Joost","year":"2016","journal-title":"Cell Syst"},{"issue":"1","key":"2021072117012032000_ref9","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.cell.2016.08.009","article-title":"Single-cell RNA-Seq reveals lineage and X chromosome dynamics in human preimplantation embryos","volume":"167","author":"Petropoulos","year":"2016","journal-title":"Cell"},{"issue":"12","key":"2021072117012032000_ref10","doi-asserted-by":"crossref","first-page":"1264","DOI":"10.1038\/nbt.3701","article-title":"Single-cell sequencing of the small-RNA transcriptome","volume":"34","author":"Faridani","year":"2016","journal-title":"Nat Biotechnol"},{"issue":"7628","key":"2021072117012032000_ref11","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":"Tirosh","year":"2016","journal-title":"Nature"},{"issue":"6","key":"2021072117012032000_ref12","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1016\/j.cell.2015.08.027","article-title":"Pathogen cell-to-cell variability drives heterogeneity in host immune responses","volume":"162","author":"Avraham","year":"2015","journal-title":"Cell"},{"issue":"4","key":"2021072117012032000_ref13","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/j.cels.2016.09.002","article-title":"A single-cell transcriptome atlas of the human pancreas","volume":"3","author":"Muraro","year":"2016","journal-title":"Cell Syst"},{"issue":"6","key":"2021072117012032000_ref14","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1038\/nmeth.2930","article-title":"Validation of noise models for single-cell transcriptomics","volume":"11","author":"Gr\u00fcn","year":"2014","journal-title":"Nat Methods"},{"key":"2021072117012032000_ref15","doi-asserted-by":"crossref","DOI":"10.12688\/f1000research.7223.1","article-title":"Single-cell transcriptome sequencing: recent advances and remaining challenges","volume":"5","author":"Liu","year":"2016","journal-title":"F1000Res"},{"key":"2021072117012032000_ref16","doi-asserted-by":"crossref","first-page":"14049","DOI":"10.1038\/ncomms14049","article-title":"Massively parallel digital transcriptional profiling of single cells","volume":"8","author":"Zheng","year":"2017","journal-title":"Nat Commun"},{"issue":"3","key":"2021072117012032000_ref17","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1038\/nrg3833","article-title":"Computational and analytical challenges in single-cell transcriptomics","volume":"16","author":"Stegle","year":"2015","journal-title":"Nat Rev Genet"},{"issue":"3","key":"2021072117012032000_ref18","first-page":"189","article-title":"Dissecting cellular heterogeneity using single-cell RNA sequencing","volume":"42","author":"Choi","year":"2019","journal-title":"Mol Cells"},{"key":"2021072117012032000_ref19","doi-asserted-by":"crossref","first-page":"1830","DOI":"10.1016\/j.csbj.2020.05.005","article-title":"Application of information theoretical approaches to assess diversity and similarity in single-cell transcriptomics","volume":"18","author":"Seweryn","year":"2020","journal-title":"Comput Struct Biotechnol J"},{"issue":"W1","key":"2021072117012032000_ref20","doi-asserted-by":"crossref","first-page":"W275","DOI":"10.1093\/nar\/gkaa394","article-title":"IRIS3: integrated cell-type-specific regulon inference server from single-cell RNA-Seq","volume":"48","author":"Ma","year":"2020","journal-title":"Nucleic Acids Res"},{"issue":"Suppl 24","key":"2021072117012032000_ref21","doi-asserted-by":"crossref","first-page":"672","DOI":"10.1186\/s12859-019-3243-1","article-title":"M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data","volume":"20","author":"Zhang","year":"2019","journal-title":"BMC Bioinformatics"},{"issue":"18","key":"2021072117012032000_ref22","doi-asserted-by":"crossref","first-page":"e111","DOI":"10.1093\/nar\/gkz655","article-title":"LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data","volume":"47","author":"Wan","year":"2019","journal-title":"Nucleic Acids Res"},{"issue":"6","key":"2021072117012032000_ref23","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1101\/gr.230771.117","article-title":"bigSCale: an analytical framework for big-scale single-cell data","volume":"28","author":"Iacono","year":"2018","journal-title":"Genome Res"},{"issue":"6352","key":"2021072117012032000_ref24","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1126\/science.aam8940","article-title":"Comprehensive single-cell transcriptional profiling of a multicellular organism","volume":"357","author":"Cao","year":"2017","journal-title":"Science"},{"issue":"3","key":"2021072117012032000_ref25","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1038\/nrg.2015.16","article-title":"Single-cell genome sequencing: current state of the science","volume":"17","author":"Gawad","year":"2016","journal-title":"Nat Rev Genet"},{"issue":"5","key":"2021072117012032000_ref26","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1038\/s41576-018-0088-9","article-title":"Challenges in unsupervised clustering of single-cell RNA-seq data","volume":"20","author":"Kiselev","year":"2019","journal-title":"Nat Rev Genet"},{"issue":"4","key":"2021072117012032000_ref27","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1093\/biostatistics\/kxx053","article-title":"Missing data and technical variability in single-cell RNA-sequencing experiments","volume":"19","author":"Hicks","year":"2018","journal-title":"Biostatistics"},{"issue":"8","key":"2021072117012032000_ref28","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1093\/bioinformatics\/btv715","article-title":"Destiny: diffusion maps for large-scale single-cell data in R","volume":"32","author":"Angerer","year":"2016","journal-title":"Bioinformatics"},{"issue":"1","key":"2021072117012032000_ref29","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1186\/s12859-016-1176-5","article-title":"FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data","volume":"17","author":"DeTomaso","year":"2016","journal-title":"BMC Bioinformatics"},{"issue":"3","key":"2021072117012032000_ref30","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1101\/gr.209601.116","article-title":"UMI-tools: modeling sequencing errors in unique molecular identifiers to improve quantification accuracy","volume":"27","author":"Smith","year":"2017","journal-title":"Genome Res"},{"issue":"7","key":"2021072117012032000_ref31","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1038\/s41581-020-0262-0","article-title":"Tools for the analysis of high-dimensional single-cell RNA sequencing data","volume":"16","author":"Wu","year":"2020","journal-title":"Nat Rev Nephrol"},{"issue":"5","key":"2021072117012032000_ref32","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1038\/nbt.4096","article-title":"Integrating single-cell transcriptomic data across different conditions, technologies, and species","volume":"36","author":"Butler","year":"2018","journal-title":"Nat Biotechnol"},{"key":"2021072117012032000_ref33","first-page":"2122","article-title":"A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor","volume":"5","author":"Lun","year":"2016","journal-title":"F1000Res"},{"issue":"4","key":"2021072117012032000_ref34","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1038\/nbt.2859","article-title":"The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells","volume":"32","author":"Trapnell","year":"2014","journal-title":"Nat Biotechnol"},{"key":"2021072117012032000_ref35","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.12688\/f1000research.15666.2","article-title":"A systematic performance evaluation of clustering methods for single-cell RNA-seq data","volume":"7","author":"Duo","year":"2018","journal-title":"F1000Res"},{"issue":"4","key":"2021072117012032000_ref36","doi-asserted-by":"crossref","first-page":"1196","DOI":"10.1093\/bib\/bbz062","article-title":"Clustering and classification methods for single-cell RNA-sequencing data","volume":"21","author":"Qi","year":"2020","journal-title":"Brief Bioinform"},{"key":"2021072117012032000_ref37","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1093\/bfgp\/elaa003","article-title":"Identifying cell types to interpret scRNA-seq data: how, why and more possibilities","volume":"19","author":"Wang","year":"2020","journal-title":"Brief Funct Genomics"},{"issue":"5","key":"2021072117012032000_ref38","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1038\/nmeth.1315","article-title":"mRNA-Seq whole-transcriptome analysis of a single cell","volume":"6","author":"Tang","year":"2009","journal-title":"Nat Methods"},{"issue":"6172","key":"2021072117012032000_ref39","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1126\/science.1247651","article-title":"Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types","volume":"343","author":"Jaitin","year":"2014","journal-title":"Science"},{"issue":"4","key":"2021072117012032000_ref40","doi-asserted-by":"crossref","first-page":"R31","DOI":"10.1186\/gb-2013-14-4-r31","article-title":"Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity","volume":"14","author":"Sasagawa","year":"2013","journal-title":"Genome Biol"},{"issue":"3","key":"2021072117012032000_ref41","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1016\/j.celrep.2012.08.003","article-title":"CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification","volume":"2","author":"Hashimshony","year":"2012","journal-title":"Cell Rep"},{"key":"2021072117012032000_ref42","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1186\/s13059-016-0938-8","article-title":"CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq","volume":"17","author":"Hashimshony","year":"2016","journal-title":"Genome Biol"},{"issue":"7","key":"2021072117012032000_ref43","doi-asserted-by":"crossref","first-page":"1160","DOI":"10.1101\/gr.110882.110","article-title":"Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq","volume":"21","author":"Islam","year":"2011","journal-title":"Genome Res"},{"issue":"11","key":"2021072117012032000_ref44","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1038\/nmeth.2639","article-title":"Smart-seq2 for sensitive full-length transcriptome profiling in single cells","volume":"10","author":"Picelli","year":"2013","journal-title":"Nat Methods"},{"issue":"5","key":"2021072117012032000_ref45","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1016\/j.cell.2015.04.044","article-title":"Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells","volume":"161","author":"Klein","year":"2015","journal-title":"Cell"},{"issue":"5","key":"2021072117012032000_ref46","doi-asserted-by":"crossref","first-page":"1202","DOI":"10.1016\/j.cell.2015.05.002","article-title":"Highly parallel genome-wide expression profiling of individual cells using Nanoliter droplets","volume":"161","author":"Macosko","year":"2015","journal-title":"Cell"},{"issue":"1","key":"2021072117012032000_ref47","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1186\/s13073-017-0467-4","article-title":"A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications","volume":"9","author":"Haque","year":"2017","journal-title":"Genome Med"},{"issue":"8","key":"2021072117012032000_ref48","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1038\/s12276-018-0071-8","article-title":"Single-cell RNA sequencing technologies and bioinformatics pipelines","volume":"50","author":"Hwang","year":"2018","journal-title":"Exp Mol Med"},{"issue":"4","key":"2021072117012032000_ref49","doi-asserted-by":"crossref","first-page":"1384","DOI":"10.1093\/bib\/bby007","article-title":"How to design a single-cell RNA-sequencing experiment: pitfalls, challenges and perspectives","volume":"20","author":"Dal Molin","year":"2019","journal-title":"Brief Bioinform"},{"issue":"2","key":"2021072117012032000_ref50","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1038\/nmeth.2772","article-title":"Quantitative single-cell RNA-seq with unique molecular identifiers","volume":"11","author":"Islam","year":"2014","journal-title":"Nat Methods"},{"issue":"6222","key":"2021072117012032000_ref51","doi-asserted-by":"crossref","first-page":"1258367","DOI":"10.1126\/science.1258367","article-title":"Expression profiling. Combinatorial labeling of single cells for gene expression cytometry","volume":"347","author":"Fan","year":"2015","journal-title":"Science"},{"issue":"6","key":"2021072117012032000_ref52","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1093\/bib\/bbs046","article-title":"A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis","volume":"14","author":"Dillies","year":"2013","journal-title":"Brief Bioinform"},{"key":"2021072117012032000_ref53","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1186\/s13059-016-0947-7","article-title":"Pooling across cells to normalize single-cell RNA sequencing data with many zero counts","volume":"17","author":"Lun","year":"2016","journal-title":"Genome Biol"},{"key":"2021072117012032000_ref54","article-title":"FASTQC. A quality control tool for high throughput sequence data","author":"Andrews","year":"2010"},{"issue":"1","key":"2021072117012032000_ref55","first-page":"3","article-title":"Cutadapt removes adapter sequences from high-throughput sequencing reads","volume":"17","author":"Martin","year":"2011","journal-title":"EMBnetjournal"},{"issue":"1","key":"2021072117012032000_ref56","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1186\/s13059-019-1670-y","article-title":"Alevin efficiently estimates accurate gene abundances from dscRNA-seq data","volume":"20","author":"Srivastava","year":"2019","journal-title":"Genome Biol"},{"issue":"1","key":"2021072117012032000_ref57","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s12859-019-2612-0","article-title":"Barcode identification for single cell genomics","volume":"20","author":"Tambe","year":"2019","journal-title":"BMC Bioinformatics"},{"issue":"12","key":"2021072117012032000_ref58","doi-asserted-by":"crossref","first-page":"1913","DOI":"10.1093\/bioinformatics\/btv053","article-title":"Starcode: sequence clustering based on all-pairs search","volume":"31","author":"Zorita","year":"2015","journal-title":"Bioinformatics"},{"issue":"1","key":"2021072117012032000_ref59","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1093\/bioinformatics\/bts635","article-title":"STAR: ultrafast universal RNA-seq aligner","volume":"29","author":"Dobin","year":"2013","journal-title":"Bioinformatics"},{"issue":"4","key":"2021072117012032000_ref60","doi-asserted-by":"crossref","first-page":"R36","DOI":"10.1186\/gb-2013-14-4-r36","article-title":"TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions","volume":"14","author":"Kim","year":"2013","journal-title":"Genome Biol"},{"issue":"4","key":"2021072117012032000_ref61","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1038\/nmeth.3317","article-title":"HISAT: a fast spliced aligner with low memory requirements","volume":"12","author":"Kim","year":"2015","journal-title":"Nat Methods"},{"key":"2021072117012032000_ref62","article-title":"Alexander Dobin, STARsolo: single-cell RNA-seq analyses beyond gene expression","volume":"8","author":"Ash Blibaum","year":"2019","journal-title":"F1000Research"},{"issue":"9","key":"2021072117012032000_ref63","doi-asserted-by":"crossref","first-page":"1543","DOI":"10.1101\/gr.121095.111","article-title":"Synthetic spike-in standards for RNA-seq experiments","volume":"21","author":"Jiang","year":"2011","journal-title":"Genome Res"},{"issue":"2","key":"2021072117012032000_ref64","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1038\/s41592-019-0654-x","article-title":"Orchestrating single-cell analysis with Bioconductor","volume":"17","author":"Amezquita","year":"2020","journal-title":"Nat Methods"},{"issue":"4","key":"2021072117012032000_ref65","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1093\/bioinformatics\/btp692","article-title":"RNA-Seq gene expression estimation with read mapping uncertainty","volume":"26","author":"Li","year":"2010","journal-title":"Bioinformatics"},{"issue":"2","key":"2021072117012032000_ref66","doi-asserted-by":"crossref","first-page":"e9","DOI":"10.1093\/nar\/gkq1015","article-title":"Accurate quantification of transcriptome from RNA-Seq data by effective length normalization","volume":"39","author":"Lee","year":"2010","journal-title":"Nucleic Acids Res"},{"issue":"10","key":"2021072117012032000_ref67","doi-asserted-by":"crossref","first-page":"R106","DOI":"10.1186\/gb-2010-11-10-r106","article-title":"Differential expression analysis for sequence count data","volume":"11","author":"Anders","year":"2010","journal-title":"Genome Biol"},{"issue":"12","key":"2021072117012032000_ref68","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1186\/s13059-014-0550-8","article-title":"Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2","volume":"15","author":"Love","year":"2014","journal-title":"Genome Biol"},{"issue":"6","key":"2021072117012032000_ref69","doi-asserted-by":"crossref","first-page":"e1004333","DOI":"10.1371\/journal.pcbi.1004333","article-title":"BASiCS: Bayesian analysis of single-cell sequencing data","volume":"11","author":"Vallejos","year":"2015","journal-title":"PLoS Comput Biol"},{"issue":"13","key":"2021072117012032000_ref70","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1093\/bioinformatics\/btv122","article-title":"Normalization and noise reduction for single cell RNA-seq experiments","volume":"31","author":"Ding","year":"2015","journal-title":"Bioinformatics"},{"issue":"22","key":"2021072117012032000_ref71","doi-asserted-by":"crossref","first-page":"e179","DOI":"10.1093\/nar\/gkx828","article-title":"Linnorm: improved statistical analysis for single cell RNA-seq expression data","volume":"45","author":"Yip","year":"2017","journal-title":"Nucleic Acids Res"},{"issue":"6","key":"2021072117012032000_ref72","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 Methods"},{"issue":"3","key":"2021072117012032000_ref73","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1038\/nmeth.4150","article-title":"Single-cell mRNA quantification and differential analysis with census","volume":"14","author":"Qiu","year":"2017","journal-title":"Nat Methods"},{"key":"2021072117012032000_ref74","doi-asserted-by":"crossref","first-page":"317","DOI":"10.3389\/fgene.2019.00317","article-title":"Single-cell RNA-Seq technologies and related computational data analysis","volume":"10","author":"Chen","year":"2019","journal-title":"Front Genet"},{"key":"2021072117012032000_ref75","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1186\/s13059-016-0927-y","article-title":"Design and computational analysis of single-cell RNA-sequencing experiments","volume":"17","author":"Bacher","year":"2016","journal-title":"Genome Biol"},{"key":"2021072117012032000_ref76","doi-asserted-by":"crossref","first-page":"8","DOI":"10.12688\/f1000research.13511.3","article-title":"netSmooth: network-smoothing based imputation for single cell RNA-seq","volume":"7","author":"Ronen","year":"2018","journal-title":"F1000Res"},{"issue":"7","key":"2021072117012032000_ref77","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":"Huang","year":"2018","journal-title":"Nat Methods"},{"issue":"1","key":"2021072117012032000_ref78","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":"Gong","year":"2018","journal-title":"BMC Bioinformatics"},{"issue":"1","key":"2021072117012032000_ref79","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1038\/s41467-018-03405-7","article-title":"An accurate and robust imputation method scImpute for single-cell RNA-seq data","volume":"9","author":"Li","year":"2018","journal-title":"Nat Commun"},{"issue":"1","key":"2021072117012032000_ref80","doi-asserted-by":"crossref","first-page":"16329","DOI":"10.1038\/s41598-018-34688-x","article-title":"AutoImpute: autoencoder based imputation of single-cell RNA-seq data","volume":"8","author":"Talwar","year":"2018","journal-title":"Sci Rep"},{"issue":"3","key":"2021072117012032000_ref81","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":"Dijk","year":"2018","journal-title":"Cell"},{"key":"2021072117012032000_ref82","first-page":"217737","article-title":"K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq data","author":"Wagner","year":"2018","journal-title":"bioRxiv"},{"issue":"2","key":"2021072117012032000_ref83","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1038\/s41587-019-0379-5","article-title":"Droplet scRNA-seq is not zero-inflated","volume":"38","author":"Svensson","year":"2020","journal-title":"Nat Biotechnol"},{"key":"2021072117012032000_ref84","article-title":"Separating measurement and expression models clarifies confusion in single cell RNA-seq analysis","author":"Sarkar","year":"2020","journal-title":"bioRxiv"},{"issue":"12","key":"2021072117012032000_ref85","doi-asserted-by":"crossref","first-page":"e1000598","DOI":"10.1371\/journal.pcbi.1000598","article-title":"An abundance of ubiquitously expressed genes revealed by tissue transcriptome sequence data","volume":"5","author":"Ramskold","year":"2009","journal-title":"PLoS Comput Biol"},{"issue":"1","key":"2021072117012032000_ref86","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1186\/s13059-019-1861-6","article-title":"Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model","volume":"20","author":"Townes","year":"2019","journal-title":"Genome Biol"},{"key":"2021072117012032000_ref87","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.12688\/f1000research.7035.1","article-title":"RNA-Seq workflow: gene-level exploratory analysis and differential expression","volume":"4","author":"Love","year":"2015","journal-title":"F1000Res"},{"issue":"1","key":"2021072117012032000_ref88","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1186\/s13059-016-1010-4","article-title":"GiniClust: detecting rare cell types from single-cell gene expression data with Gini index","volume":"17","author":"Jiang","year":"2016","journal-title":"Genome Biol"},{"issue":"16","key":"2021072117012032000_ref89","doi-asserted-by":"crossref","first-page":"2865","DOI":"10.1093\/bioinformatics\/bty1044","article-title":"M3Drop: dropout-based feature selection for scRNASeq","volume":"35","author":"Andrews","year":"2019","journal-title":"Bioinformatics"},{"issue":"5","key":"2021072117012032000_ref90","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1038\/ng.3818","article-title":"Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors","volume":"49","author":"Li","year":"2017","journal-title":"Nat Genet"},{"issue":"6167","key":"2021072117012032000_ref91","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1126\/science.1245316","article-title":"Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells","volume":"343","author":"Deng","year":"2014","journal-title":"Science"},{"key":"2021072117012032000_ref92","first-page":"1802.03426","article-title":"UMAP: uniform manifold approximation and projection for dimension reduction","author":"McInnes","year":"2018","journal-title":"arXiv e-prints"},{"issue":"6","key":"2021072117012032000_ref93","doi-asserted-by":"crossref","first-page":"e8746","DOI":"10.15252\/msb.20188746","article-title":"Current best practices in single-cell RNA-seq analysis: a tutorial","volume":"15","author":"Luecken","year":"2019","journal-title":"Mol Syst Biol"},{"issue":"4","key":"2021072117012032000_ref94","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.cels.2016.04.001","article-title":"Low dimensionality in gene expression data enables the accurate extraction of transcriptional programs from shallow sequencing","volume":"2","author":"Heimberg","year":"2016","journal-title":"Cell Syst"},{"key":"2021072117012032000_ref95","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1007\/978-3-642-04898-2_455","volume-title":"Principal Component Analysis, in International Encyclopedia of Statistical Science","author":"Jolliffe","year":"2011"},{"key":"2021072117012032000_ref96","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1186\/s13059-015-0805-z","article-title":"ZIFA: dimensionality reduction for zero-inflated single-cell gene expression analysis","volume":"16","author":"Pierson","year":"2015","journal-title":"Genome Biol"},{"issue":"1","key":"2021072117012032000_ref97","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1186\/s13059-017-1334-8","article-title":"F-scLVM: scalable and versatile factor analysis for single-cell RNA-seq","volume":"18","author":"Buettner","year":"2017","journal-title":"Genome Biol"},{"issue":"12","key":"2021072117012032000_ref98","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1038\/s41592-018-0229-2","article-title":"Deep generative modeling for single-cell transcriptomics","volume":"15","author":"Lopez","year":"2018","journal-title":"Nat Methods"},{"key":"2021072117012032000_ref99","first-page":"2579","article-title":"Visualizing high-dimensional data using t-SNE","volume-title":"J. Mach. Learn. Res.","author":"Maaten","year":"2008"},{"issue":"11","key":"2021072117012032000_ref100","doi-asserted-by":"crossref","first-page":"3522","DOI":"10.1093\/bioinformatics\/btaa189","article-title":"Projected t-SNE for batch correction","volume":"36","author":"Aliverti","year":"2020","journal-title":"Bioinformatics"},{"issue":"7","key":"2021072117012032000_ref101","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1038\/s41576-019-0122-6","article-title":"Deep learning: new computational modelling techniques for genomics","volume":"20","author":"Eraslan","year":"2019","journal-title":"Nat Rev Genet"},{"key":"2021072117012032000_ref102","first-page":"851","article-title":"Deep learning in bioinformatics","volume-title":"Brief. Bioinform.","author":"Min","year":"2017"},{"issue":"1","key":"2021072117012032000_ref103","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1038\/s41467-018-07931-2","article-title":"Single-cell RNA-seq denoising using a deep count autoencoder","volume":"10","author":"Eraslan","year":"2019","journal-title":"Nat Commun"},{"issue":"1","key":"2021072117012032000_ref104","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1186\/s13059-019-1806-0","article-title":"scBFA: modeling detection patterns to mitigate technical noise in large-scale single-cell genomics data","volume":"20","author":"Li","year":"2019","journal-title":"Genome Biol"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/22\/4\/bbaa314\/39136359\/bbaa314.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/22\/4\/bbaa314\/39136359\/bbaa314.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T17:16:26Z","timestamp":1626887786000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaa314\/6034054"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,15]]},"references-count":104,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,7,20]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaa314","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,7]]},"published":{"date-parts":[[2020,12,15]]},"article-number":"bbaa314"}}