{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:33:28Z","timestamp":1772138008536,"version":"3.50.1"},"reference-count":75,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T00:00:00Z","timestamp":1594080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,5,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>With the development of single-cell RNA sequencing (scRNA-seq) technology, it has become possible to perform large-scale transcript profiling for tens of thousands of cells in a single experiment. Many analysis pipelines have been developed for data generated from different high-throughput scRNA-seq platforms, bringing a new challenge to users to choose a proper workflow that is efficient, robust and reliable for a specific sequencing platform. Moreover, as the amount of public scRNA-seq data has increased rapidly, integrated analysis of scRNA-seq data from different sources has become increasingly popular. However, it remains unclear whether such integrated analysis would be biassed if the data were processed by different upstream pipelines. In this study, we encapsulated seven existing high-throughput scRNA-seq data processing pipelines with Nextflow, a general integrative workflow management framework, and evaluated their performance in terms of running time, computational resource consumption and data analysis consistency using eight public datasets generated from five different high-throughput scRNA-seq platforms. Our work provides a useful guideline for the selection of scRNA-seq data processing pipelines based on their performance on different real datasets. In addition, these guidelines can serve as a performance evaluation framework for future developments in high-throughput scRNA-seq data processing.<\/jats:p>","DOI":"10.1093\/bib\/bbaa116","type":"journal-article","created":{"date-parts":[[2020,5,18]],"date-time":"2020-05-18T15:21:23Z","timestamp":1589815283000},"source":"Crossref","is-referenced-by-count":18,"title":["Comparison of high-throughput single-cell RNA sequencing data processing pipelines"],"prefix":"10.1093","volume":"22","author":[{"given":"Mingxuan","family":"Gao","sequence":"first","affiliation":[{"name":"Xiamen University"}]},{"given":"Mingyi","family":"Ling","sequence":"additional","affiliation":[{"name":"Xiamen University"}]},{"given":"Xinwei","family":"Tang","sequence":"additional","affiliation":[{"name":"Xiamen University"}]},{"given":"Shun","family":"Wang","sequence":"additional","affiliation":[{"name":"Xiamen University"}]},{"given":"Xu","family":"Xiao","sequence":"additional","affiliation":[{"name":"Xiamen University"}]},{"given":"Ying","family":"Qiao","sequence":"additional","affiliation":[{"name":"Xiamen University"}]},{"given":"Wenxian","family":"Yang","sequence":"additional","affiliation":[{"name":"CTO of Aginome Scientific"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2179-173X","authenticated-orcid":false,"given":"Rongshan","family":"Yu","sequence":"additional","affiliation":[{"name":"Digital Fujian Institute of Healthcare and Biomedical Big Data, School of Informatic, Xiamen University"}]}],"member":"286","published-online":{"date-parts":[[2020,7,7]]},"reference":[{"issue":"5","key":"2021052110025695200_ref1","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":"4","key":"2021052110025695200_ref2","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1016\/j.molcel.2017.01.023","article-title":"Comparative analysis of single-cell RNA sequencing methods","volume":"65","author":"Ziegenhain","year":"2017","journal-title":"Mol Cell"},{"issue":"1","key":"2021052110025695200_ref3","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.molcel.2018.10.020","article-title":"Comparative analysis of droplet-based ultra-high throughput single-cell RNA-seq systems","volume":"73","author":"Zhang","year":"2019","journal-title":"Mol Cell"},{"issue":"15","key":"2021052110025695200_ref4","doi-asserted-by":"crossref","first-page":"2213","DOI":"10.1002\/1873-3468.12684","article-title":"Computational approaches for interpreting scRNA-seq data","volume":"591","author":"Rostom","year":"2017","journal-title":"FEBS Lett"},{"issue":"8","key":"2021052110025695200_ref5","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1038\/nbt.2325","article-title":"Transcriptome sequencing of single cells with smart-Seq","volume":"30","author":"Goetz","year":"2012","journal-title":"Nat Biotechnol"},{"issue":"8","key":"2021052110025695200_ref6","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":"5","key":"2021052110025695200_ref7","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":"5","key":"2021052110025695200_ref8","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 ebryonic stem cells","volume":"161","author":"Klein","year":"2015","journal-title":"Cell"},{"key":"2021052110025695200_ref9","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1126\/science.aam8999","article-title":"Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding","volume":"360","author":"Rosenberg","year":"2018","journal-title":"Science"},{"key":"2021052110025695200_ref10","first-page":"33","article-title":"High throughput single cell RNA sequencing, bioinformatics analysis and applications","volume":"1068","author":"Huang","year":"2018","journal-title":"Single Cell Biomedicine"},{"key":"2021052110025695200_ref11","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":"4","key":"2021052110025695200_ref12","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1038\/nmeth.4220","article-title":"Power analysis of single-cell RNA-sequencing experiments","volume":"14","author":"Svensson","year":"2017","journal-title":"Nat Methods"},{"issue":"3","key":"2021052110025695200_ref13","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":"1","key":"2021052110025695200_ref14","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1186\/s13059-018-1449-6","article-title":"dropEst: pipeline for accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments","volume":"19","author":"Petukhov","year":"2018","journal-title":"Genome Biol"},{"issue":"8","key":"2021052110025695200_ref15","doi-asserted-by":"crossref","first-page":"e1006361","DOI":"10.1371\/journal.pcbi.1006361","article-title":"scPipe: a flexible R\/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data","volume":"14","author":"Tian","year":"2018","journal-title":"PLoS Comput Biol"},{"issue":"6","key":"2021052110025695200_ref16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/gigascience\/giy059","article-title":"zUMIs \u2013 a fast and flexible pipeline to process RNA sequencing data with UMIs","volume":"7","author":"Parekh","year":"2018","journal-title":"Giga Science"},{"key":"2021052110025695200_ref17","doi-asserted-by":"crossref","first-page":"2937","DOI":"10.1038\/s41467-018-05347-6","article-title":"Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq","volume":"9","author":"Bagnoli","year":"2018","journal-title":"Nat Commun"},{"issue":"4","key":"2021052110025695200_ref18","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s40484-016-0089-7","article-title":"Differential expression analyses for single-cell RNA-Seq: old questions on new data","volume":"4","author":"Miao","year":"2016","journal-title":"Quantative Biology"},{"key":"2021052110025695200_ref19","article-title":"Comparison of computational methods for imputing single-cell RNA-sequencing data","author":"Zhang","year":"2017","journal-title":"IEEE\/ACM Trans Comput Biol Bioinform"},{"issue":"4","key":"2021052110025695200_ref20","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1093\/bib\/bby011","article-title":"Evaluation of tools for highly variable gene discovery from single-cell RNA-seq data","volume":"20","author":"Yip","year":"2018","journal-title":"Brief Bioinform"},{"issue":"6","key":"2021052110025695200_ref21","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1038\/s41592-019-0425-8","article-title":"Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments","volume":"16","author":"Tian","year":"2019","journal-title":"Nat Methods"},{"key":"2021052110025695200_ref22","first-page":"bbz063","article-title":"Machine learning and statistical methods for clustering single-cell RNA-sequencing data","author":"Petegrosso","year":"2019","journal-title":"Brief Bioinform"},{"issue":"4","key":"2021052110025695200_ref23","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1093\/bfgp\/elx044","article-title":"Clustering single cells: a review of approaches on high-and low-depth single-cell RNA-seq data","volume":"17","author":"Menon","year":"2018","journal-title":"Brief Funct Genomics"},{"issue":"1","key":"2021052110025695200_ref24","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1186\/s13059-019-1795-z","article-title":"A comparison of automatic cell identification methods for single-cell RNA sequencing data","volume":"20","author":"Abdelaal","year":"2019","journal-title":"Genome Biol"},{"key":"2021052110025695200_ref25","first-page":"bbz096","article-title":"Evaluation of single-cell classifiers for single-cell RNA sequencing data sets","author":"Zhao","year":"2019","journal-title":"Brief Bioinform"},{"issue":"1","key":"2021052110025695200_ref26","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1186\/s13059-019-1863-4","article-title":"Systematic comparative analysis of single-nucleotide variant detection methods from single-cell RNA sequencing data","volume":"20","author":"Liu","year":"2019","journal-title":"Genome Biol"},{"key":"2021052110025695200_ref27","doi-asserted-by":"crossref","first-page":"4667","DOI":"10.1038\/s41467-019-12266-7","article-title":"A systematic evaluation of single cell RNA-seq analysis pipelines","volume":"20","author":"Vieth","year":"2019","journal-title":"Nat Commun"},{"issue":"4","key":"2021052110025695200_ref28","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1038\/nbt.3820","article-title":"Nextflow enables reproducible computational workflows","volume":"35","author":"Di Tommaso","year":"2017","journal-title":"Nat Biotechnol"},{"issue":"6","key":"2021052110025695200_ref29","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":"2","key":"2021052110025695200_ref30","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":"2013","journal-title":"Nat Methods"},{"issue":"1","key":"2021052110025695200_ref31","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1038\/nmeth.1778","article-title":"Counting absolute numbers of molecules using unique molecular identifiers","volume":"9","author":"Kivioja","year":"2011","journal-title":"Nat Methods"},{"key":"2021052110025695200_ref32","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.coisb.2017.12.007","article-title":"Methods and challenges in the analysis of single-cell RNA-sequencing data","volume":"7","author":"Camara","year":"2018","journal-title":"Current Opinion in Systems Biology"},{"key":"2021052110025695200_ref33","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"},{"issue":"5","key":"2021052110025695200_ref34","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1038\/nbt.3519","article-title":"Near-optimal probabilistic RNA-seq quantification","volume":"34","author":"Bray","year":"2016","journal-title":"Nat Biotechnol"},{"issue":"12","key":"2021052110025695200_ref35","doi-asserted-by":"crossref","first-page":"i192","DOI":"10.1093\/bioinformatics\/btw277","article-title":"Rapmap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomes","volume":"32","author":"Srivastava","year":"2016","journal-title":"Bioinformatics"},{"issue":"10","key":"2021052110025695200_ref36","doi-asserted-by":"crossref","first-page":"e108","DOI":"10.1093\/nar\/gkt214","article-title":"The subread aligner: fast, accurate and scalable read mapping by seed-and-vote","volume":"41","author":"Liao","year":"2013","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"2021052110025695200_ref37","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"},{"key":"2021052110025695200_ref38","doi-asserted-by":"crossref","first-page":"614","DOI":"10.3389\/fgene.2019.00614","article-title":"Pipeliner: a Nextflow-based framework for the definition of sequencing data processing pipelines","volume":"10","author":"Federico","year":"2019","journal-title":"Front Genet"},{"key":"2021052110025695200_ref39","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jproteome.9b00328","article-title":"ThermoRawFileParser: modular, scalable, and cross-platform raw file conversion","author":"Hulstaert","year":"2019","journal-title":"J Proteome Res"},{"key":"2021052110025695200_ref40","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1007\/978-1-4939-9074-0_24","article-title":"Scalable workflows and reproducible data analysis for genomics","volume":"1910","author":"Strozzi","year":"2019","journal-title":"Methods Mol Biol"},{"key":"2021052110025695200_ref41","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.isci.2018.10.023","article-title":"Pergola: boosting visualization and analysis of longitudinal data by unlocking genomic analysis tools","volume":"9","author":"Espinosa-Carrasco","year":"2018","journal-title":"iScience"},{"key":"2021052110025695200_ref42","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1186\/s12859-018-2446-1","article-title":"Developing reproducible bioinformatics analysis workflows for heterogeneous computing environments to support African genomics","volume":"19","author":"Baichoo","year":"2018","journal-title":"BMC Bioinformatics"},{"issue":"7","key":"2021052110025695200_ref43","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.jgg.2018.06.005","article-title":"LncPipe: a Nextflow-based pipeline for identification and analysis of long non-coding RNAs from RNA-Seq data","volume":"45","author":"Zhao","year":"2018","journal-title":"J Genet Genomics"},{"issue":"4","key":"2021052110025695200_ref44","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1038\/nmeth.4179","article-title":"Seq-well: portable, low-cost RNA sequencing of single cells at high throughput","volume":"14","author":"Geirahn","year":"2017","journal-title":"Nat Methods"},{"key":"2021052110025695200_ref45","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s13059-018-1407-3","article-title":"Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads","volume":"19","author":"Sasagawa","year":"2018","journal-title":"Genome Biol"},{"issue":"S7","key":"2021052110025695200_ref46","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1186\/s12864-016-2897-6","article-title":"Detection of high variability in gene expression from single-cell RNA-seq profiling","volume":"17","author":"Chen","year":"2016","journal-title":"BMC Genomics"},{"key":"2021052110025695200_ref47","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"},{"key":"2021052110025695200_ref48","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"},{"issue":"11","key":"2021052110025695200_ref49","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1038\/nmeth.2645","article-title":"Accounting for technical noise in single-cell RNA-seq experiments","volume":"10","author":"Brennecke","year":"2013","journal-title":"Nat Methods"},{"issue":"16","key":"2021052110025695200_ref50","doi-asserted-by":"crossref","first-page":"2865","DOI":"10.1093\/bioinformatics\/bty1044","article-title":"M3Drop: dropout-based feature selection for scRNA-seq","volume":"35","author":"Andrews","year":"2019","journal-title":"Bioinformatics"},{"issue":"5","key":"2021052110025695200_ref51","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1038\/nbt.3192","article-title":"Spatial reconstruction of single-cell gene expression data","volume":"33","author":"Satija","year":"2015","journal-title":"Nat Biotechnol"},{"issue":"5","key":"2021052110025695200_ref52","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1038\/nmeth.4236","article-title":"SC3: consensus clustering of single-cell RNA-seq data","volume":"14","author":"Kiselev","year":"2017","journal-title":"Nat Methods"},{"issue":"1","key":"2021052110025695200_ref53","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12859-016-0984-y","article-title":"pcaReduce: hierarchical clustering of single cell transcriptional profiles","volume":"17","author":"Zurauskiene","year":"2016","journal-title":"BMC Bioinformatics"},{"issue":"5","key":"2021052110025695200_ref54","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1038\/nmeth.4662","article-title":"FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data","volume":"15","author":"Herman","year":"2018","journal-title":"Nat Methods"},{"key":"2021052110025695200_ref55","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.mam.2017.07.002","article-title":"Identifying cell populations with scRNASeq","volume":"59","author":"Andrews","year":"2017","journal-title":"Mol Aspects Med"},{"key":"2021052110025695200_ref56","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":"D1","key":"2021052110025695200_ref57","doi-asserted-by":"crossref","first-page":"D721","DOI":"10.1093\/nar\/gky900","article-title":"CellMarker: a manually curated resource of cell markers in human and mouse","volume":"47","author":"Zhang","year":"2019","journal-title":"Nucleic Acids Res"},{"key":"2021052110025695200_ref58","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s40425-017-0215-8","article-title":"Gene expression markers of tumor infiltrating leukocytes","volume":"5","author":"Danaher","year":"2017","journal-title":"J Immunother Cancer"},{"issue":"8","key":"2021052110025695200_ref59","first-page":"1251","article-title":"Clonal replacement of tumor-specific T cells following PD-1 blockade","volume":"25","author":"Danaher","year":"2019","journal-title":"Nat Methods"},{"issue":"8","key":"2021052110025695200_ref60","doi-asserted-by":"crossref","first-page":"e48","DOI":"10.1093\/nar\/gkz116","article-title":"SuperCT: a supervised-learning framework for enhanced characterization of single-cell transcriptomic profiles","volume":"47","author":"Xie","year":"2019","journal-title":"Nucleic Acids Res"},{"issue":"3","key":"2021052110025695200_ref61","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"},{"issue":"10","key":"2021052110025695200_ref62","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1038\/nmeth.4402","article-title":"Reversed graph embedding resolves complex single-cell trajectories","volume":"14","author":"Qiu","year":"2017","journal-title":"Nat Methods"},{"issue":"1","key":"2021052110025695200_ref63","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1093\/bioinformatics\/btp616","article-title":"edgeR: a Bioconductor package for differential expression analysis of digital gene expression data","volume":"26","author":"Robinson","year":"2010","journal-title":"Bioinformatics"},{"key":"2021052110025695200_ref64","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1186\/s13059-015-0844-5","article-title":"MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data","volume":"16","author":"Finak","year":"2015","journal-title":"Genome Biol"},{"issue":"18","key":"2021052110025695200_ref65","doi-asserted-by":"crossref","first-page":"3223","DOI":"10.1093\/bioinformatics\/bty332","article-title":"DEsingle for detecting three types of differential expression in single-cell RNA-seq data","volume":"34","author":"Miao","year":"2018","journal-title":"Bioinformatics"},{"issue":"4","key":"2021052110025695200_ref66","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":"Molin","year":"2018","journal-title":"Brief Bioinform"},{"key":"2021052110025695200_ref67","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1186\/1471-2164-6-150","article-title":"Proposed methods for testing and selecting the ERCC external RNA controls","volume":"6","author":"External RNA Controls Consortium","year":"2005","journal-title":"BMC Genomics"},{"issue":"9","key":"2021052110025695200_ref68","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"},{"key":"2021052110025695200_ref69","doi-asserted-by":"crossref","first-page":"5125","DOI":"10.1038\/ncomms6125","article-title":"Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures","volume":"5","author":"Munro","year":"2014","journal-title":"Nat Commun"},{"issue":"3","key":"2021052110025695200_ref70","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"},{"issue":"5","key":"2021052110025695200_ref71","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1016\/j.cell.2018.02.001","article-title":"Mapping the mouse cell atlas by microwell-seq","volume":"172","author":"Han","year":"2018","journal-title":"Cell"},{"key":"2021052110025695200_ref72","doi-asserted-by":"crossref","first-page":"2163","DOI":"10.1038\/s41467-019-10122-2","article-title":"Hydro-Seq enables contamination-free high throughput single-cell RNA-sequencing for circulating tumor cells","volume":"10","author":"Cheng","year":"2019","journal-title":"Nat Commun"},{"issue":"5","key":"2021052110025695200_ref73","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1038\/nbt.4091","article-title":"Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors","volume":"36","author":"Haghverdi","year":"2018","journal-title":"Nat Biotechnol"},{"issue":"16","key":"2021052110025695200_ref74","doi-asserted-by":"crossref","first-page":"2539","DOI":"10.1093\/bioinformatics\/btx196","article-title":"Removal of batch effects using distribution-matching residual networks","volume":"33","author":"Shaham","year":"2017","journal-title":"Bioinformatics"},{"issue":"1","key":"2021052110025695200_ref75","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1186\/s13059-019-1764-6","article-title":"BERMUDA: a novel deep transfer learning method for single-cell RNA sequencing batch correction reveals hidden high-resolution cellular subtypes","volume":"20","author":"Wang","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\/3\/bbaa116\/37966155\/bbaa116.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"http:\/\/academic.oup.com\/bib\/article-pdf\/22\/3\/bbaa116\/37966155\/bbaa116.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,5,21]],"date-time":"2021-05-21T06:04:06Z","timestamp":1621577046000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbaa116\/5868074"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,7]]},"references-count":75,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,5,20]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbaa116","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2020.02.09.940221","asserted-by":"object"}]},"ISSN":["1477-4054"],"issn-type":[{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,5]]},"published":{"date-parts":[[2020,7,7]]},"article-number":"bbaa116"}}