{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T09:45:38Z","timestamp":1768729538962,"version":"3.49.0"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T00:00:00Z","timestamp":1603065600000},"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\/501100000923","name":"Australian Research Council","doi-asserted-by":"publisher","award":["DP180100120"],"award-info":[{"award-number":["DP180100120"]}],"id":[{"id":"10.13039\/501100000923","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><jats:p>Single-cell mRNA sequencing has been adopted as a powerful technique for understanding gene expression profiles at the single-cell level. However, challenges remain due to factors such as the inefficiency of mRNA molecular capture, technical noises and separate sequencing of cells in different batches. Normalization methods have been developed to ensure a relatively accurate analysis. This work presents a survey on 10 tools specifically designed for single-cell mRNA sequencing data preprocessing steps, among which 6 tools are used for dropout normalization and 4 tools are for batch effect correction. In this survey, we outline the main methodology for each of these tools, and we also compare these tools to evaluate their normalization performance on datasets which are simulated under the constraints of dropout inefficiency, batch effect or their combined effects. We found that Saver and Baynorm performed better than other methods in dropout normalization, in most cases. Beer and Batchelor performed better in the batch effect normalization, and the Saver\u2013Beer tool combination and the Baynorm\u2013Beer combination performed better in the mixed dropout-and-batch effect normalization. Over-normalization is a common issue occurred to these dropout normalization tools that is worth of future investigation. For the batch normalization tools, the capability of retaining heterogeneity between different groups of cells after normalization can be another direction for future improvement.<\/jats:p>","DOI":"10.1093\/bib\/bbaa248","type":"journal-article","created":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T11:20:24Z","timestamp":1599218424000},"source":"Crossref","is-referenced-by-count":5,"title":["Sequencing dropout-and-batch effect normalization for single-cell mRNA profiles: a survey and comparative analysis"],"prefix":"10.1093","volume":"22","author":[{"given":"Tian","family":"Lan","sequence":"first","affiliation":[{"name":"Faculty of Engineering and IT in the University of Technology Sydney"}]},{"given":"Gyorgy","family":"Hutvagner","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering, University of Technology Sydney"}]},{"given":"Qing","family":"Lan","sequence":"additional","affiliation":[{"name":"Neurosurgical Department of Second Affiliated Hospital of Soochow University"}]},{"given":"Tao","family":"Liu","sequence":"additional","affiliation":[{"name":"Children\u2019s Cancer Institute Australia"}]},{"given":"Jinyan","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Engineering and IT in the University of Technology Sydney"}]}],"member":"286","published-online":{"date-parts":[[2020,10,19]]},"reference":[{"issue":"5","key":"2021072112192678400_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":"2021072112192678400_ref2","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1093\/bioinformatics\/bts714","article-title":"Data exploration, quality control and testing in single-cell qPCR-based gene expression 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