Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/33399
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dc.contributor.authorLiao, Yang-
dc.contributor.authorRaghu, Dinesh-
dc.contributor.authorPal, Bhupinder-
dc.contributor.authorMielke, Lisa A-
dc.contributor.authorShi, Wei-
dc.date.accessioned2023-07-26T06:37:04Z-
dc.date.available2023-07-26T06:37:04Z-
dc.date.issued2023-07-01-
dc.identifier.citationBioinformatics (Oxford, England) 2023-07-01; 39(7)en_US
dc.identifier.issn1367-4811-
dc.identifier.urihttps://ahro.austin.org.au/austinjspui/handle/1/33399-
dc.description.abstractThe 10x Genomics Chromium single-cell RNA sequencing technology is a powerful gene expression profiling platform, which is capable of profiling expression of thousands of genes in tens of thousands of cells simultaneously. This platform can produce hundreds of million reads in a single experiment, making it a very challenging task to quantify expression of genes in individual cells due to the massive data volume. Here, we present cellCounts, a new tool for efficient and accurate quantification of Chromium data. cellCounts employs the seed-and-vote strategy to align reads to a reference genome, collapses reads to Unique Molecular Identifiers (UMIs) and then assigns UMIs to genes based on the featureCounts program. Using both simulation and real datasets for evaluation, cellCounts was found to compare favourably to cellRanger and STARsolo. cellCounts is implemented in R, making it easily integrated with other R programs for analysing Chromium data. cellCounts was implemented as a function in R package Rsubread that can be downloaded from http://bioconductor.org/packages/release/bioc/html/Rsubread.html. Data and analysis code used in this study can be freely accessed via La Trobe University's Institutional Repository at https://doi.org/10.26181/21588276.en_US
dc.language.isoeng-
dc.titlecellCounts: an R function for quantifying 10x Chromium single-cell RNA sequencing data.en_US
dc.typeJournal Articleen_US
dc.identifier.journaltitleBioinformatics (Oxford, England)en_US
dc.identifier.affiliationOlivia Newton-John Cancer Research Instituteen_US
dc.identifier.affiliationSchool of Cancer Medicine, La Trobe University, Bundoora, Victoria 3086, Australia.en_US
dc.identifier.doi10.1093/bioinformatics/btad439en_US
dc.type.contentTexten_US
dc.identifier.orcid0000-0003-1182-7735en_US
dc.identifier.pubmedid37462540-
dc.description.volume39-
dc.description.issue7-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.openairetypeJournal Article-
item.grantfulltextnone-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.languageiso639-1en-
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