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cellCounts: an R function for quantifying 10x Chromium single-cell RNA sequencing data
SUMMARY: The 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 expe...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10365925/ https://www.ncbi.nlm.nih.gov/pubmed/37462540 http://dx.doi.org/10.1093/bioinformatics/btad439 |
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author | Liao, Yang Raghu, Dinesh Pal, Bhupinder Mielke, Lisa A Shi, Wei |
author_facet | Liao, Yang Raghu, Dinesh Pal, Bhupinder Mielke, Lisa A Shi, Wei |
author_sort | Liao, Yang |
collection | PubMed |
description | SUMMARY: The 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. AVAILABILITY AND IMPLEMENTATION: 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. |
format | Online Article Text |
id | pubmed-10365925 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-103659252023-07-25 cellCounts: an R function for quantifying 10x Chromium single-cell RNA sequencing data Liao, Yang Raghu, Dinesh Pal, Bhupinder Mielke, Lisa A Shi, Wei Bioinformatics Applications Note SUMMARY: The 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. AVAILABILITY AND IMPLEMENTATION: 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. Oxford University Press 2023-07-18 /pmc/articles/PMC10365925/ /pubmed/37462540 http://dx.doi.org/10.1093/bioinformatics/btad439 Text en © The Author(s) 2023. Published by Oxford University Press. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Applications Note Liao, Yang Raghu, Dinesh Pal, Bhupinder Mielke, Lisa A Shi, Wei cellCounts: an R function for quantifying 10x Chromium single-cell RNA sequencing data |
title | cellCounts: an R function for quantifying 10x Chromium single-cell RNA sequencing data |
title_full | cellCounts: an R function for quantifying 10x Chromium single-cell RNA sequencing data |
title_fullStr | cellCounts: an R function for quantifying 10x Chromium single-cell RNA sequencing data |
title_full_unstemmed | cellCounts: an R function for quantifying 10x Chromium single-cell RNA sequencing data |
title_short | cellCounts: an R function for quantifying 10x Chromium single-cell RNA sequencing data |
title_sort | cellcounts: an r function for quantifying 10x chromium single-cell rna sequencing data |
topic | Applications Note |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10365925/ https://www.ncbi.nlm.nih.gov/pubmed/37462540 http://dx.doi.org/10.1093/bioinformatics/btad439 |
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