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scruff: an R/Bioconductor package for preprocessing single-cell RNA-sequencing data

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) enables the high-throughput quantification of transcriptional profiles in single cells. In contrast to bulk RNA-seq, additional preprocessing steps such as cell barcode identification or unique molecular identifier (UMI) deconvolution are necessary...

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Detalles Bibliográficos
Autores principales: Wang, Zhe, Hu, Junming, Johnson, W. Evan, Campbell, Joshua D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6498700/
https://www.ncbi.nlm.nih.gov/pubmed/31046658
http://dx.doi.org/10.1186/s12859-019-2797-2
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author Wang, Zhe
Hu, Junming
Johnson, W. Evan
Campbell, Joshua D.
author_facet Wang, Zhe
Hu, Junming
Johnson, W. Evan
Campbell, Joshua D.
author_sort Wang, Zhe
collection PubMed
description BACKGROUND: Single-cell RNA sequencing (scRNA-seq) enables the high-throughput quantification of transcriptional profiles in single cells. In contrast to bulk RNA-seq, additional preprocessing steps such as cell barcode identification or unique molecular identifier (UMI) deconvolution are necessary for preprocessing of data from single cell protocols. R packages that can easily preprocess data and rapidly visualize quality metrics and read alignments for individual cells across multiple samples or runs are still lacking. RESULTS: Here we present scruff, an R/Bioconductor package that preprocesses data generated from the CEL-Seq or CEL-Seq2 protocols and reports comprehensive data quality metrics and visualizations. scruff rapidly demultiplexes, aligns, and counts the reads mapped to genome features with deduplication of unique molecular identifier (UMI) tags. scruff also provides novel and extensive functions to visualize both pre- and post-alignment data quality metrics for cells from multiple experiments. Detailed read alignments with corresponding UMI information can be visualized at specific genome coordinates to display differences in isoform usage. The package also supports the visualization of quality metrics for sequence alignment files for multiple experiments generated by Cell Ranger from 10X Genomics. scruff is available as a free and open-source R/Bioconductor package. CONCLUSIONS: scruff streamlines the preprocessing of scRNA-seq data in a few simple R commands. It performs data demultiplexing, alignment, counting, quality report and visualization systematically and comprehensively, ensuring reproducible and reliable analysis of scRNA-seq data.
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spelling pubmed-64987002019-05-09 scruff: an R/Bioconductor package for preprocessing single-cell RNA-sequencing data Wang, Zhe Hu, Junming Johnson, W. Evan Campbell, Joshua D. BMC Bioinformatics Software BACKGROUND: Single-cell RNA sequencing (scRNA-seq) enables the high-throughput quantification of transcriptional profiles in single cells. In contrast to bulk RNA-seq, additional preprocessing steps such as cell barcode identification or unique molecular identifier (UMI) deconvolution are necessary for preprocessing of data from single cell protocols. R packages that can easily preprocess data and rapidly visualize quality metrics and read alignments for individual cells across multiple samples or runs are still lacking. RESULTS: Here we present scruff, an R/Bioconductor package that preprocesses data generated from the CEL-Seq or CEL-Seq2 protocols and reports comprehensive data quality metrics and visualizations. scruff rapidly demultiplexes, aligns, and counts the reads mapped to genome features with deduplication of unique molecular identifier (UMI) tags. scruff also provides novel and extensive functions to visualize both pre- and post-alignment data quality metrics for cells from multiple experiments. Detailed read alignments with corresponding UMI information can be visualized at specific genome coordinates to display differences in isoform usage. The package also supports the visualization of quality metrics for sequence alignment files for multiple experiments generated by Cell Ranger from 10X Genomics. scruff is available as a free and open-source R/Bioconductor package. CONCLUSIONS: scruff streamlines the preprocessing of scRNA-seq data in a few simple R commands. It performs data demultiplexing, alignment, counting, quality report and visualization systematically and comprehensively, ensuring reproducible and reliable analysis of scRNA-seq data. BioMed Central 2019-05-02 /pmc/articles/PMC6498700/ /pubmed/31046658 http://dx.doi.org/10.1186/s12859-019-2797-2 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Wang, Zhe
Hu, Junming
Johnson, W. Evan
Campbell, Joshua D.
scruff: an R/Bioconductor package for preprocessing single-cell RNA-sequencing data
title scruff: an R/Bioconductor package for preprocessing single-cell RNA-sequencing data
title_full scruff: an R/Bioconductor package for preprocessing single-cell RNA-sequencing data
title_fullStr scruff: an R/Bioconductor package for preprocessing single-cell RNA-sequencing data
title_full_unstemmed scruff: an R/Bioconductor package for preprocessing single-cell RNA-sequencing data
title_short scruff: an R/Bioconductor package for preprocessing single-cell RNA-sequencing data
title_sort scruff: an r/bioconductor package for preprocessing single-cell rna-sequencing data
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6498700/
https://www.ncbi.nlm.nih.gov/pubmed/31046658
http://dx.doi.org/10.1186/s12859-019-2797-2
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