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scQCEA: a framework for annotation and quality control report of single-cell RNA-sequencing data
BACKGROUND: Systematic description of library quality and sequencing performance of single-cell RNA sequencing (scRNA-seq) data is imperative for subsequent downstream modules, including re-pooling libraries. While several packages have been developed to visualise quality control (QC) metrics for sc...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10327311/ https://www.ncbi.nlm.nih.gov/pubmed/37415108 http://dx.doi.org/10.1186/s12864-023-09447-6 |
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author | Nassiri, Isar Fairfax, Benjamin Lee, Angela Wu, Yanxia Buck, David Piazza, Paolo |
author_facet | Nassiri, Isar Fairfax, Benjamin Lee, Angela Wu, Yanxia Buck, David Piazza, Paolo |
author_sort | Nassiri, Isar |
collection | PubMed |
description | BACKGROUND: Systematic description of library quality and sequencing performance of single-cell RNA sequencing (scRNA-seq) data is imperative for subsequent downstream modules, including re-pooling libraries. While several packages have been developed to visualise quality control (QC) metrics for scRNA-seq data, they do not include expression-based QC to discriminate between true variation and background noise. RESULTS: We present scQCEA (acronym of the single-cell RNA sequencing Quality Control and Enrichment Analysis), an R package to generate reports of process optimisation metrics for comparing sets of samples and visual evaluation of quality scores. scQCEA can import data from 10X or other single-cell platforms and includes functions for generating an interactive report of QC metrics for multi-omics data. In addition, scQCEA provides automated cell type annotation on scRNA-seq data using differential gene expression patterns for expression-based quality control. We provide a repository of reference gene sets, including 2348 marker genes, which are exclusively expressed in 95 human and mouse cell types. Using scRNA-seq data from 56 gene expressions and V(D)J T cell replicates, we show how scQCEA can be applied for the visual evaluation of quality scores for sets of samples. In addition, we use the summary of QC measures from 342 human and mouse shallow-sequenced gene expression profiles to specify optimal sequencing requirements to run a cell-type enrichment analysis function. CONCLUSIONS: The open-source R tool will allow examining biases and outliers over biological and technical measures, and objective selection of optimal cluster numbers before downstream analysis. scQCEA is available at https://isarnassiri.github.io/scQCEA/ as an R package. Full documentation, including an example, is provided on the package website. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09447-6. |
format | Online Article Text |
id | pubmed-10327311 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-103273112023-07-08 scQCEA: a framework for annotation and quality control report of single-cell RNA-sequencing data Nassiri, Isar Fairfax, Benjamin Lee, Angela Wu, Yanxia Buck, David Piazza, Paolo BMC Genomics Software BACKGROUND: Systematic description of library quality and sequencing performance of single-cell RNA sequencing (scRNA-seq) data is imperative for subsequent downstream modules, including re-pooling libraries. While several packages have been developed to visualise quality control (QC) metrics for scRNA-seq data, they do not include expression-based QC to discriminate between true variation and background noise. RESULTS: We present scQCEA (acronym of the single-cell RNA sequencing Quality Control and Enrichment Analysis), an R package to generate reports of process optimisation metrics for comparing sets of samples and visual evaluation of quality scores. scQCEA can import data from 10X or other single-cell platforms and includes functions for generating an interactive report of QC metrics for multi-omics data. In addition, scQCEA provides automated cell type annotation on scRNA-seq data using differential gene expression patterns for expression-based quality control. We provide a repository of reference gene sets, including 2348 marker genes, which are exclusively expressed in 95 human and mouse cell types. Using scRNA-seq data from 56 gene expressions and V(D)J T cell replicates, we show how scQCEA can be applied for the visual evaluation of quality scores for sets of samples. In addition, we use the summary of QC measures from 342 human and mouse shallow-sequenced gene expression profiles to specify optimal sequencing requirements to run a cell-type enrichment analysis function. CONCLUSIONS: The open-source R tool will allow examining biases and outliers over biological and technical measures, and objective selection of optimal cluster numbers before downstream analysis. scQCEA is available at https://isarnassiri.github.io/scQCEA/ as an R package. Full documentation, including an example, is provided on the package website. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12864-023-09447-6. BioMed Central 2023-07-06 /pmc/articles/PMC10327311/ /pubmed/37415108 http://dx.doi.org/10.1186/s12864-023-09447-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Software Nassiri, Isar Fairfax, Benjamin Lee, Angela Wu, Yanxia Buck, David Piazza, Paolo scQCEA: a framework for annotation and quality control report of single-cell RNA-sequencing data |
title | scQCEA: a framework for annotation and quality control report of single-cell RNA-sequencing data |
title_full | scQCEA: a framework for annotation and quality control report of single-cell RNA-sequencing data |
title_fullStr | scQCEA: a framework for annotation and quality control report of single-cell RNA-sequencing data |
title_full_unstemmed | scQCEA: a framework for annotation and quality control report of single-cell RNA-sequencing data |
title_short | scQCEA: a framework for annotation and quality control report of single-cell RNA-sequencing data |
title_sort | scqcea: a framework for annotation and quality control report of single-cell rna-sequencing data |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10327311/ https://www.ncbi.nlm.nih.gov/pubmed/37415108 http://dx.doi.org/10.1186/s12864-023-09447-6 |
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