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SampleQC: robust multivariate, multi-cell type, multi-sample quality control for single-cell data
Quality control (QC) is a critical component of single-cell RNA-seq (scRNA-seq) processing pipelines. Current approaches to QC implicitly assume that datasets are comprised of one cell type, potentially resulting in biased exclusion of rare cell types. We introduce SampleQC, which robustly fits a Ga...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9912498/ https://www.ncbi.nlm.nih.gov/pubmed/36765378 http://dx.doi.org/10.1186/s13059-023-02859-3 |
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author | Macnair, Will Robinson, Mark |
author_facet | Macnair, Will Robinson, Mark |
author_sort | Macnair, Will |
collection | PubMed |
description | Quality control (QC) is a critical component of single-cell RNA-seq (scRNA-seq) processing pipelines. Current approaches to QC implicitly assume that datasets are comprised of one cell type, potentially resulting in biased exclusion of rare cell types. We introduce SampleQC, which robustly fits a Gaussian mixture model across multiple samples, improves sensitivity, and reduces bias compared to current approaches. We show via simulations that SampleQC is less susceptible to exclusion of rarer cell types. We also demonstrate SampleQC on a complex real dataset (867k cells over 172 samples). SampleQC is general, is implemented in R, and could be applied to other data types. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-02859-3. |
format | Online Article Text |
id | pubmed-9912498 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-99124982023-02-11 SampleQC: robust multivariate, multi-cell type, multi-sample quality control for single-cell data Macnair, Will Robinson, Mark Genome Biol Method Quality control (QC) is a critical component of single-cell RNA-seq (scRNA-seq) processing pipelines. Current approaches to QC implicitly assume that datasets are comprised of one cell type, potentially resulting in biased exclusion of rare cell types. We introduce SampleQC, which robustly fits a Gaussian mixture model across multiple samples, improves sensitivity, and reduces bias compared to current approaches. We show via simulations that SampleQC is less susceptible to exclusion of rarer cell types. We also demonstrate SampleQC on a complex real dataset (867k cells over 172 samples). SampleQC is general, is implemented in R, and could be applied to other data types. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13059-023-02859-3. BioMed Central 2023-02-10 /pmc/articles/PMC9912498/ /pubmed/36765378 http://dx.doi.org/10.1186/s13059-023-02859-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 | Method Macnair, Will Robinson, Mark SampleQC: robust multivariate, multi-cell type, multi-sample quality control for single-cell data |
title | SampleQC: robust multivariate, multi-cell type, multi-sample quality control for single-cell data |
title_full | SampleQC: robust multivariate, multi-cell type, multi-sample quality control for single-cell data |
title_fullStr | SampleQC: robust multivariate, multi-cell type, multi-sample quality control for single-cell data |
title_full_unstemmed | SampleQC: robust multivariate, multi-cell type, multi-sample quality control for single-cell data |
title_short | SampleQC: robust multivariate, multi-cell type, multi-sample quality control for single-cell data |
title_sort | sampleqc: robust multivariate, multi-cell type, multi-sample quality control for single-cell data |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9912498/ https://www.ncbi.nlm.nih.gov/pubmed/36765378 http://dx.doi.org/10.1186/s13059-023-02859-3 |
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