miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data.

Single-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two w...

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Autores principales:	Ariel A Hippen, Matias M Falco, Lukas M Weber, Erdogan Pekcan Erkan, Kaiyang Zhang, Jennifer Anne Doherty, Anna Vähärautio, Casey S Greene, Stephanie C Hicks
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/a27474f972ea4229a2ac4f764625b728
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spelling	oai:doaj.org-article:a27474f972ea4229a2ac4f764625b7282021-12-02T19:58:01ZmiQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data.1553-734X1553-735810.1371/journal.pcbi.1009290https://doaj.org/article/a27474f972ea4229a2ac4f764625b7282021-08-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009290https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Single-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a 'low-quality' cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA (mtDNA) encoded genes and (ii) if a small number of genes are detected. Current best practices use these QC metrics independently with either arbitrary, uniform thresholds (e.g. 5%) or biological context-dependent (e.g. species) thresholds, and fail to jointly model these metrics in a data-driven manner. Current practices are often overly stringent and especially untenable on certain types of tissues, such as archived tumor tissues, or tissues associated with mitochondrial function, such as kidney tissue [1]. We propose a data-driven QC metric (miQC) that jointly models both the proportion of reads mapping to mtDNA genes and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset. We demonstrate how our QC metric easily adapts to different types of single-cell datasets to remove low-quality cells while preserving high-quality cells that can be used for downstream analyses. Our software package is available at https://bioconductor.org/packages/miQC.Ariel A HippenMatias M FalcoLukas M WeberErdogan Pekcan ErkanKaiyang ZhangJennifer Anne DohertyAnna VähärautioCasey S GreeneStephanie C HicksPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 8, p e1009290 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Ariel A Hippen Matias M Falco Lukas M Weber Erdogan Pekcan Erkan Kaiyang Zhang Jennifer Anne Doherty Anna Vähärautio Casey S Greene Stephanie C Hicks miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data.
description	Single-cell RNA-sequencing (scRNA-seq) has made it possible to profile gene expression in tissues at high resolution. An important preprocessing step prior to performing downstream analyses is to identify and remove cells with poor or degraded sample quality using quality control (QC) metrics. Two widely used QC metrics to identify a 'low-quality' cell are (i) if the cell includes a high proportion of reads that map to mitochondrial DNA (mtDNA) encoded genes and (ii) if a small number of genes are detected. Current best practices use these QC metrics independently with either arbitrary, uniform thresholds (e.g. 5%) or biological context-dependent (e.g. species) thresholds, and fail to jointly model these metrics in a data-driven manner. Current practices are often overly stringent and especially untenable on certain types of tissues, such as archived tumor tissues, or tissues associated with mitochondrial function, such as kidney tissue [1]. We propose a data-driven QC metric (miQC) that jointly models both the proportion of reads mapping to mtDNA genes and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset. We demonstrate how our QC metric easily adapts to different types of single-cell datasets to remove low-quality cells while preserving high-quality cells that can be used for downstream analyses. Our software package is available at https://bioconductor.org/packages/miQC.
format	article
author	Ariel A Hippen Matias M Falco Lukas M Weber Erdogan Pekcan Erkan Kaiyang Zhang Jennifer Anne Doherty Anna Vähärautio Casey S Greene Stephanie C Hicks
author_facet	Ariel A Hippen Matias M Falco Lukas M Weber Erdogan Pekcan Erkan Kaiyang Zhang Jennifer Anne Doherty Anna Vähärautio Casey S Greene Stephanie C Hicks
author_sort	Ariel A Hippen
title	miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data.
title_short	miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data.
title_full	miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data.
title_fullStr	miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data.
title_full_unstemmed	miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data.
title_sort	miqc: an adaptive probabilistic framework for quality control of single-cell rna-sequencing data.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/a27474f972ea4229a2ac4f764625b728
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miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data.

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