Biology-inspired data-driven quality control for scientific discovery in single-cell transcriptomics.

Transcriptome Gene Expression Profiling Quality Control Single-Cell Analysis
Adaptive QC Biological variation Data-driven Exploratory data analysis (EDA) Quality control (QC) Single cell scRNA-seq

Journal

Genome biology
ISSN: 1474-760X
Titre abrégé: Genome Biol
Pays: England
ID NLM: 100960660

Informations de publication

Date de publication:
27 12 2022
Historique:
received: 18 08 2021
accepted: 23 11 2022
entrez: 27 12 2022
pubmed: 28 12 2022
medline: 30 12 2022
Statut: epublish

Résumé

Quality control (QC) of cells, a critical first step in single-cell RNA sequencing data analysis, has largely relied on arbitrarily fixed data-agnostic thresholds applied to QC metrics such as gene complexity and fraction of reads mapping to mitochondrial genes. The few existing data-driven approaches perform QC at the level of samples or studies without accounting for biological variation. We first demonstrate that QC metrics vary with both tissue and cell types across technologies, study conditions, and species. We then propose data-driven QC (ddqc), an unsupervised adaptive QC framework to perform flexible and data-driven QC at the level of cell types while retaining critical biological insights and improved power for downstream analysis. ddqc applies an adaptive threshold based on the median absolute deviation on four QC metrics (gene and UMI complexity, fraction of reads mapping to mitochondrial and ribosomal genes). ddqc retains over a third more cells when compared to conventional data-agnostic QC filters. Finally, we show that ddqc recovers biologically meaningful trends in gradation of gene complexity among cell types that can help answer questions of biological interest such as which cell types express the least and most number of transcripts overall, and ribosomal transcripts specifically. ddqc retains cell types such as metabolically active parenchymal cells and specialized cells such as neutrophils which are often lost by conventional QC. Taken together, our work proposes a revised paradigm to quality filtering best practices-iterative QC, providing a data-driven QC framework compatible with observed biological diversity.

Sections du résumé

BACKGROUND
Quality control (QC) of cells, a critical first step in single-cell RNA sequencing data analysis, has largely relied on arbitrarily fixed data-agnostic thresholds applied to QC metrics such as gene complexity and fraction of reads mapping to mitochondrial genes. The few existing data-driven approaches perform QC at the level of samples or studies without accounting for biological variation.
RESULTS
We first demonstrate that QC metrics vary with both tissue and cell types across technologies, study conditions, and species. We then propose data-driven QC (ddqc), an unsupervised adaptive QC framework to perform flexible and data-driven QC at the level of cell types while retaining critical biological insights and improved power for downstream analysis. ddqc applies an adaptive threshold based on the median absolute deviation on four QC metrics (gene and UMI complexity, fraction of reads mapping to mitochondrial and ribosomal genes). ddqc retains over a third more cells when compared to conventional data-agnostic QC filters. Finally, we show that ddqc recovers biologically meaningful trends in gradation of gene complexity among cell types that can help answer questions of biological interest such as which cell types express the least and most number of transcripts overall, and ribosomal transcripts specifically.
CONCLUSIONS
ddqc retains cell types such as metabolically active parenchymal cells and specialized cells such as neutrophils which are often lost by conventional QC. Taken together, our work proposes a revised paradigm to quality filtering best practices-iterative QC, providing a data-driven QC framework compatible with observed biological diversity.

Identifiants

DOI: 10.1186/s13059-022-02820-w PMID: 36575523 PMC: PMC9793662
pubmed: 36575523
doi: 10.1186/s13059-022-02820-w
pii: 10.1186/s13059-022-02820-w
pmc: PMC9793662
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

267

Informations de copyright

© 2022. The Author(s).

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Auteurs

Ayshwarya Subramanian (A)

Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA. subraman@broadinstitute.org.
Brigham and Womens's Hospital, Harvard Medical School, Boston, USA. subraman@broadinstitute.org.
ORCID: 0000-0002-4134-7612

Mikhail Alperovich (M)

Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
MIT PRIMES, Massachusetts Institute of Technology, Cambridge, MA, USA.
Lexington High School, Lexington, MA, USA.
Present Address: Wake Technical Community College, Raleigh, USA.

Yiming Yang (Y)

Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Center for Immunology and Inflammatory Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA.
Present Address: Department of Cellular and Tissue Genomics, Genentech Inc., South San Francisco, CA, USA.

Bo Li (B)

Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Center for Immunology and Inflammatory Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA.
Present Address: Department of Cellular and Tissue Genomics, Genentech Inc., South San Francisco, CA, USA.
Department of Medicine, Harvard Medical School, Boston, MA, 02115, USA.

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Classifications MeSH

Recherchemedicale.com › Phénomènes génétiques › Structures génétiques › Transcriptome › Biology-inspired data-driven quality control...
Recherchemedicale.com › Techniques d'investigation › Techniques génétiques › Analyse de profil d'expression de gènes › Biology-inspired data-driven quality control...
Recherchemedicale.com › Technologie, industrie et agriculture › Technologie › Contrôle de qualité › Biology-inspired data-driven quality control...
Recherchemedicale.com › Techniques d'investigation › Techniques cytologiques › Analyse sur cellule unique › Biology-inspired data-driven quality control...