ArticleGenome biology2022
Biology-inspired data-driven quality control for scientific discovery in single-cell transcriptomics.
Article in Genome biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed, 70 citations in OpenAlex.
- 4-1BBCell reports. Medicine · 2025Trial
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- Machine learning reveals X chromosome transcriptional signatures that classify systemic lupus erythematosus in females.BMC rheumatology · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Nanoparticle induction of antigen-presenting monocyte-derived dendritic cells relieves immunosuppression and inhibits metastasis.Cell biomaterials · 2026Article
- MitoChontrol: Adaptive mitochondrial filtering for robust single-cell RNA sequencing quality control.bioRxiv : the preprint server for biology · 2026Article
- Single-Cell Multi-Omics Reveal Gene Regulatory Mechanisms Underlying Cardiac Embryonic Development.Genes · 2026Review
- Integrative single-cell RNA-seq and ATAC-seq profiling identifies NTN5Zoological research · 2026Article
- Spatial Transcriptomics of TMJ Reveals a Remodeling Fibroblast-Immune Microenvironment Driving Arthritis Pain.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A single-dose of PDGFB circular RNA enables sustained growth factor expression to accelerate diabetic wound healing.Journal of nanobiotechnology · 2026Article
- Deciphering the Tumor Microenvironment Composition Using Bulk Transcriptomics: A Guide to Recent Advances and Open Challenges.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Surface Marker Identification to Capture Live Circulating Tumor Cells in Metastatic Triple-Negative Breast Cancer.Cancer research communications · 2026Article
- Tumor prognostic risk stratification based on pseudo-time analysis of single-cell sequencing for patients with lung adenocarcinoma.Journal of thoracic disease · 2025Article
- Prostate cancer cells converge to an inflammatory-like state upon metastatic dissemination.Nature communications · 2025Article
- Single-Cell Transcriptomic Profiling of GL261 Glioblastoma Cells Reveals Gene Expression Signatures Underlying Tumorigenicity.Cellular and molecular neurobiology · 2025Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundQuality 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.
resultsWe 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.
conclusionsddqc 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.