ArticlePLoS computational biology2021
miQC: An adaptive probabilistic framework for quality control of single-cell RNA-sequencing data.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 71 papers.
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Who cites it
71 citing papers in PubMed, 98 citations in OpenAlex.
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- Multi-omics analysis reveals the attenuation of the interferon pathway as a driver of chemo-refractory ovarian cancer.Cell reports. Medicine · 2025Trial
- Personalized neoantigen-targeting cancer vaccines in ovarian cancer: a phase 1 trial.Nature medicine · 2026Article
- Dual-Target Cardiac Regeneration: ControllableCirculation · 2026Article
- Characterizing intra- and intertumor heterogeneity in ovarian high-grade serous carcinoma subtypes using single-cell and spatial transcriptomics.Genome research · 2026Article
- Epithelial interferon-stimulated gene programs modulate chemotherapy sensitivity in colorectal cancer via EPSTI1.Oncogene · 2026Article
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- Priming of CD8Nature neuroscience · 2026Article
- The Single-Cell Pediatric Cancer Atlas: Data portal and open-source tools for single-cell transcriptomics of pediatric tumors.Cell genomics · 2026Article
- ScQCenrich enables multi-metric quality control for single-cell RNA sequencing.Communications biology · 2026Article
- Single-cell transcriptomic profiling of patient-derived pancreatic ductal adenocarcinoma primary cell cultures.Scientific data · 2026Article
- Ex Vivo Expansion of Melanoma Tumor-Infiltrating Lymphocytes Leads to a Dominant Exhausted T-cell Population with a Lack of Memory Markers.Cancer immunology research · 2026Article
- Unveiling alternate pathways for SARS-CoV-2 infection via extracellular vesicle-mediated transfer of ACE2 and TMPRSS2.Nature communications · 2026Article
- MitoChontrol: Adaptive mitochondrial filtering for robust single-cell RNA sequencing quality control.bioRxiv : the preprint server for biology · 2026Article
- Ex Vivo Immuno-Oncology Platform Reveals Spatial T-cell Infiltration Patterns Linked to ATR Inhibition Responses in High-Grade Serous Ovarian Cancer.Cancer immunology research · 2026Article
- The transcription factor HHEX maintains glucocorticoid levels and protects adrenals from androgen-induced lipid depletion.Nature communications · 2026Article
- Sex-associated transcriptional changes to synovial macrophages in the aging joint.Frontiers in immunology · 2026Article
- An integrated bulk and single-cell transcriptomic analysis reveals stemness-driven immune regulation and therapeutic vulnerability in colorectal cancer.Journal of Cancer · 2026Article
- Surface Marker Identification to Capture Live Circulating Tumor Cells in Metastatic Triple-Negative Breast Cancer.Cancer research communications · 2026Article
- Spatial Transcriptomics RevealsbioRxiv : the preprint server for biology · 2025Article
11 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
Abstract
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.
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Registered trials
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.