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Flexible, probabilistic metrics for quality control of scRNA-seq data

Bioconductor version: Release (3.19)

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 encoded genes (mtDNA) and (ii) if a small number of genes are detected. miQC is data-driven QC metric that jointly models both the proportion of reads mapping to mtDNA and the number of detected genes with mixture models in a probabilistic framework to predict the low-quality cells in a given dataset.

Author: Ariel Hippen [aut, cre], Stephanie Hicks [aut]

Maintainer: Ariel Hippen <ariel.hippen at>

Citation (from within R, enter citation("miQC")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

miQC HTML R Script
Reference Manual PDF


biocViews GeneExpression, Preprocessing, QualityControl, Sequencing, SingleCell, Software
Version 1.12.0
In Bioconductor since BioC 3.13 (R-4.1) (3 years)
License BSD_3_clause + file LICENSE
Depends R (>= 3.5.0)
Imports SingleCellExperiment, flexmix, ggplot2, splines
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Suggests scRNAseq, scater, BiocStyle, knitr, rmarkdown
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Source Package miQC_1.12.0.tar.gz
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macOS Binary (x86_64) miQC_1.12.0.tgz
macOS Binary (arm64) miQC_1.12.0.tgz
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