miQC

DOI: 10.18129/B9.bioc.miQC    

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

Bioconductor version: Release (3.14)

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 gmail.com>

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

Installation

To install this package, start R (version "4.1") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("miQC")

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

Documentation

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

browseVignettes("miQC")

 

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PDF   Reference Manual
Text   NEWS
Text   LICENSE

Details

biocViews GeneExpression, Preprocessing, QualityControl, Sequencing, SingleCell, Software
Version 1.2.0
In Bioconductor since BioC 3.13 (R-4.1) (0.5 years)
License BSD_3_clause + file LICENSE
Depends R (>= 3.5.0)
Imports SingleCellExperiment, flexmix, ggplot2, splines
LinkingTo
Suggests scRNAseq, scater, biomaRt, BiocStyle, knitr, rmarkdown
SystemRequirements
Enhances
URL https://github.com/greenelab/miQC
BugReports https://github.com/greenelab/miQC/issues
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report  

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package miQC_1.2.0.tar.gz
Windows Binary miQC_1.2.0.zip
macOS 10.13 (High Sierra) miQC_1.2.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/miQC
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/miQC
Package Short Url https://bioconductor.org/packages/miQC/
Package Downloads Report Download Stats
Old Source Packages for BioC 3.14 Source Archive

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