recipe bioconductor-miqc

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



BSD_3_clause + file LICENSE



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.

package bioconductor-miqc

(downloads) docker_bioconductor-miqc



depends bioconductor-singlecellexperiment:


depends r-base:


depends r-flexmix:

depends r-ggplot2:



You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-miqc

and update with::

   mamba update bioconductor-miqc

To create a new environment, run:

mamba create --name myenvname bioconductor-miqc

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `bioconductor-miqc/tags`_ for valid values for ``<tag>``)

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