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



Required By:


With an activated Bioconda channel (see set-up-channels), install with:

conda install bioconductor-miqc

and update with:

conda update bioconductor-miqc

or use the docker container:

docker pull<tag>

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

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