Package: miQC
Type: Package
Title: Flexible, probabilistic metrics for quality control of scRNA-seq
        data
Version: 1.6.0
Authors@R: 
    c(person("Ariel", "Hippen", role = c("aut", "cre"),
    email = "ariel.hippen@gmail.com"),
    person("Stephanie", "Hicks", role = c("aut"),
    email = "shicks19@jhu.edu"))
Description: 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.
URL: https://github.com/greenelab/miQC
BugReports: https://github.com/greenelab/miQC/issues
License: BSD_3_clause + file LICENSE
Imports: SingleCellExperiment, flexmix, ggplot2, splines
Suggests: scRNAseq, scater, BiocStyle, knitr, rmarkdown
biocViews: SingleCell, QualityControl, GeneExpression, Preprocessing,
        Sequencing
VignetteBuilder: knitr
Encoding: UTF-8
RoxygenNote: 7.2.0
LazyData: TRUE
git_url: https://git.bioconductor.org/packages/miQC
git_branch: RELEASE_3_16
git_last_commit: de1de6e
git_last_commit_date: 2022-11-01
Date/Publication: 2022-11-01
NeedsCompilation: no
Packaged: 2022-11-01 22:04:07 UTC; biocbuild
Author: Ariel Hippen [aut, cre],
  Stephanie Hicks [aut]
Maintainer: Ariel Hippen <ariel.hippen@gmail.com>
Depends: R (>= 3.5.0)
Built: R 4.2.2; ; 2022-11-06 10:35:36 UTC; unix
