recipe bioconductor-matrixqcvis

Shiny-based interactive data-quality exploration for omics data






Data quality assessment is an integral part of preparatory data analysis to ensure sound biological information retrieval. We present here the MatrixQCvis package, which provides shiny-based interactive visualization of data quality metrics at the per-sample and per-feature level. It is broadly applicable to quantitative omics data types that come in matrix-like format (features x samples). It enables the detection of low-quality samples, drifts, outliers and batch effects in data sets. Visualizations include amongst others bar- and violin plots of the (count/intensity) values, mean vs standard deviation plots, MA plots, empirical cumulative distribution function (ECDF) plots, visualizations of the distances between samples, and multiple types of dimension reduction plots. Furthermore, MatrixQCvis allows for differential expression analysis based on the limma (moderated t-tests) and proDA (Wald tests) packages. MatrixQCvis builds upon the popular Bioconductor SummarizedExperiment S4 class and enables thus the facile integration into existing workflows. The package is especially tailored towards metabolomics and proteomics mass spectrometry data, but also allows to assess the data quality of other data types that can be represented in a SummarizedExperiment object.

package bioconductor-matrixqcvis

(downloads) docker_bioconductor-matrixqcvis



depends bioconductor-complexheatmap:


depends bioconductor-experimenthub:


depends bioconductor-impute:


depends bioconductor-limma:


depends bioconductor-pcamethods:


depends bioconductor-proda:


depends bioconductor-summarizedexperiment:


depends bioconductor-vsn:


depends r-base:


depends r-dplyr:


depends r-ggplot2:


depends r-hmisc:


depends r-htmlwidgets:


depends r-imputelcmd:


depends r-mass:


depends r-plotly:


depends r-rlang:


depends r-rmarkdown:


depends r-rtsne:


depends r-shiny:


depends r-shinydashboard:


depends r-shinyhelper:


depends r-shinyjs:


depends r-tibble:


depends r-tidyr:


depends r-umap:


depends r-upsetr:




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-matrixqcvis

and update with::

   mamba update bioconductor-matrixqcvis

To create a new environment, run:

mamba create --name myenvname bioconductor-matrixqcvis

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-matrixqcvis/tags`_ for valid values for ``<tag>``)

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