MatrixQCvis

DOI: 10.18129/B9.bioc.MatrixQCvis    

This is the development version of MatrixQCvis; for the stable release version, see MatrixQCvis.

Shiny-based interactive data-quality exploration for omics data

Bioconductor version: Development (3.15)

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.

Author: Thomas Naake [aut, cre], Wolfgang Huber [aut]

Maintainer: Thomas Naake <thomasnaake at googlemail.com>

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

Installation

To install this package, start R (version "4.2") and enter:

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

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

BiocManager::install("MatrixQCvis")

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("MatrixQCvis")

 

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Details

biocViews DimensionReduction, GUI, Metabolomics, Proteomics, Software, Visualization
Version 1.3.1
In Bioconductor since BioC 3.13 (R-4.1) (0.5 years)
License GPL (>= 3)
Depends SummarizedExperiment(>= 1.20.0), plotly (>= 4.9.3), shiny (>= 1.6.0)
Imports ComplexHeatmap(>= 2.7.9), dplyr (>= 1.0.5), ggplot2 (>= 3.3.3), grDevices (>= 4.1.0), Hmisc (>= 4.5-0), htmlwidgets (>= 1.5.3), impute(>= 1.65.0), imputeLCMD (>= 2.0), limma(>= 3.47.12), methods (>= 4.1.0), openxlsx (>= 4.2.3), pcaMethods(>= 1.83.0), proDA(>= 1.5.0), rlang (>= 0.4.10), rmarkdown (>= 2.7), Rtsne (>= 0.15), S4Vectors(>= 0.29.15), shinydashboard (>= 0.7.1), shinyhelper (>= 0.3.2), shinyjs (>= 2.0.0), stats (>= 4.1.0), tibble (>= 3.1.1), tidyr (>= 1.1.3), umap (>= 0.2.7.0), UpSetR (>= 1.4.0), vegan (>= 2.5-7), vsn(>= 3.59.1)
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Suggests BiocGenerics(>= 0.37.4), BiocStyle(>= 2.19.2), hexbin (>= 1.28.2), knitr (>= 1.33), testthat (>= 3.0.2)
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Build Report  

Package Archives

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

Source Package MatrixQCvis_1.3.1.tar.gz
Windows Binary
macOS 10.13 (High Sierra) MatrixQCvis_1.3.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/MatrixQCvis
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/MatrixQCvis
Package Short Url https://bioconductor.org/packages/MatrixQCvis/
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