MatrixQCvis
This is the released version of MatrixQCvis; for the devel version, see MatrixQCvis.
Shiny-based interactive data-quality exploration for omics data
Bioconductor version: Release (3.20)
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("MatrixQCvis")):
Installation
To install this package, start R (version "4.4") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
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")| Shiny-based interactive data quality exploration of omics data | HTML | R Script |
| Reference Manual | ||
| NEWS | Text |
Details
| biocViews | DimensionReduction, GUI, Metabolomics, Proteomics, QualityControl, ShinyApps, Software, Transcriptomics, Visualization |
| Version | 1.14.0 |
| In Bioconductor since | BioC 3.13 (R-4.1) (3.5 years) |
| License | GPL-3 |
| Depends | DT (>= 0.33), SummarizedExperiment(>= 1.20.0), plotly (>= 4.9.3), shiny (>= 1.6.0) |
| Imports | ComplexHeatmap(>= 2.7.9), dplyr (>= 1.0.5), ExperimentHub(>= 2.6.0), 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), MASS (>= 7.3-58.1), methods (>= 4.1.0), pcaMethods(>= 1.83.0), proDA(>= 1.5.0), rlang (>= 0.4.10), rmarkdown (>= 2.7), Rtsne (>= 0.15), shinydashboard (>= 0.7.1), shinyhelper (>= 0.3.2), shinyjs (>= 2.0.0), stats (>= 4.1.0), sva(>= 3.52.0), tibble (>= 3.1.1), tidyr (>= 1.1.3), umap (>= 0.2.7.0), UpSetR (>= 1.4.0), vsn(>= 3.59.1) |
| System Requirements | |
| URL |
See More
| Suggests | BiocGenerics(>= 0.37.4), BiocStyle(>= 2.19.2), hexbin (>= 1.28.2), httr (>= 1.4.7), jpeg (>= 0.1-10), knitr (>= 1.33), statmod (>= 1.5.0), testthat (>= 3.0.2) |
| Linking To | |
| Enhances | |
| Depends On Me | |
| Imports Me | |
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| Build Report | Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Source Package | MatrixQCvis_1.14.0.tar.gz |
| Windows Binary (x86_64) | MatrixQCvis_1.14.0.zip |
| macOS Binary (x86_64) | MatrixQCvis_1.14.0.tgz |
| macOS Binary (arm64) | MatrixQCvis_1.14.0.tgz |
| Source Repository | git clone https://git.bioconductor.org/packages/MatrixQCvis |
| Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/MatrixQCvis |
| Bioc Package Browser | https://code.bioconductor.org/browse/MatrixQCvis/ |
| Package Short Url | https://bioconductor.org/packages/MatrixQCvis/ |
| Package Downloads Report | Download Stats |