MatrixQCvis
Shiny-based interactive data-quality exploration for omics data
Bioconductor version: 3.24 · Package version: 1.21.1
Other Bioconductor versions
devel is the development version; release is the current stable one.
3.24 (devel), 3.23 (release)
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.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("MatrixQCvis")Details
| Maintainer | Thomas Naake <thomasnaake@googlemail.com> |
| Author | Thomas Naake [aut, cre] (ORCID: <https://orcid.org/0000-0001-7917-5580>), Wolfgang Huber [aut] (ORCID: <https://orcid.org/0000-0002-0474-2218>) |
| License | GPL-3 |
| Source branch | devel |
| Build report | Bioconductor build system, r-universe |
| biocViews | DimensionReduction, GUI, Metabolomics, Proteomics, QualityControl, ShinyApps, Software, Transcriptomics, Visualization |
| Package Short Url | https://bioconductor.org/packages/MatrixQCvis/ |
Citation
From within R, enter citation("MatrixQCvis"):
Thomas Naake, Wolfgang Huber. MatrixQCvis: Shiny-based interactive data-quality exploration for omics data. doi:10.18129/B9.bioc.MatrixQCvis, R package version 1.21.1, https://bioconductor.org/packages/MatrixQCvis.
Generated from the package metadata; it may differ from the package's own citation.
Documentation
Download
Follow the installation instructions to use this package in your R session.
| Source package | MatrixQCvis_1.21.1.tar.gz |
| Windows binary (x86_64) | MatrixQCvis_1.21.1.zip |
| macOS binary (arm64) | MatrixQCvis_1.21.1.tgz |
| macOS binary (x86_64) | MatrixQCvis_1.21.1.tgz |
Dependencies
Depends: R (>= 4.1.0), 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)
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)