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# _MatrixQCvis_ - Interactive exploration of data quality ## Overview The _MatrixQCvis_ package 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 To install _MatrixQCvis_ from GitHub, install the package via `devtools`: ```r if (!requireNamespace("devtools", quietly = TRUE)) install.packages("devtools") library(devtools) install_github("tnaake/MatrixQCvis") ``` Alternatively, the _MatrixQCvis_ package can also be installed via the Bioconductor project. For the [release]( version enter: ```r if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("MatrixQCvis") ``` For the [development]( version enter: ```r if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") # the following initializes usage of Bioc devel BiocManager::install(version = "devel") BiocManager::install("MatrixQCvis") ``` The version at the Bioconductor development branch and the code in the Github repository are places where we publicly disclose our development process and users are supposed to refer to the Bioconductor releae version as the authoritative version for use. ## Quick start _MatrixQCvis_ is based on the _SummarizedExperiment_ class. The shiny application can be started with passing a _SummarizedExperiment_ object (in the following denoted as _se_) or without such an object. The second function call will load an interface to load a _SummarizedExperiment_ object stored in a RDS file. ```r library("MatrixQCvis") ## initialize the application with passing a SummarizedExperiment object shinyQC(se) ## initialize the application without passing a SummarizedExperiment object shinyQC() ```