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:

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:

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

For the development version enter:

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.

library("MatrixQCvis")

## initialize the application with passing a SummarizedExperiment object
shinyQC(se)

## initialize the application without passing a SummarizedExperiment object
shinyQC()