MatrixQCvis: shiny-based interactive data quality exploration for omics data

MotivationFirst-line data quality assessment and exploratory data analysis are integral parts of any data analysis workflow. In high-throughput quantitative omics experiments (e.g. transcriptomics, proteomics, metabolomics), after initial processing, the data are typically presented as a matrix of numbers (feature IDs x samples). Efficient and standardized data-quality metrics calculation and visualization are key to track the within-experiment quality of these rectangular data types and to guarantee for high-quality data sets and subsequent biological question-driven inference. ResultsWe present MatrixQCvis, which provides interactive visualization of data quality metrics at the per-sample and per-feature level using Rs shiny framework. It provides efficient and standardized ways to analyze data quality of quantitative omics data types that come in a matrix-like format (features IDs x samples). MatrixQCvis builds upon the Bioconductor SummarizedExperiment S4 class and thus facilitates the integration into existing workflows. AvailabilityMatrixQCVis is implemented in R. It is available via Bioconductor and released under the GPL v3.0 license. Contactthomas.naake@embl.de Supplementary informationSupplementary Information is available at bioRxiv online.