Thomas Naake and Wolfgang Huber
First-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 and metabolomics), after initial processing, the data are typically presented as a matrix of numbers (feature IDs × 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 datasets and subsequent biological question-driven inference.
We present MatrixQCvis, which provides interactive visualization of data quality metrics at the per-sample and per-feature level using R’s 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 × samples). MatrixQCvis builds upon the Bioconductor SummarizedExperiment S4 class and thus facilitates the integration into existing workflows.
MatrixQCVis is implemented in R. It is available via Bioconductor and released under the GPL v3.0 license.
Hosna is part of Strasbourg Medical School double degree programme and holds an MSc in bioinformatics from Paris-Saclay University. She joined the Huber group in January 2021 for her master’s thesis and focused on statistical method development for clustering single-cell data. Since September 2021 she is a PhD student in the group and will focus on analysing single-cell multi-omics data to study drug response in blood cancers.
Julia received her PhD in Molecular, Cell and Developmental Biology from the University of California, Santa Cruz for her work on alternative splicing and translational control in humans and primates. She joined the Huber group as data manager/ data scientist in July 2021. As a member of the DECODE project and the GHGA Team, she works on creating and maintaining research data infrastructure.
Tobias is a physician scientist and postdoctoral fellow. In April 2021, he joined a collaborative research project (SIMONA) between the Huber Group at EMBL and the Dietrich Group at the Heidelberg University Hospital. His current research focuses on understanding intratumor heterogeneity within the lymph node niche using multimodal single cell applications.