MatrixQCvis

MatrixQCvis provides computational assessment and exploratory analysis of quantitative omics matrices (feature IDs × samples) to evaluate data quality for transcriptomics, proteomics, and metabolomics studies.

Key Features:

• Per-sample and per-feature quality metrics: Computes and visualizes per-sample and per-feature data-quality metrics for feature×sample matrices.
• Standardized metrics calculation: Implements standardized methods for calculating various data-quality metrics to ensure consistency across analyses.
• SummarizedExperiment integration: Operates on Bioconductor SummarizedExperiment S4 objects to integrate with Bioconductor workflows.

Scientific Applications:

• Omics data quality assessment: Evaluates and identifies anomalies, artifacts, and quality issues in transcriptomics, proteomics, and metabolomics datasets.
• Support for downstream inference: Facilitates early detection of data issues to support reliable biological question–driven inference and downstream analyses.

Methodology:

Processes quantitative omics data as feature IDs × samples matrices to compute and visualize quality metrics, using the R Shiny framework and operating on Bioconductor SummarizedExperiment S4 objects.