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

Thomas Naake1, Wolfgang Huber1

  • 1Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg 69117, Germany.

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|November 17, 2021
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Summary
This summary is machine-generated.

MatrixQCvis offers interactive visualization for assessing data quality in omics experiments. This R-based tool ensures high-quality quantitative omics datasets through efficient, standardized metrics and visualizations.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Science

Background:

  • Data quality assessment is crucial for high-throughput omics studies (e.g., transcriptomics, proteomics, metabolomics).
  • Quantitative omics data are often matrix-formatted (features × samples), requiring robust quality control.
  • Standardized methods for data quality metrics are essential for reliable biological inference.

Purpose of the Study:

  • To introduce MatrixQCvis, an R package for interactive visualization of data quality metrics.
  • To provide efficient and standardized tools for analyzing quantitative omics data quality.
  • To facilitate integration into existing omics data analysis workflows.

Main Methods:

  • Utilizes R's Shiny framework for interactive, per-sample, and per-feature visualizations.
  • Leverages the Bioconductor SummarizedExperiment S4 class for data structure.
  • Implements standardized data quality metrics calculation and visualization.

Main Results:

  • MatrixQCvis enables interactive exploration of data quality metrics.
  • Provides efficient analysis of matrix-formatted quantitative omics data.
  • Facilitates robust quality assessment at both sample and feature levels.

Conclusions:

  • MatrixQCvis enhances data quality assessment in omics research.
  • The tool ensures high-quality datasets for reliable biological discovery.
  • It offers a standardized and integrated approach to omics data quality control.