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OmicsEV: a tool for comprehensive quality evaluation of omics data tables
Bo Wen1,2, Eric J Jaehnig1,2, Bing Zhang1,2
1Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA.
Bioinformatics (Oxford, England)
|October 22, 2022
Summary
OmicsEV is a new R package designed to evaluate the quality of omics data tables. It helps researchers assess data integrity and select optimal processing methods for RNA-Seq and mass spectrometry studies.
Area of Science:
- Genomics
- Bioinformatics
- Computational Biology
Background:
- Omics studies, including RNA-Seq and mass spectrometry, generate large data tables essential for biological discovery.
- Data quality is paramount and influenced by experimental procedures and computational data processing.
- Assessing data quality is critical for reliable interpretation of omics results.
Purpose of the Study:
- To introduce OmicsEV, an R package for comprehensive quality evaluation of omics data tables.
- To provide tools for assessing various aspects of data quality, including depth, normalization, batch effects, and reproducibility.
- To aid researchers in identifying optimal data processing strategies for their omics studies.
Main Methods:
- OmicsEV employs a suite of methods to analyze omics data tables.
- Evaluates key quality metrics: data depth, normalization effectiveness, batch effects, biological signal strength, platform reproducibility, and multi-omics concordance.
- Generates both visual and quantitative assessment reports.
Main Results:
- OmicsEV provides a systematic approach to quality control for omics data.
- Offers detailed insights into potential data issues and processing artifacts.
- Facilitates informed decision-making regarding data processing parameters.
Conclusions:
- OmicsEV enhances the reliability and reproducibility of omics research.
- Empowers researchers to rigorously assess and improve the quality of their omics data.
- Supports the selection of appropriate computational methods for omics data analysis.
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