Year: 2022
Type: article
Abstract: SUMMARY: RNA-Seq and mass spectrometry-based studies generate omics data tables with measurements for tens of thousands of genes across all samples in a study. The success of a study relies on the quality of these data tables, which is determined by both experimental data generation and computational methods used to process raw experimental data into quantitative data tables. We present OmicsEV, an R package for the quality evaluation of omics data tables. For each data table, OmicsEV uses a series of methods to evaluate data depth, data normalization, batch effect, biological signal, platform reproducibility and multi-omics concordance, producing comprehensive visual and quantitative evaluation results that help assess the data quality of individual data tables and facilitate the identification of the optimal data processing method and parameters for the omics study under investigation. AVAILABILITY AND IMPLEMENTATION: The source code and the user manual of OmicsEV are available at https://github.com/bzhanglab/OmicsEV, and the source code is released under the GPL-3 license.
Source: Bioinformatics
Authors: Bo Wen, Eric J Jaehnig, Bing Zhang
Institution: Baylor College of Medicine
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,618
OpenAlex ID: W4307091806