OmicsEV

OmicsEV assesses the quality and reliability of omics data tables for RNA-Seq and mass spectrometry-based studies by evaluating data completeness, normalization, batch effects, biological signal, platform reproducibility, and multi-omics concordance.

Key Features:

• Data Depth: Evaluates whether datasets contain sufficient measurements across samples, including high-dimensional tables with tens of thousands of genes.
• Data Normalization: Assesses the effectiveness of normalization procedures in removing technical variability.
• Batch Effect: Identifies and quantifies batch effects that could bias downstream analyses.
• Biological Signal: Assesses the presence and strength of genuine biological signal within the data.
• Platform Reproducibility: Checks consistency across experimental platforms or runs.
• Multi-Omics Concordance: Evaluates agreement between omics layers such as transcriptomics and proteomics.
• Implementation: Provided as an R package for computational evaluation of omics data tables.
• Output Types: Produces both visual and quantitative evaluation results.

Scientific Applications:

• RNA-Seq quality assessment: Applied to evaluate data tables generated from RNA-Seq experiments.
• Mass spectrometry/proteomics quality assessment: Applied to evaluate data tables from mass spectrometry-based proteomics studies.
• Multi-omics concordance evaluation: Used to compare and quantify agreement between transcriptomics and proteomics datasets.
• Selection of processing methods: Guides choice of data processing methods and parameter settings based on quantitative and visual assessments.
• Platform reproducibility assessment: Used to detect inconsistencies between experimental platforms or runs.

Methodology:

Implemented as an R package that systematically assesses data depth, normalization, batch effects, biological signal, platform reproducibility, and multi-omics concordance and returns visual and quantitative evaluation results.