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omicsMIC: a Comprehensive Benchmarking Platform for Robust Comparison of Imputation Methods in Mass
Weiqiang Lin1, Jiadong Ji2, Kuan-Jui Su1
1Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA.
Missing values in omics data hinder biomarker discovery. The omicsMIC platform evaluates 28 imputation methods, aiding researchers in selecting the best strategy for mass spectrometry-based omics data analysis.
Area of Science:
- Biomedical data analysis
- Computational biology
- Mass spectrometry-based omics
Background:
- Mass spectrometry is crucial for proteomics, lipidomics, and metabolomics.
- Missing values in omics data present a significant challenge for biological process elucidation and biomarker identification.
- Existing imputation methods lack comprehensive comparative analysis, complicating researcher choices.
Conclusions:
- omicsMIC serves as a valuable tool for the scientific community in mass spectrometry-based omics research.
- Facilitates robust data analysis by addressing missing values.
- Enhances the comprehensive identification of biomarkers and understanding of complex biological processes.
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