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.
Mass spectrometry omics data often contain missing values, hindering biomarker discovery. A new platform, omicsMIC, compares 28 imputation methods to aid researchers in selecting the best strategy for their data.
Area of Science:
- Biomedical data analysis
- Computational biology
- Mass spectrometry-based omics
Background:
- Mass spectrometry is crucial for proteomics, lipidomics, and metabolomics, but missing values complicate biomarker identification and biological process elucidation.
- Existing imputation methods for omics data lack comprehensive comparison, leaving researchers without clear guidance for selection.
Purpose of the Study:
- To address the challenge of missing values in mass spectrometry-based omics data.
- To provide a platform for comparing various imputation methods to aid researchers in selecting the most appropriate strategy.
Main Methods:
- Development of omicsMIC, an interactive platform for evaluating 28 different imputation methods.
- Benchmarking imputation methods using real-time visualizations of outcomes.
- Facilitating data-driven decisions in imputation method selection.
Main Results:
- omicsMIC offers a versatile framework for assessing imputation method performance.
- The platform acknowledges data heterogeneity and dataset-specific attributes.
- Empowers researchers to choose imputation strategies based on visualized results.
Conclusions:
- omicsMIC provides a valuable tool for the scientific community working with mass spectrometry-based omics data.
- Facilitates informed selection of imputation methods, improving the reliability of omics data analysis.
- Enhances the comprehensive identification of biomarkers and elucidation of biological processes.
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