omicsMIC: a comprehensive benchmarking platform for robust comparison of imputation methods in mass spectrometry-based omics data.
Weiqiang LinJiadong JiKuan-Jui SuChuan QiuQing TianLan-Juan ZhaoZhe LuoChong WuHui ShenHong-Wen DengPublished in: NAR genomics and bioinformatics (2024)
Mass spectrometry is a powerful and widely used tool for generating proteomics, lipidomics and metabolomics profiles, which is pivotal for elucidating biological processes and identifying biomarkers. However, missing values in mass spectrometry-based omics data may pose a critical challenge for the comprehensive identification of biomarkers and elucidation of the biological processes underlying human complex disorders. To alleviate this issue, various imputation methods for mass spectrometry-based omics data have been developed. However, a comprehensive comparison of these imputation methods is still lacking, and researchers are frequently confronted with a multitude of options without a clear rationale for method selection. To address this pressing need, we developed omicsMIC (mass spectrometry-based omics with Missing values Imputation methods Comparison platform), an interactive platform that provides researchers with a versatile framework to evaluate the performance of 28 diverse imputation methods. omicsMIC offers a nuanced perspective, acknowledging the inherent heterogeneity in biological data and the unique attributes of each dataset. Our platform empowers researchers to make data-driven decisions in imputation method selection based on real-time visualizations of the outcomes associated with different imputation strategies. The comprehensive benchmarking and versatility of omicsMIC make it a valuable tool for the scientific community engaged in mass spectrometry-based omics research. omicsMIC is freely available at https://github.com/WQLin8/omicsMIC.
Keyphrases
- mass spectrometry
- liquid chromatography
- single cell
- gas chromatography
- capillary electrophoresis
- high performance liquid chromatography
- high resolution
- electronic health record
- high throughput
- big data
- endothelial cells
- healthcare
- tandem mass spectrometry
- clinical trial
- type diabetes
- mental health
- adipose tissue
- simultaneous determination
- glycemic control