The objective of CompLiMet is to provide experimental and computational biologists methods for data processing and analyte annotation, statistical and machine learning analyses, and pathway and network investigation primarily for lipidomic and metabolomic data.
A Gaussian naïve Bayes classifier for targeted lipidomics that annotates peak identities according to eight user-specified peak features related to retention time, intensity, and peak shape5.
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A software solution for handling sample imbalance, primarily for metabolomics and lipidomics datasets.
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A user-friendly solution for dataset appropriate imputation of missing data.
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Projection/visualization with statistical assessment of the significance of the separation of sample groups.
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An application for calculation of distance correlation in omics data, measuring linear and non-linear dependencies between variables. SiDCo further provides a novel “signed distance correlation” that includes overall directionality as well as magnitude of the correlation.
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A tool that searches a computationally generated database of glycerophospholipids by m/z with option to draw all possible lipid structures and/or download static visualizations.
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