CIMA | Chemical structure-Informed Metabolomics Analysis

A strategy to boost the statistical power of hypothesis testing in metabolomics by incorporating quantitative molecular descriptors for each metabolite. CIMA selects potentially informative summary molecular descriptors and outputs chemical structure-informed false discovery rates. The proposed approach focuses on the general metabolomic hypothesis-testing problem, whereas incorporating structure information into other common metabolomic inference practices, such as multivariate predictive model construction and network inference, is warranted for further research.