The package conducts pathway testing from untargetted metabolomics data. It requires the user to supply feature-level test results, from case-control testing, regression, or other suitable feature-level tests for the study design. Weights are given to metabolic features based on how many metabolites they could potentially match to. The package can combine positive and negative mode results in pathway tests. The package contains two types of statistical testing that considers matching uncertainty - (1) a permutation test that is based on the hypergeometric test and (2) a GSEA type test with weighted features/metabolites.
The package conducts (1) a weighted hypergeometric test using permutations on metabolomics data. The weights are assigned based on how many metabolites each data feature can match to, (2) a GSEA type test based on an estimation of importance of metabolites/features. The importance is evluated by the size of matching for each metabolite/feature and the p-value of features.
The user can tune a parameter to change the penalty for multiple-matched features and choose the type of pathway testing.
Tianwei Yu (firstname.lastname@example.org)
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