Recursive feature random forest
To determine which metabolites were most predictive of response to the probiotic supplement (responder or non-responder), a recursive feature random forest (caret, rfeControl, number = 100, leave one out cross validation) was used. Data input was the participant-specific differences from end of intervention (week 10) to baseline (week −3). If a participant did not have both the baseline and end of intervention time point for a given experimental platform they were removed from analysis. All metabolite difference values were centered and scaled. The recursive feature random forest models returned the minimum feature set needed for highest accuracy.