Fig. 3: Performance of the individual association methods for compositional predictors.
From: A systematic benchmark of integrative strategies for microbiome-metabolome data
To accommodate long running times due to the number of pairs between species and metabolites, we considered scenarios with a number of features half the number of individuals (See supplementary methods) A QQplots of the individual association methods across our three simulation settings. B Power of the individual association methods across our two main scenarios. P values ≤ 0.05 were considered significant. For the CLR-lm method and HALLA, p-values were combined using ACAT in order to provide similar comparisons with the log-contrast regression and MiRKAT (See Methods). For MiRKAT, we reported Type-I error rate and power using the ILR transformed microbiome data and the log transformed metabolites, while for HALLA, we considered the CLR transformed microbiome and the log metabolome. The straight line represents the background ACAT-combined power using Spearman’s correlation on the CLR microbiome and the log metabolome. Powers were averaged over 1000 replicates.
