Fig. 3: Overlap of significant features across tools and tool clustering.
From: Microbiome differential abundance methods produce different results across 38 datasets
a, b The number of tools that called each feature significant, stratified by features called by each individual tool for the a unfiltered and b 10% prevalence-filtered data. Results are shown as a percentage of all ASVs identified by each tool. The total number of significant features identified by each tool is indicated by the bar colors. For example, based on the unfiltered data these bars indicate that almost 40% of significant ASVs identified by ALDEx2 were shared across all other tools, while ALDEx2 did not identify any significant ASVs shared by fewer than eight tools. Note that when interpreting these results that they are dependent on which methods were included, and whether they are represented multiple times. For instance, two different workflows for running MaAslin2 are included, which produced similar outputs. c, d Plots are displayed for the first two principal coordinates (PCs) for both c non-prevalence-filtered and d 10% prevalence-filtered data. These plots are based on the mean inter-tool Jaccard distance across the 38 main datasets that we analyzed, computed by averaging over the inter-tool distance matrices for all individual datasets to weight each dataset equally. Abbreviations: TMM, trimmed mean of M-values; TMMwsp, trimmed mean of M-values with singleton pairing; rare, rarefied; CLR, center-log-ratio. Source data are provided as a Source Data file.
