Fig. 1: Variation in the proportion of significant features depending on the differential abundance method and dataset. | Nature Communications

Fig. 1: Variation in the proportion of significant features depending on the differential abundance method and dataset.

From: Microbiome differential abundance methods produce different results across 38 datasets

Fig. 1: Variation in the proportion of significant features depending on the differential abundance method and dataset.The alternative text for this image may have been generated using AI.

Heatmaps indicate the numbers of significant amplicon sequence variants (ASVs) identified in each dataset by the corresponding tool based on a unfiltered data and b 10% prevalence-filtered data. Cells are colored based on the standardized (scaled and mean centered) percentage of significant ASVs for each dataset. Additional colored cells in the left-most six columns indicate the dataset characteristics we hypothesized could be driving variation in these results (darker colors indicate higher values). Datasets were hierarchically clustered based on Euclidean distances using the complete method. Abbreviations: prev., previous; 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.

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