Fig. 3

mbImpute empowers DESeq2-phyloseq in identifying DA taxa. a The barplots show classification accuracy, measured by 5-fold cross-validated precision-recall area under the curve (PR-AUC), by the random forest algorithm for predicting samples’ disease conditions in two T2D datasets [18, 19] and four CRC datasets [14–17]. The features are the DA taxa detected by DESeq2-phyloseq (light color) or mbImpute-empowered DESeq2-phyloseq (dark color; labeled as mbImpute + DESeq2-phyloseq). b The histograms show the distributions of three taxa in control and T2D samples in [18] before and after mbImpute is applied. The three taxa, Ruminococcus sp_5_1_39BFAA, Ruminococcus callidus, and Ruminococcus albus, are identified as enriched in T2D samples only after imputation. c The histograms show the distributions of three taxa in control and CRC samples in [17] before and after mbImpute is applied. The three taxa, Ruminococcus gnavus, Lachnospiraceae bacterium_2_1_58FAA, and Granulicatella adiacens, are identified as enriched in CRC samples only after imputation. In b and c, adjusted p values calculated by DESeq2-phyloseq are listed