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Identification of features is a critical task in microbiome studies that iscomplicated by the fact that microbial data are high dimensional andheterogeneous. Masked by the complexity of the data, the problem of separatingsignals from noise becomes challenging and troublesome. For instance, whenperforming differential abundance tests, multiple testing adjustments tend tobe overconservative, as the probability of a type I error (false positive)increases dramatically with the large numbers of hypotheses. Moreover, thegrouping effect of interest can be obscured by heterogeneity. These factors canincorrectly lead to the conclusion that there are no differences in themicrobiome compositions. We translate and represent the problem of identifyingdifferential features as a dynamic layout of separating the signal from itsrandom background. We propose progressive permutation as a method to achievethis process and show converging patterns. More specifically, we progressivelypermute the grouping factor labels of the microbiome samples and performmultiple differential abundance tests in each scenario. We then compare thesignal strength of the top features from the original data with theirperformance in permutations, and observe an apparent decreasing trend if thesetop features are true positives identified from the data. We have developedthis into a user-friendly RShiny tool and R package, which consist of functionsthat can convey the overall association between the microbiome and the groupingfactor, rank the robustness of the discovered microbes, and list thediscoveries, their effect sizes, and individual abundances.

ProgPermute: Progressive permutation for a dynamic representation of the robustness of microbiome discoveries