  Identification of features is a critical task in microbiome studies that is
complicated by the fact that microbial data are high dimensional and
heterogeneous. Masked by the complexity of the data, the problem of separating
signals from noise becomes challenging and troublesome. For instance, when
performing differential abundance tests, multiple testing adjustments tend to
be overconservative, as the probability of a type I error (false positive)
increases dramatically with the large numbers of hypotheses. Moreover, the
grouping effect of interest can be obscured by heterogeneity. These factors can
incorrectly lead to the conclusion that there are no differences in the
microbiome compositions. We translate and represent the problem of identifying
differential features as a dynamic layout of separating the signal from its
random background. We propose progressive permutation as a method to achieve
this process and show converging patterns. More specifically, we progressively
permute the grouping factor labels of the microbiome samples and perform
multiple differential abundance tests in each scenario. We then compare the
signal strength of the top features from the original data with their
performance in permutations, and observe an apparent decreasing trend if these
top features are true positives identified from the data. We have developed
this into a user-friendly RShiny tool and R package, which consist of functions
that can convey the overall association between the microbiome and the grouping
factor, rank the robustness of the discovered microbes, and list the
discoveries, their effect sizes, and individual abundances.
