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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.