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High-throughput sequencing technology allows us to test the compositionaldifference of bacteria in different populations. One important feature of humanmicrobiome data is that it often includes a large number of zeros. Such datacan be treated as being generated from a two-part model that includes a zeropoint-mass. Motivated by analysis of such non-negative data with excessivezeros, we introduce several truncated rank-based two-group and multi-grouptests for such data, including a truncated rank-based Wilcoxon rank-sum testfor two-group comparison and two truncated Kruskal-Wallis tests for multi-groupcomparison. We show both analytically through asymptotic relative efficiencyanalysis and by simulations that the proposed tests have higher power than thestandard rank-based tests, especially when the proportion of zeros in the datais high. The tests can also be applied to repeated measurements ofcompositional data via simple within-subject permutations. In a simplebefore-and-after treatment experiment, the within-subject permutation issimilar to the paired rank test. However, the proposed tests handle theexcessive zeros, which leads to a better power. We apply the tests to theanalysis of a gut microbiome data set to compare the microbiome compositions ofhealthy and pediatric Crohn's disease patients and to assess the treatmenteffects on microbiome compositions. We identify several bacterial genera thatare missed by the standard rank-based tests.

Truncated Rank-Based Tests for Two-Part Models with Excessive Zeros and Applications to Microbiome Data