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One of the challenging tasks in this analysis is detecting a community\u2010level difference in microbiome data. Inherently, the count data of operational taxonomic units are sparse for most microbiomes, and classical tests for distribution differences under the normality assumption can hardly handle high\u2010dimensional microbiome profiles. We introduce a novel concept of pointwise entropy for the operational taxonomic units of microbiome, enabling permutation\u2010based hypothesis testing at the community levels. Through synthetic data and real\u2010world microbiome profiles related to human milk digestion designed for group comparison, we demonstrate its effectiveness in detecting community\u2010level differences. 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