We introduce a novel framework for di↵erential abundance analysis in sparse ultrahigh-dimensional marker gene microbiome data. Our methodology relies on a network-based normalization technique and a two stage zero-inflated mixture count regression model that can take into consideration under-sampling and over-dispersion.
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