Simulation results for differential abundance analysis under the log-normal, Poisson, and negative-binomial models, respectively.

Simulation results for differential abundance analysis under the log-normal, Poisson, and negative-binomial models, respectively.

<p>Error bars represent the 95% confidence intervals and the dashed vertical line indicates the target FDR level of 0.05.</p>

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Bibliographic Details
Main Author:	Huijuan Zhou (5745020) (author)
Other Authors:	Jun Chen (4238) (author), Xianyang Zhang (2592901) (author)
Published:	2025
Subjects:	Microbiology Genetics Ecology Inorganic Chemistry Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified uses variational inference reconstructing true abundances real microbiome datasets multiple posterior samples microbiome sequencing data microbial biomarker discovery excessive zeros arising differential abundance analysis bimodal abundance distribution assume unimodal abundance computationally efficient solution efficient imputation taxa via scalable expectation require log probabilistic framework method offers maximization algorithm insufficient sampling inherently sparse downstream analyses dirichlet priors broadly applicable biological absence analyzing high accurate imputation
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