Randomized quantile residuals for diagnosing zero-inflated generalized linear mixed models with applications to microbiome count data

Background: For differential abundance analysis, zero-inflated generalized linear models, typically zero-inflated NB models, have been increasingly used to model microbiome and other sequencing count data. A common assumption in estimating the false discovery rate is that the p values are uniformly...

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Bibliographic Details
Main Authors:	Bai, W. (Author), Dong, M. (Author), Feng, C. (Author), Li, L. (Author), Xu, W. (Author)
Format:	Article
Language:	English
Published:	BioMed Central Ltd 2021
Subjects:	Abundance analysis article computer simulation Computer Simulation controlled study Count datum False discovery rate Generalized linear mixed models Generalized linear model human Linear Models methodology microbiome Microbiome Microbiota microflora Model checking Models, Statistical nonhuman Performance Poisson distribution Poisson Distribution P-values randomized controlled trial Regression analysis Research Design simulation Simulation studies statistical model Zero-inflated
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Randomized quantile residuals for diagnosing zero-inflated generalized linear mixed models with applications to microbiome count data

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