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

ABSTRACT:

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 distributed under the null hypothesis, which demands that the postulated model fit the count data adequately. Mis-specification of the distribution of the count data may lead to excess false discoveries. Therefore, model checking is critical to control the FDR at a nominal level in differential abundance analysis. Increasing studies show that the method of randomized quantile residual (RQR) performs well in diagnosing count regression models. However, the performance of RQR in diagnosing

SUBMITTER: Bai W 

PROVIDER: S-EPMC8620156 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature