Understanding When Poisson Log-Normal Models Outperform Penalized Poisson Regression for Microbiome Count Data
Multivariate count models are often justified by their ability to capture latent dependence, but researchers receive little guidance on when this added structure improves on simpler penalized marginal Poisson regression. We study this question using real microbiome data under a unified held-out evaluation framework. For count prediction, we compare PLN and GLMNet(Poisson) on 20 datasets spanning 32 to 18,270 samples and 24 to 257 taxa, using held-out Poisson deviance under leave-one-taxon-out prediction with 3-fold sample cross-validation rather than synthetic or in-sample criteria. For network inference, we compare PLNNetwork and GLMNet(Poisson) neighborhood selection on five publicly av...
| Authors | Daniel Agyapong, Julien Chiquet, Jane Marks et al. |
|---|---|
| Primary category | cs.LG |
| Published | 4 April 2026 |
| Last updated at source | 4 April 2026 |
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