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Understanding When Poisson Log-Normal Models Outperform Penalized Poisson Regression for Microbiome Count Data

arXiv:2604.03853v1 Announce Type: new Abstract: 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 available datasets with experimentally validated microbial interaction truth. PLN outperforms GLMNet(Poisson) on most count-prediction datasets, with gains up to 38 percent. The primary predictor of the winner is the sample-to-taxon ratio, with mean absolu

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Daniel Agyapong, Julien Chiquet, Jane Marks, Toby Dylan Hocking · · 1 min read · 11 views
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arXiv:2604.03853v1 Announce Type: new Abstract: 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 available datasets with experimentally validated microbial interaction truth. PLN outperforms GLMNet(Poisson) on most count-prediction datasets, with gains up to 38 percent. The primary predictor of the winner is the sample-to-taxon ratio, with mean absolute correlation as the strongest secondary signal and overdispersion as an additional predictor. PLNNetwork performs best on broad undirected interaction benchmarks, whereas GLMNet(Poisson) is better aligned with local or directional effects. Taken together, these results provide guidance for choosing between latent multivariate count models and penalized Poisson regression in biological count prediction and interaction recovery.

Executive Summary

This study provides a comprehensive evaluation of the performance of Poisson log-normal (PLN) models compared to penalized Poisson regression (GLMNet) in predicting microbial count data and recovering microbial interaction networks. The results indicate that PLN models outperform GLMNet on most count-prediction datasets, particularly when the sample-to-taxon ratio is high. The study also suggests that PLNNetwork is better suited for recovering broad undirected interactions, while GLMNet(Poisson) is more effective for local or directional effects. These findings provide valuable guidance for researchers in choosing between these models for biological count prediction and interaction recovery.

Key Points

  • ▸ Poisson log-normal (PLN) models outperform penalized Poisson regression (GLMNet) on most count-prediction datasets
  • ▸ Sample-to-taxon ratio is a key predictor of model performance
  • ▸ PLNNetwork is better suited for recovering broad undirected interactions

Merits

Strength

The study employs a unified held-out evaluation framework, allowing for a direct comparison of model performance across diverse datasets.

Demerits

Limitation

The study only considers a limited range of model specifications and evaluation metrics, which may not capture the full range of possible scenarios.

Expert Commentary

This study makes a valuable contribution to the field of microbiome data analysis by providing a systematic comparison of PLN and GLMNet models. The findings suggest that PLN models are better suited for high-dimensional microbial count data, and that the sample-to-taxon ratio is a key predictor of model performance. However, the study's limitations, such as the restricted range of model specifications and evaluation metrics, should be addressed in future research. Researchers should consider the broader implications of these findings for the development of more robust and interpretable machine learning methods for biological data analysis.

Recommendations

  • ✓ Future studies should explore the application of PLN and GLMNet models to a wider range of biological datasets
  • ✓ Researchers should develop more robust and interpretable machine learning methods for analyzing high-dimensional biological data

Sources

Original: arXiv - cs.LG

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