Tweedieverse and DAssemble: Robust Multimodal Analysis for Microbiome Data

Thomas M. Menino Convention & Exhibition Center 

The reproducibility crisis in omics data science is particularly evident in microbiome research, where heterogeneity in sequencing technologies, library preparation protocols, and preprocessing pipelines leads to complex count data characterized by sparsity, overdispersion, and compositional effects. Despite this variability, most differential abundance methods rely on a single distributional assumption across all features, resulting in model misspecification, unstable inference, and inconsistent findings. We present two complementary frameworks to mitigate this gap, Tweedieverse and DAssemble. Tweedieverse leverages the flexibility of the Tweedie distribution to enable feature-specific distributional adaptation through a single tuning parameter, providing a unified approach that captures diverse mean–variance relationships across features and modalities. DAssemble, on the other hand, improves robustness by aggregating differential abundance signals across multiple statistical models, thereby mitigating model-specific biases and reducing sensitivity to any single modeling assumption. Analyses of real and synthetic microbiome datasets demonstrate improved statistical power, false discovery rate control, and stability of discoveries relative to published methods. Open-source R packages implementing these methods are publicly available at https://github.com/himelmallick/DAssemble and https://github.com/himelmallick/tweedieverse.

Differential Analysis

Microbiome

Multimodal

Multi-omics

Metagenomics