Latent-Class Poisson-Lognormal Mixed Models for Clustering Multivariate Longitudinal Count Data
Abstract
Multivariate longitudinal count data arise in biological studies, where responses may be over-dispersed, correlated within subjects and over time, measured at irregular times, and exhibit unobserved subgroup structure. Our goal was to develop a model that can accommodate over-dispersion, longitudinal dependence, cross-outcome dependence, covariate effects, irregular observation times, and covariate-dependent latent-class membership. We propose a latent-class Poisson-lognormal mixed model for clustering heterogeneous multivariate count trajectories. Estimation is performed using a variational EM algorithm designed for scalable inference. Simulation studies show accurate parameter estimation, strong latent-class recovery, and reliable selection of the number of classes, as compared to state-of-the-art approaches. We also applied the proposed approach to longitudinal neutrophil RNA-seq data from hospitalized COVID-19 patients as an illustration. The proposed framework provides a flexible approach for clustering complex longitudinal biological count data. Implementation code is available at https://github.com/PengqiLiu-Mc/latent-class-mpln-mixed-models.
Related
- A latent space hidden Markov model for clustering views in longitudinal multilayer networks
- A multivariate unsupervised lognormal-GPD mixture for dependent losses
- Robust model-based clustering via mixtures of multivariate pseudo-Voigt distributions
- Salient Bayesian Clustering for Proportional Data via a Multivariate Beta Mixture Model
- Twoblock clustering trees with coskewness-based dimension reduction: recovering piecewise multivariate linear regimes
BibTeX
@misc{liu2026latentclass,
title = {{Latent-Class Poisson-Lognormal Mixed Models for Clustering Multivariate Longitudinal Count Data}},
author = {Pengqi Liu and Utkarsh J. Dang and Sanjeena Subedi},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.12860},
eprint = {2610.12860},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.12860},
}
RIS
TY - UNPB
TI - Latent-Class Poisson-Lognormal Mixed Models for Clustering Multivariate Longitudinal Count Data
AU - Pengqi Liu
AU - Utkarsh J. Dang
AU - Sanjeena Subedi
PY - 2026
DA - 2026/10/09
PB - arXiv
DO - 10.48550/arxiv.2610.12860
UR - https://arxiv.org/abs/2610.12860
AB - Multivariate longitudinal count data arise in biological studies, where responses may be over-dispersed, correlated within subjects and over time, measured at irregular times, and exhibit unobserved subgroup structure. Our goal was to develop a model that can accommodate over-dispersion, longitudinal dependence, cross-outcome dependence, covariate effects, irregular observation times, and covariate-dependent latent-class membership. We propose a latent-class Poisson-lognormal mixed model for clustering heterogeneous multivariate count trajectories. Estimation is performed using a variational EM algorithm designed for scalable inference. Simulation studies show accurate parameter estimation, strong latent-class recovery, and reliable selection of the number of classes, as compared to state-of-the-art approaches. We also applied the proposed approach to longitudinal neutrophil RNA-seq data from hospitalized COVID-19 patients as an illustration. The proposed framework provides a flexible approach for clustering complex longitudinal biological count data. Implementation code is available at https://github.com/PengqiLiu-Mc/latent-class-mpln-mixed-models.
ER -