A Probabilistic Model for Zero-Inflated Count Tensors with Structured Latent Representations

Abstract

We propose a unified probabilistic framework for modeling high-dimensional count tensors with excess zeros. Such data arise naturally in a variety of applications, including single-cell Hi-C experiments, where observations are represented as a third-order tensor indexed by genomic locus pairs and cells. We develop a zero-inflated Poisson tensor model that captures the underlying latent structure through a low-rank tensor decomposition. For single-cell Hi-C data, the proposed framework further accommodates heterogeneous cell populations through latent cluster structure and exploits the ordered nature of genomic loci via smooth latent representations. We develop a likelihood-based estimation procedure together with a Bayes-optimal classifier for distinguishing structural zeros from technical zeros, enabling principled false-zero detection, imputation, and uncertainty quantification. Theoretically, we establish identifiability of the proposed model and consistency of the proposed estimators. Simulation studies and analyses of single-cell Hi-C data demonstrate improved performance in false-zero detection, latent structure recovery, and clustering.