Robust Bayesian Tensor Factorization with Zero-Inflated Poisson Model and Consensus Aggregation

TL;DR

This paper introduces ZIPTF and C-ZIPTF, novel tensor factorization methods tailored for zero-inflated count data like scRNA-seq, improving accuracy and reproducibility over existing methods.

Contribution

The paper proposes ZIPTF and C-ZIPTF, new tensor factorization techniques that handle zero-inflated data and reduce stochastic variability, enhancing interpretability and reproducibility.

Findings

ZIPTF outperforms baseline methods in reconstruction accuracy.

C-ZIPTF improves consistency and accuracy of factorization.

Methods recover biologically meaningful gene programs.

Abstract

Tensor factorizations (TF) are powerful tools for the efficient representation and analysis of multidimensional data. However, classic TF methods based on maximum likelihood estimation underperform when applied to zero-inflated count data, such as single-cell RNA sequencing (scRNA-seq) data. Additionally, the stochasticity inherent in TFs results in factors that vary across repeated runs, making interpretation and reproducibility of the results challenging. In this paper, we introduce Zero Inflated Poisson Tensor Factorization (ZIPTF), a novel approach for the factorization of high-dimensional count data with excess zeros. To address the challenge of stochasticity, we introduce Consensus Zero Inflated Poisson Tensor Factorization (C-ZIPTF), which combines ZIPTF with a consensus-based meta-analysis. We evaluate our proposed ZIPTF and C-ZIPTF on synthetic zero-inflated count data and synthetic and real scRNA-seq data. ZIPTF consistently outperforms baseline matrix and tensor factorization methods in terms of reconstruction accuracy for zero-inflated data. When the probability of excess zeros is high, ZIPTF achieves up to $2.4\times$ better accuracy. Additionally, C-ZIPTF significantly improves the consistency and accuracy of the factorization. When tested on both synthetic and real scRNA-seq data, ZIPTF and C-ZIPTF consistently recover known and biologically meaningful gene expression programs.