Hierarchical Bayesian Model for Gene Deconvolution and Functional Analysis in Human Endometrium Across the Menstrual Cycle

2025-10-30

AI Summary

Bulk RNA-seq fails to resolve cell-type-specific expression dynamics in the human endometrium, whose cellular composition undergoes dramatic cyclical changes across the menstrual cycle. Method: We propose a hierarchical Bayesian deconvolution model that jointly infers cell-type proportions and cell-type-specific gene expression profiles. The model incorporates single-cell reference atlases as priors and employs Markov Chain Monte Carlo (MCMC) sampling for robust inference, ensuring resilience to reference bias and technical noise. Contribution/Results: In both simulated and real datasets, the model accurately recapitulates dynamic shifts in epithelial, stromal, and immune cell proportions from the proliferative to secretory phase. It further identifies a decidualization-associated functional gene module specifically upregulated in secretory-phase stromal cells. Results are validated through multiple robustness checks. This interpretable computational framework advances understanding of hormone-driven endometrial functional regulation and holds promise for clinical translation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Bayesian Learning

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Bulk tissue RNA sequencing of heterogeneous samples provides averaged gene expression profiles, obscuring cell type-specific dynamics. To address this, we present a probabilistic hierarchical Bayesian model that deconvolves bulk RNA-seq data into constituent cell-type expression profiles and proportions, leveraging a high-resolution single-cell reference. We apply our model to human endometrial tissue across the menstrual cycle, a context characterized by dramatic hormone-driven cellular composition changes. Our extended framework provides a principled inference of cell type proportions and cell-specific gene expression changes across cycle phases. We demonstrate the model's structure, priors, and inference strategy in detail, and we validate its performance with simulations and comparisons to existing methods. The results reveal dynamic shifts in epithelial, stromal, and immune cell fractions between menstrual phases, and identify cell-type-specific differential gene expression associated with endometrial function (e.g., decidualization markers in stromal cells during the secretory phase). We further conduct robustness tests and show that our Bayesian approach is resilient to reference mismatches and noise. Finally, we discuss the biological significance of our findings, potential clinical implications for fertility and endometrial disorders, and future directions, including integration of spatial transcriptomics.

Problem

• Deconvolves bulk RNA-seq into cell-type profiles
• Infers cellular composition changes across menstrual cycle
• Identifies cell-specific gene expression in endometrium

Innovation

• Hierarchical Bayesian model deconvolves bulk RNA-seq data
• Leverages single-cell reference for cell-type expression profiles
• Infers cell proportions and gene expression changes