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A Regularized Bayesian Dirichlet-multinomial Regression Model for Integrating Single-cell-level Omics and

Yanghong Guo¹, Lei Yu², Lei Guo²

¹Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, Texas, U.S.A.

Biorxiv : the Preprint Server for Biology

|June 19, 2024

Summary

This study introduces a new computational framework to link cell type abundance from single-cell RNA sequencing data with clinical variables. The method reveals significant disease-specific associations, offering new biological insights.

Keywords:

Dirichlet-multinomial regression models hierarchical tree integrative analysis single-cell RNA sequencing spike-and-slap priors

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Area of Science:

Computational Biology
Genomics
Translational Medicine

Background:

Cell type abundance varies significantly between patients, influenced by phenotype, age, gender, and lifestyle.
Existing methods struggle to integrate single-cell omics data with clinical variables effectively.

Purpose of the Study:

To develop a novel computational framework for integrating single-cell RNA sequencing (scRNA-seq) data with patient-level clinical data.
To investigate the relationships between cell type abundance and clinical variables across different diseases.

Main Methods:

A regularized Bayesian Dirichlet-multinomial regression framework was developed.
A hierarchical tree structure was incorporated to analyze relationships at various cell-type granularities.
The model was applied to scRNA-seq data from patients with pulmonary fibrosis, COVID-19, and non-small cell lung cancer.

Main Results:

The proposed framework successfully identified significant associations between specific cell types and clinical variables.
These associations were observed across the three distinct disease cohorts studied.
The integrative analysis provided novel biological insights into disease mechanisms.

Conclusions:

The developed Bayesian regression framework offers a robust method for integrating scRNA-seq and clinical data.
This approach can uncover cell-type-specific associations with clinical variables, advancing disease understanding.
The findings have the potential to inform future clinical interventions and personalized medicine strategies.