# Bayesian Differential Causal Network and Clustering Methods for Single-Cell Data

> **NIH NIH R01** · TEXAS A&M UNIVERSITY · 2024 · $297,934

## Abstract

PROJECT SUMMARY (See instructions): 
The emergence of single-cell RNA-sequencing (scRNA-seq) techniques has motivated many 
computational methods to study gene regulation and cell differentiation at the single-cell level. However, to 
improve the translational value of scRNA-seq, new methods are required to comparatively study the 
molecular differences between normal and pathological cells/tissues, and between control and 
case/treatment groups. The vast majority of existing network and clustering models have focused on 
scRNA-seq data generated under one experimental condition. Moreover, most existing scRNA-seq network 
models are correlative in nature and do not infer causality. What remains lacking are rigorous statistical 
methods for inference of differential causal gene regulation and cell composition in response to 
experimental interventions. There is, therefore, a critical need to develop novel methodologies for 
identifying the effects of experimental interventions on causal gene regulatory relationships and cell 
differentiation by jointly modeling scRNA-seq data across experimental groups. Without such tools, 
mechanistically understanding gene regulatory activities and cell differentiation will likely remain difficult. 
Our overall objective is to design and validate Bayesian network and clustering models for identifying 
differential causal gene regulatory networks and cell composition for scRNA-seq data generated under 
different experimental conditions. To achieve the overall objective, three specific aims will be pursued: 
Aim 1: Develop a differential zero-inflated negative binomial Bayesian network model to construct 
differential cell-type-specific causal gene regulatory networks under two experimental conditions. 
Aim 2: Develop a trajectory-dependent directed cyclic graph model to construct cell-specific causal 
networks with feedback loops and monitor its structural changes with cell development. 
Aim 3: Develop a scalable Bayesian semiparametric differential clustering method to discover differential 
cell composition and cell-type-specific marker genes that are shared and/or unique to each experimental 
group.

## Key facts

- **NIH application ID:** 10928233
- **Project number:** 5R01GM148974-03
- **Recipient organization:** TEXAS A&M UNIVERSITY
- **Principal Investigator:** Yang Ni
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $297,934
- **Award type:** 5
- **Project period:** 2022-09-21 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10928233

## Citation

> US National Institutes of Health, RePORTER application 10928233, Bayesian Differential Causal Network and Clustering Methods for Single-Cell Data (5R01GM148974-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10928233. Licensed CC0.

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