# Statistical Methods for Gene Regulatory Analysis From Single Cell Genomics Data

> **NIH NIH P20** · CLEMSON UNIVERSITY · 2024 · $24,423

## Abstract

Gene regulatory networks (GRNs) provide information on the cis-regulatory elements controlling contextspecific 
expression of target genes, as well as the transcription factors acting on these elements. 
Understanding the dynamics of gene regulation is fundamental for understanding how cells undergo 
specialization for different functions, despite having the same genome; how cells respond to different 
environments by modulating gene expression; and how non-coding genetic variants cause diseases. 
Inference of GRNs from genomics data is a systematic approach to study gene regulation. However, the 
accuracy of such inference is limited if the cellular context under interest is a heterogenous mixture. The 
development of single cell genomics technologies can fill this gap by providing high-resolution GRNs. 
Therefore, there is a compelling need for efficient statistical methods to infer GRNs from single cell 
genomics data. The long-term goal of this project is to obtain a mechanistic understanding of how noncoding 
genetic variants affect cellular context-dependent GRNs and influence phenotypes. Single cell 
transcriptomic (scRNA-seq) and chromatin accessibility (scATAC-seq) data provide information on different 
cellular features, i.e., gene expression and active regulatory element location, respectively. Integration of 
these two types of data will provide more accurate information on gene regulation. In Specific Aim 1, we 
will extend our initial studies inferring subpopulation-dependent GRNs from unpaired scRNA-seq and 
scATAC-seq data (supported by a COBRE in Human Genetics Pilot Project since 02/01/2022) by 
benchmarking existing methods for integrative analysis of unpaired scRNA-seq and scATAC-seq data to 
build an optimized pipeline for unpaired data analysis. We will develop a statistical method to infer 
subpopulation-specific GRNs and analyze large-scale published datasets to build a database of GRNs for 
hundreds of cellular contexts. In Specific Aim 2, we will develop statistical methods for comparative gene 
regulatory analysis based on single cell genomics data. The comparison of GRNs between samples from 
diseased versus healthy patients or between two different treatments is an important scientific problem. 
Thus, an efficient computational method for comparative gene regulatory analysis based on different types 
of single cell genomics data is needed. In Specific Aim 3, we will develop a method and software to infer 
cell type specific GRNs from sc-multiome data. This method and software would have a significant and 
broad impact by providing a detailed view of how trans- and cis-regulatory elements work together to affect 
gene expression in a cell type-specific manner.

## Key facts

- **NIH application ID:** 10808906
- **Project number:** 5P20GM139769-04
- **Recipient organization:** CLEMSON UNIVERSITY
- **Principal Investigator:** Robert R. H Anholt
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $24,423
- **Award type:** 5
- **Project period:** 2021-02-10 → 2024-02-02

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10808906

## Citation

> US National Institutes of Health, RePORTER application 10808906, Statistical Methods for Gene Regulatory Analysis From Single Cell Genomics Data (5P20GM139769-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10808906. Licensed CC0.

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