Analyzing gene relationships in single-cell RNA data
Official title: Statistical methods for co-expression network analysis of population-scale scRNA-seq data
Funded at University of Wisconsin-Madison · NIH-10740240 · Data updated 2026-11-12
This study is looking at how genes work together in individual cells to uncover important connections that might help us understand diseases better, especially how our bodies respond to infections.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Wisconsin-Madison NIH-funded |
| Lab location | 1 site (Madison, United States) |
| Project start | September 5, 2023 |
| Project end | August 31, 2025 |
| Funding year | 2023 |
| Award (this year) | $407,645 |
| NIH project number | 1R21HG012881-01A1 |
| Project ID | NIH-10740240 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals with conditions that affect gene expression, particularly those related to the immune system and hematopoietic disorders.
✕Less likely to help
Patients with stable conditions that do not involve significant gene expression changes may not benefit from this research.
What this research studies
This research focuses on developing advanced statistical methods to analyze gene co-expression networks using single-cell RNA sequencing (scRNA-seq) data. By examining how genes interact at a population level, the project aims to uncover hidden relationships that traditional analysis methods may overlook. The approach involves creating personalized dynamic networks that reflect individual variations in gene expression, which could lead to better understanding of biological processes and disease mechanisms. The research will also explore how these networks relate to different phenotypes, such as responses to infections.
Why it matters
Potential benefit: If successful, this research could lead to more personalized and effective treatments by identifying key gene interactions that influence health outcomes.
How similar studies have gone: Previous research has shown promise in using network analysis for bulk RNA-seq data, but this approach for scRNA-seq is relatively novel and untested.
Where this research is happening
-
University of Wisconsin-Madison
Madison, United States Active
Researchers
- Principal investigator: Keles, Sunduz — University of Wisconsin-Madison
- Study coordinator: Keles, Sunduz
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.