# Cheng - Proj 2

> **NIH NIH P20** · DARTMOUTH COLLEGE · 2020 · $236,505

## Abstract

PROJECT SUMMARY/ABSTRACT
Single-cell RNA-seq (scRNA-seq) analysis has been widely used to determine transcriptomic profiles at a
single-cell resolution. A large number of computational methods have been developed to analyze scRNA-seq
data. Most of these methods focus on better processing and interpretation of scRNA-seq data, and there is a
lack of computational approaches for downstream analyses that generate novel biological hypotheses from
scRNA-seq profiles. In this project, we propose to develop a new computational framework for scRNA-seq data
to infer the regulatory activity of transcriptional factors (TFs) at the single-cell level, and then construct
regulatory network associated with single-cell phenotypes. In contrast to all existing network inference
methods, our framework determines regulatory interactions in single cells based on TF activities, rather than
their expression levels. This is in line with the fact that TF functions are rarely reflected by their expression due
to intensive post-transcriptional and post-translational events. To facilitate the application of this framework, we
will construct a user-friendly database to release and update the software/packages, pipelines, and processed
data profiles that will be produced from this project. Additionally, we will apply and test our framework in two in-
house scRNA-seq datasets for melanoma and systemic sclerosis generated by our collaborators. This
framework will provide useful tools to reveal regulatory mechanisms that determine the phenotypes of cells
captured in scRNA-seq analyses. Considering the wide application of scRNA-seq approaches, we expect that
our framework will benefit a broad range of research communities in biological and biomedical areas.

## Key facts

- **NIH application ID:** 9985946
- **Project number:** 5P20GM130454-02
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** CHAO CHENG
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $236,505
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9985946

## Citation

> US National Institutes of Health, RePORTER application 9985946, Cheng - Proj 2 (5P20GM130454-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9985946. Licensed CC0.

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