# Novel Systems Biology Methods for the Cell-type-specific Regulatory Networks Reconstruction from scRNA-seq Data

> **NIH NIH R15** · SAINT LOUIS UNIVERSITY · 2022 · $454,500

## Abstract

Project Summary/Abstract
Complex diseases studies have revealed how specific cell types contribute to the evolution of
different diseases. Many drugs are not effective to a large population of patients due to the
cellular diversity. Modern single-cell RNA sequencing (scRNA-seq) technologies provide
opportunities to detect and dissect the heterogeneity in cells, enable us to measure the gene
expression level of thousands of individual cells in a single experiment. Comprehensive analysis
of scRNA-seq data and correct reconstruction of cell-type-specific regulatory networks could
help develop personalized and targeted treatment of some complex diseases for different
patients. So, the scRNA-seq has more advantages than the traditional bulk RNA-seq. Most of
genome and computational studies are based on the traditional bulk RNA sequencing data,
which measure the average expression of the cell population, without examining the cell-type-
specific expression profiles. There are several challenges in the scRNA-seq data analysis and
cell-type-specific regulatory network reconstruction. The first challenge is, there are a large
amount of missing values in the scRNA-seq data, which will attenuate the power and
advantages of scRNA-seq, and make it difficult to correctly reconstruct a cell-type-specific
network. We propose novel data-driven deep generative modeling methods to impute (estimate)
the missing static and time-series scRNA-seq data without making certain distribution
assumptions for the missing values. Some studies have revealed that the regulatory networks
undergo systematic rewiring at different stages. It is of importance to know how many stages
the cell has experienced, and when the stage transition starts to occur from the high-
dimensional scRNA-seq data, which is the second challenge problem—change-points detection.
We propose to develop an adversarial network-based method to identify the change-points
without introducing model parameters. Another challenge is how to correctly reconstruct and
intelligently validate the cell-type-specific regulatory networks from the scRNA-seq data, and
identify key regulatory components that contribute to the network rewiring during stage
transition. We propose to integrate the deep generative modeling methods and change-points
detection algorithm with our weighted dynamic Bayesian network and Model Checking
technique in a unified framework to reconstruct cell-type-specific regulatory networks. Our
studies will improve our understanding of regulatory network dynamics, and provide a key to
discovering the mechanisms underlying the pathogenesis of diseases.

## Key facts

- **NIH application ID:** 10579768
- **Project number:** 1R15GM148915-01
- **Recipient organization:** SAINT LOUIS UNIVERSITY
- **Principal Investigator:** Haijun Gong
- **Activity code:** R15 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $454,500
- **Award type:** 1
- **Project period:** 2022-09-21 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10579768

## Citation

> US National Institutes of Health, RePORTER application 10579768, Novel Systems Biology Methods for the Cell-type-specific Regulatory Networks Reconstruction from scRNA-seq Data (1R15GM148915-01). Retrieved via AI Analytics 2026-05-28 from https://api.ai-analytics.org/grant/nih/10579768. Licensed CC0.

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