# Statistical methods for gene regulatory analysis and single cell genomics

> **NIH NIH R01** · STANFORD UNIVERSITY · 2020 · $376,665

## Abstract

PROJECT SUMMARY
This project has three major components.
Analysis of matched accessibility, expression and 3D contact data in diverse contexts
In this component we will develop statistical methods for modeling gene regulatory relations based
on the joint analysis of gene expression data (from RNA-seq), chromatin accessibility data (from
ATAC-seq or DNase-seq) and 3D interaction data (from Hi-C or HiChIP) from diverse cellular
contexts. The focus will on “matched data sets” where the different types of omics assays are
performed on the same or closely matched cellular contexts. The methodology should be
applicable to cases when some data types are missing in a substantial subset of contexts. The
methodology should also be able to incorporate a large variety of non-context dependent data.
Analysis of matched omics data in time courses
In this component we will develop an approach to the modeling of time course data that is capable
of utilizing prior information provided by a general regulatory model learned from diverse contexts.
The emphasis will be on experiments where expression, accessibility and 3D contact data are
generated at each time points to study a specific biological processes.
Joint analysis of multiple types of single cell omics data
In this component we will develop statistical methods for the joint analysis of single cell omics data
on expression, accessibility, and 3D contact. Specifically, we are interested in the situation where
multiple types of single cell omics data are generated from the same heterogeneous population of
cells. We will develop statistical methods to resolve the population into relevant subpopulations
and to infer subpopulation-specific gene regulatory relations. We will also develop methods to
handle the case when 3D contact data is from bulk sample.

## Key facts

- **NIH application ID:** 10001015
- **Project number:** 5R01HG010359-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Wing H. WONG
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $376,665
- **Award type:** 5
- **Project period:** 2019-08-22 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10001015

## Citation

> US National Institutes of Health, RePORTER application 10001015, Statistical methods for gene regulatory analysis and single cell genomics (5R01HG010359-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10001015. Licensed CC0.

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