# Statistical Methods for Bulk-Tissue and Single-Cell Multi-Omics Integration

> **NIH NIH R35** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2020 · $376,250

## Abstract

PROJECT SUMMARY/ABSTRACT
Single-cell sequencing circumvents the averaging artifacts associated with traditional bulk population data and
has seen rapid technological developments over the past few years. This offers new opportunities to study
genomic, transcriptomic, and epigenomic heterogeneity at the cellular level without cell type confounding, but it
also requires novel analytical approaches. One major challenge in such genomic studies is the lack of rigorous
methods for integrating bulk-tissue and single-cell sequencing data and for aligning multi-modal single-cell omics
data. The research program of my lab centers around developing statistical/computational methods and
bioinformatics tools to better utilize and analyze different types of next-generation sequencing data, with a special
focus on detecting structural variants, deciphering genomic and transcriptomic heterogeneity, and assessing
cellular heterogeneity by single-cell omics approaches. Our long-term vision is to introduce problems arising
from new biomedical data to the statistics community and to provide data-driven statistical methods and open-
source tools to biomedical researchers for better data analysis and experimental design. Specifically, in the next
five years, our proposed program of research will focus on the following interconnected objectives: (i) bulk omics
deconvolution aided by single-cell sequencing, followed by association testing with clinical variables; (ii) joint
modeling of bulk genomic sequencing and single-cell transcriptomic sequencing data to simultaneously infer
DNA and RNA variation at the single-cell level; and (iii) multi-modal alignment of single-cell omics data. During
this period, we will keep collaborating with experimental labs, applying our developed methods to interrogate
cellular heterogeneity under both biological and clinical settings. We will provide our methods as freely available
and open-source R packages, which will include extensive tutorials and workflows that are accessible and useful
to the biomedical research community.

## Key facts

- **NIH application ID:** 10028728
- **Project number:** 1R35GM138342-01
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Yuchao Jiang
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $376,250
- **Award type:** 1
- **Project period:** 2020-09-05 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10028728

## Citation

> US National Institutes of Health, RePORTER application 10028728, Statistical Methods for Bulk-Tissue and Single-Cell Multi-Omics Integration (1R35GM138342-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10028728. Licensed CC0.

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