# Integrative characterization of cell state via modeling of multi-omics data

> **NIH NIH R35** · OREGON HEALTH & SCIENCE UNIVERSITY · 2022 · $385,000

## Abstract

PROJECT SUMMARY
Advent of singe-cell genomics has enhanced our ability to study heterogeneous cell populations (1) to track the
time course of cellular differentiation and identify drivers, (2) to identify potentially novel cell states/types in an
unsupervised manner, and (3) to identify cell populations that are linked disease outcomes. More recently, we
and others have developed single-cell multiomics technologies that enable measuring of multiple modalities of
a single cell at the same time, including the transcriptome, the methylome, the epigenome and surface marker
proteins. These assays offer unprecedented opportunities to study the state of single cells more
comprehensively; by developing the necessary computational methods for these assays, we can obtain more
accurate and deeper characterization of cell states and obtain mechanistic insights into the relationships
between state of chromatin, the proteome and the transcriptomic states within an individual cell. However,
there is a dramatic lack of interpretable computational methods to study multiomics data. To address this gap
and propel the field forward, we are proposing to develop computational methodologies that will (1)
characterize the state of a single cell in a multiomic setting in an unsupervised manner, (2) characterize the
regulatory landscape of single-cells by identifying transcription factor binding activity and (3) identify multiple
structural variations at single-cells.
First, we will develop interpretable topic-modeling based methods for characterizing single cells based on
multiomic readout. Building on our past success with topic models to accurately cluster and characterize
single-cell populations, we will develop novel topic modeling approaches for multiomics assays in order to
achieve a much deeper profiling of the state of a cell, which will lead to potential insights into the links between
multiple modalities measured by a multiomic assay, such as transcriptomic and epigenomic state. Second, we
propose to develop a single-cell transcription factor footprinting (TF) methods. Computational detection of TF
footprints can identify the landscape of active transcription factors that determine important drivers of cell state
and identity. We will develop the methodology to identify active transcription factors at an unprecedented
single-cell resolution and to investigate links between the methylome and TF binding. Lastly, we will develop
methods to identify different types of structural variations at single cell resolution by leveraging novel multiomic
assays developed by my collaborators. This methodology will enhance our ability to study the heterogeneity of
structural variations in different cell populations. Overall, the proposed suite of computational methodologies
will allow a broad audience of researchers who generate and analyze multiomic data to annotate the multi-
modally measured cell states in heterogeneous cell populations in a deep and unprecedented manner a...

## Key facts

- **NIH application ID:** 10501946
- **Project number:** 1R35GM147698-01
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** Galip Gurkan Yardimci
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $385,000
- **Award type:** 1
- **Project period:** 2022-09-16 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10501946

## Citation

> US National Institutes of Health, RePORTER application 10501946, Integrative characterization of cell state via modeling of multi-omics data (1R35GM147698-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10501946. Licensed CC0.

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