# Computational Tools for Single Cell Analysis: Application to Retinal Degeneration

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2021 · $397,093

## Abstract

Identifying the regulatory networks altered during retinal degeneration will provide insights into the mechanisms
underlying retinal disease. Analysis of such network perturbation at the single cell level will help us to pinpoint
the key molecular events that could be missed in traditional analysis using the bulk samples. However, to
identify the altered networks in retinal disease is still challenging, partly due to the lack of powerful
computational tools. First, many clustering methods yield different and sometimes conflicting results. Second,
single cell expression can be used to detect previously unrecognized cell types, while the current clustering
algorithms are often not sensitive enough to detect novel, sometimes rare, cell types. Third, genomic
interactions obtained from bulk samples and single cells are likely to be complementary to each other and
reflect different aspects in terms of gene regulation, co-expression and protein-protein interactions. Novel
integrative methods are desired to maximize the information we gain from these genomic datasets. To address
these challenges, we will develop computational approaches for single cell data analysis. Specifically, we will
develop an iterative clustering method for single cell gene expression analysis (Aim 1). Our approach is
designed to be robust and sensitive. We will then develop a method to determine active regulatory networks by
integrating single cell RNA-Seq dataset and ATAC-Seq from bulk samples (Aim 2). This method will enable us
to identify the regulatory circuits at the single cell level. We will then perform single cell RNA-Seq in retinal
degenerative models (Aim 3) and apply our computational approaches to the dataset. We expect to identify the
drivers and pathways involved in photoreceptor degeneration. Finally, we will develop a database for cell
marker genes by analyzing publically available single cell datasets (Aim 4). We believe that the computational
algorithms and database we propose to develop will be valuable resource for the research community. The in-
depth study on retinal degenerative models will reveal key molecular events that lead to the disease and
provide novel therapeutic targets for the retinal degenerative diseases.

## Key facts

- **NIH application ID:** 10179397
- **Project number:** 5R01EY029548-04
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Jiang Qian
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $397,093
- **Award type:** 5
- **Project period:** 2018-09-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10179397

## Citation

> US National Institutes of Health, RePORTER application 10179397, Computational Tools for Single Cell Analysis: Application to Retinal Degeneration (5R01EY029548-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10179397. Licensed CC0.

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