# Differential exon usage in single cell RNA-seq

> **NIH NIH R16** · UNIVERSITY OF NEVADA LAS VEGAS · 2024 · $145,639

## Abstract

Project Summary
 Alternative splicing generates transcripts that vary between tissues and cell types, and
contributes to cell differentiation and organ development. Despite its importance in development and
cell identity, alternative splicing is usually neglected in single cell RNA sequencing (scRNA-seq)
analysis. This is due to the challenge in identifying confident alternative splicing events from scRNA-
seq data, which are limited in read-depth, and comes with large amount of noise due to variation in
molecule capture efficiency, amplification bias, and excessive zero-counts or dropouts. We propose
a novel approach to infer differential splicing based on short-read full-length scRNA-seq data,
utilizing read counts mapping to adjacent exons. In addition, we plan to systematically evaluate
existing approaches to answer questions on the strategy regarding the variable, pooling and
dispersion estimates. Finally, we will assess the accuracy of short read based methods by comparing
against scRNA-seq generated with new types of protocols. The work proposed will increase our
understanding on the utility and limitations of short read scRNA-seq data, and provide a better
pipeline for alternative splicing analysis in scRNA-seq.

## Key facts

- **NIH application ID:** 10835923
- **Project number:** 5R16GM149510-02
- **Recipient organization:** UNIVERSITY OF NEVADA LAS VEGAS
- **Principal Investigator:** Mira Han
- **Activity code:** R16 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $145,639
- **Award type:** 5
- **Project period:** 2023-06-01 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10835923

## Citation

> US National Institutes of Health, RePORTER application 10835923, Differential exon usage in single cell RNA-seq (5R16GM149510-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10835923. Licensed CC0.

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