# Statistical Methods for Elucidating Regulatory Mechanisms and Functional Impacts of Transcriptome Variation at Population and Single-Cell Scales

> **NIH NIH R35** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2023 · $110,272

## Abstract

PROJECT SUMMARY / ABSTRACT
Bulk RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) are powerful high-throughput
techniques for studying transcriptome variation at population and single-cell scales. Many computational
methods have been developed for analyzing bulk RNA-seq and scRNA-seq data. However, there remain multiple
challenges in identifying disease/trait-associated genes from population-scale bulk RNA-seq data, studying
temporal transcriptome dynamics from scRNA-seq data, and benchmarking scRNA-seq computational tools. In
our proposed research, we will develop statistical methods to address these challenges and elucidate regulatory
mechanisms of transcriptome variation at population and single-cell scales. At the population scale, we will
develop a unified statistical framework for identifying associations between genotypes and RNA isoform
abundances, the “ideal” RNA-level molecular phenotypes. Our framework will unify existing diverse approaches
that focus on specific aspects of transcript variation (e.g., gene expression, alternative exon/intron usage, and
alternative polyadenylation) and, for the first time, incorporate the uncertainty in estimating isoform abundances.
As a result, our framework should improve the accuracy and power in detecting associations between genetic
variants and genes. We will make our framework applicable to all second- and third-generation RNA-seq data
and apply it to the GTEx data, the most comprehensive genotype-transcriptome database, to discover genes
that are associated with the disease/trait-associated variants found by GWAS. At the single-cell scale, we will
develop three methods: 1) a valid statistical test for detecting temporally differentially expressed genes from
scRNA-seq data while accounting for the uncertainty in trajectory inference, 2) a clustering method that
integrates mechanistic and statistical modeling for identifying cell subpopulations along a temporal process, and
3) a comprehensive and interpretable simulator that generates realistic scRNA-seq data for benchmarking
computational tools. The first two methods will offer much-in-demand solutions to temporal gene expression
analysis of scRNA-seq data. Their applications will include the study of macrophage transcriptome changes
during immune responses. The third method will be the first scalable and transparent simulator that captures
gene correlations and allows the tuning of experimental parameters, including cell numbers and library sizes.
Overall, we expect that our proposed methods will significantly improve the power, robustness, and
reproducibility of studying transcriptome variation from bulk and single-cell RNA-seq data.

## Key facts

- **NIH application ID:** 10799343
- **Project number:** 3R35GM140888-03S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Jingyi Jessica Li
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $110,272
- **Award type:** 3
- **Project period:** 2021-06-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10799343

## Citation

> US National Institutes of Health, RePORTER application 10799343, Statistical Methods for Elucidating Regulatory Mechanisms and Functional Impacts of Transcriptome Variation at Population and Single-Cell Scales (3R35GM140888-03S1). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10799343. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*