# Statistical Methods for Single-Cell Transcriptomics

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2020 · $372,914

## Abstract

PROJECT SUMMARY
Cells are the basic biological units of multicellular organisms. Recent technological breakthroughs have made
it possible to measure gene expression at the single-cell level, thus paving the way for exploring gene
expression heterogeneity among cells. The collection of abundances of all RNA species in a cell forms its
“molecular fingerprint”, enabling the investigation of many fundamental biological questions beyond those
possible by traditional bulk RNA-seq experiments. Single-cell RNA-seq (scRNA-seq) allows us to better
describe the lineage and type of single cells, characterize the stochasticity of gene expression across cells,
and improve our understanding of cellular function in health and disease. ScRNA-seq analysis is transforming
biomedical sciences, and has already made great impact in fields such as neuroscience and immunology, and
can enhance our understanding of disease development in numerous other contexts including cardiometabolic
diseases. However, scRNA-seq data present new challenges for which standard analytical methods are not
designed to confront. Current scRNA-seq protocols are complex, often introducing technical biases that vary
across cells, which, if not properly removed, can obscure cell type identification and lead to biased results in
downstream analyses. Published scRNA-seq studies have mainly been proof-of-principal studies illustrating
the utility of scRNA-seq in cell type classification and other basic biological analyses. However, as the use of
scRNA-seq continues to grow, researchers are beginning to explore their utility in disease gene discovery.
Building upon our expertise in statistical methods development and our experience with analysis of genomics
data for human cardiometabolic diseases, in this proposal, we propose to develop novel statistical methods to
address some of the key analytical challenges in scRNA-seq analysis. We will guide methods development
through the analysis of scRNA-seq data generated from ongoing collaborations with collaborators at the
University of Pennsylvania and Columbia University. We propose the following specific aims. Aim 1: Develop
methods to recover gene expression and identify cell types. Aim 2: Develop methods to detect gene
expression changes between cell types or conditions. Aim 3: Develop methods to estimate isoform-specific
gene expression and detect differential alternative splicing. Aim 4: Develop methods to model allele-specific
transcriptional bursting and its genetic regulation. This proposal addresses critical challenges in scRNA-seq
analysis, and it brings together an exceptional team of scientists with proven track record in statistical
genomics, single-cell biology, and cardiometabolic disease. The successful completion of this project will allow
researchers to better disentangle complex cellular heterogeneity, precisely relate genomic sequence to gene
regulation, and facilitate the translation of basic research findings into clinical studies ...

## Key facts

- **NIH application ID:** 10005375
- **Project number:** 5R01GM125301-04
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Mingyao Li
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $372,914
- **Award type:** 5
- **Project period:** 2017-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10005375

## Citation

> US National Institutes of Health, RePORTER application 10005375, Statistical Methods for Single-Cell Transcriptomics (5R01GM125301-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10005375. Licensed CC0.

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