# Gene set analysis of single cell genomics

> **NIH NIH R35** · DARTMOUTH COLLEGE · 2024 · $410,000

## Abstract

PROJECT SUMMARY
Advances in single cell assays have enabled the genome-wide measurement of DNA sequence, RNA
expression, chromatin accessibility and protein abundance for tens-of-thousands of cells isolated from
a single tissue sample. Methods that capture or infer spatial or temporal information provide additional
contextual information to create a detailed, cell-level picture of gene activity and function. These meth-
ods give researchers a powerful tool for identifying the cell types in the analyzed tissue, the phenotype
of those cells and the network of cell-cell interactions that control tissue structure and function. Although
these techniques have revolutionized the study of complex tissues, the significant sparsity and noise
of single cell measurements means that statistical analysis is typically performed at the level of large
groups or clusters of cells. Although a cluster-based analysis can mitigate sparsity and noise, the re-
sults reflect the state of the average cell in the cluster, which may be quite dissimilar from many cells
in heterogeneous populations. To fully realize the potential of single cell profiling, bioinformatics meth-
ods are needed that can accurately characterize individual cells rather than cell groups. One promising
approach for generating cell-level estimates is gene set testing or pathway analysis, which can more
effectively capture the state of individual cells by combining the measurements for all genes in a biolog-
ical pathway. Unfortunately, statistical and biological differences between single cell and bulk genomic
data make it challenging to use existing gene set testing methods, that were developed for bulk tissue,
on single cell data. To address this limitation, we will create innovative algorithms for cell-level gene set
testing and will use these techniques to support the estimation of cell type, phenotype and interaction
potential. The translational application of these methods to study immune cell signaling within the tu-
mor microenvironment will help validate our approach and provide important insights into the immune
response to cancer.

## Key facts

- **NIH application ID:** 10899480
- **Project number:** 5R35GM146586-03
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** Hildreth Frost
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $410,000
- **Award type:** 5
- **Project period:** 2022-09-22 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10899480

## Citation

> US National Institutes of Health, RePORTER application 10899480, Gene set analysis of single cell genomics (5R35GM146586-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10899480. Licensed CC0.

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