# Statistical methods for spatial RNA sequencing experiments

> **NIH NIH R01** · UNIVERSITY OF WISCONSIN-MADISON · 2022 · $341,850

## Abstract

Abstract
Spatial RNA-sequencing has emerged as a revolutionary tool that allows us to address scientific questions
that were elusive just a few years ago. Specifically, the spatial RNA-sequencing technology has the potential
to revolutionize studies of tissue structure and function in health and disease. However, much of the potential
has yet to be realized as statistical methods to analyze spatial RNA-seq data are lacking. For many types of
analyses, the methods currently in use obscure and, in some cases, distort biological signals. A number of
statistical and computational challenges must be addressed to prevent inaccurate conclusions, and to
optimize novel discovery. This proposal addresses those challenges. In particular, while the technology is
powerful, it is not without error; and considerable contamination exists in spatial RNA-seq data. We propose
methods to remove this contamination and thereby ensure robust and accurate downstream inference. We
also propose statistical methods to adjust for technical variability induced by differences in sequencing depth.
By reducing technical variability, these methods will improve the power with which signals of interest can be
studied. Finally, we propose methods for characterizing changes in the dependence structure of sets of genes.
These types of methods are required to improve our understanding of how coordinated changes in genes
affect tissue structure and function in health and disease. Taken together, successful completion of this project
will help to ensure that maximal information is obtained from powerful spatial RNA-seq experiments.

## Key facts

- **NIH application ID:** 10490388
- **Project number:** 5R01GM102756-10
- **Recipient organization:** UNIVERSITY OF WISCONSIN-MADISON
- **Principal Investigator:** Christina Kendziorski
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $341,850
- **Award type:** 5
- **Project period:** 2012-08-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10490388

## Citation

> US National Institutes of Health, RePORTER application 10490388, Statistical methods for spatial RNA sequencing experiments (5R01GM102756-10). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10490388. Licensed CC0.

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