# DMS/NIGMS 2: Advanced Statistical Methods for Spatially Resolved Transcriptomics Studies

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2022 · $300,000

## Abstract

The recent emergence of various spatially resolved transcriptomic technologies have enabled the study of
spatial transcriptomic landscape across a tissue section or within single cells, catalyzing new discoveries in
many areas of biology. Despite the fast development of spatial transcriptomic technologies, however,
statistical methods for analyzing spatial transcriptomic data are vastly underdeveloped. Analyzing spatial
transcriptomic data faces important statistical challenges that arise from the complexities and unique features
of these data. Here, we propose to address some of these key statistical challenges in this emerging field
through developing a suite of novel statistical methods. Specifically, we will (1) develop Gaussian predictive
process models to model the spatial correlation structure in a computational effective way to rapidly identify
genes with spatial expression patterns; (2) develop integrative methods to incorporate reference single cell
RNA sequencing data along with spatial correlation structure in spatial transcriptomics to enable accurate
deconvolution of cell types on the tissue; (3) develop Potts models to perform tissue segmentation and detect
tissue regions and microenvironment in a de novo fashion. We will develop, distribute, and support user-friendly
open-source software implementing the proposed methods and disseminate them to the scientific
community. We will perform rigorous and comprehensive simulations and apply our methods to analyze
multiple public spatial transcriptomics data that are collected from different technical platforms and are of
different scales. We will also perform an in-depth analysis with supplemental experiments on the spatial
transcriptomics data being collected as part of the study of the role of dysregulated stem cell biology in breast
cancer disparities in African American women.

## Key facts

- **NIH application ID:** 10493427
- **Project number:** 5R01GM144960-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Xiang Zhou
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $300,000
- **Award type:** 5
- **Project period:** 2021-09-24 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10493427

## Citation

> US National Institutes of Health, RePORTER application 10493427, DMS/NIGMS 2: Advanced Statistical Methods for Spatially Resolved Transcriptomics Studies (5R01GM144960-02). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/10493427. Licensed CC0.

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