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

University Of Michigan At Ann Arbor, Ann Arbor MI

Investigators

Linked publications & trials

Abstract

Project Summary 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. In the funded parent award, we proposed to develop a suite of novel statistical methods and computational tools (1) to model the spatial correlation structure in a computational effective way to rapidly identify genes with spatial expression patterns; (2) 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; and (3) to perform tissue segmentation and detect tissue regions and microenvironment in a de novo fashion. In this proposal to administrative supplements for equipment, we propose to use a single unit of GPU node to provide the necessary GPU computation support for this project, solve the computational bottle neck created by the increasingly large spatially resolved transcriptomics studies, and enhance the usability and applicability of the proposed statistical methods and computational tools and hence their impact in this emerging research field of spatially resolved transcriptomics studies. The proposed administrative supplement will ensure the successful development of the proposed statistical methods and computational tools in the parent award and the high impact of the developed methods to the scientific community.