Analysis of Cellular Heterogeneity and Community Connectivity Using Spatial Transcriptomics Data

Thomas M. Menino Convention & Exhibition Center 

High-throughput spatial transcriptomics (HST) is a powerful experimental technology that allows for profiling gene expression in tissue samples at or near the single-cell level, while retaining the spatial location of each sequencing unit. In the HST data analysis, the identification of the tissue architecture, which reflects biological cell types or states, is routinely implemented using either spatial clustering or label transfer approaches. However, cellular heterogeneity differences and similarities between tissue regions are often ignored, although they can elucidate cellular dynamics in important settings such as the tumor microenvironment. To address these limitations, we developed a statistical framework for the analysis of cellular heterogeneity and community connectivity analysis using HST data, which facilitates our understanding of the heterogeneity of each tissue region and relationships between different tissue regions. We will illustrate this framework through simulation studies and real data applications, including 10X Visium data of melanoma brain metastases and invasive ductal carcinoma.

spatial transcriptomics

community connectivity

cellular heterogeneity

network analysis 

Section on Statistics in Genomics and Genetics