Accounting for Spatial Correlation in Graphical Analysis of Spatial Transcriptomics Data

Music City Center 

Understanding how gene networks vary across spatial regions, conditions, and cell types is essential for decoding tissue organization and disease mechanisms. Spatial Transcriptomics (ST) technologies provide gene expression data with spatial context, but estimating gene-gene correlations remains challenging due to spatial autocorrelation among cells, which can produce spurious associations and obscure true biological relationships. Existing methods often ignore spatial structure or lack scalability for single-cell resolution data. In this project, we present a flexible and efficient approach for estimating gene correlation networks from single-cell ST data by first removing spatial correlation. This preprocessing step enables the use of standard co-expression and network inference methods while avoiding spatial confounding. Our approach improves reproducibility, reveals biologically meaningful associations, and facilitates robust comparisons of gene networks across tissue regions, cell types, and experimental conditions.

Spatial transcriptomics

co-expression

Splines 

Section on Statistics in Genomics and Genetics