Accounting for Spatial Correlation in Network Analysis of Spatial Transcriptomics Data

Thomas M. Menino Convention & Exhibition Center 

Co-expression analysis is key for understanding disease mechanisms and gene regulatory and functional relationships.In spatial transcriptomics, layered sources of variation such as spatial gradients, microenvironmental structure, and technical effects can induce correlations between genes that arise from shared location rather than true biological regulation. To address this, we propose SpaceDecorr, a method that adjusts gene expression for technical artifacts and spatial dependencies by modeling each gene independently using a Negative Binomial Generalized Additive Model (NB-GAM) with spatial splines. Co-expression is then estimated from the Pearson residuals, yielding decorrelated expression values suitable for downstream analysis. This method targets cell-intrinsic coordination, rather than clustering genes by shared spatial patterns, and supports multi-sample analysis through independent per-sample adjustment. Across simulations and real datasets, it consistently reduces false-positive correlations and improves the functional coherence of co-expression modules.

Spatial transcriptomics

gene co-expression

spatial autocorrelation

multi-sample analysis 

Section on Statistics in Genomics and Genetics