Controlling False Discoveries after Clustering via Data Splitting for Spatial Marker Detection

Thomas M. Menino Convention & Exhibition Center 

Spatial omics technologies are transforming biomedical research by enabling genome-wide measurement of molecular activity while preserving the spatial context within tissues. These advances create unprecedented opportunities to uncover cell–cell interactions, tissue organization, and disease mechanisms. A crucial step in realizing this potential is identifying spatial domain markers, which are essential for defining tissue architecture and understanding disease progression. However, using the same data for both clustering and marker detection creates the problem of "double dipping", which can lead to inflated false discoveries, particularly when domain boundaries are poorly defined. To address this challenge, we develop SpaDS and SpaMDS, data splitting based approaches for differential expression testing after clustering for spatial omics, enabling robust spatial domain marker discovery with controlled false discovery rate and high power. Through extensive simulations and analyses of spatial omics datasets, we demonstrate that the data-splitting methods are easy to implement, adaptable to existing spatial omics data analysis pipelines, and often outperform other approaches.

Statistical genomics

Spatial transcriptomics

data splitting

FDR control 

Section on Statistics in Genomics and Genetics