GRIDGENE: Guided Region Identification based on Density of GENEs—a transcript density-based approach to characterize tissues by spatial transcriptomics
TL;DR
GRIDGENE (Guided Region Identification based on Density of GENEs), an approach for defining regions of interest based on transcript density, broadens the analytical toolkit for spatial omics, enabling both cell-free and cell-based insights.
Abstract
Abstract Motivation Spatial omics brought unprecedented power to study biological processes within tissues while preserving spatial context and morphology. Most spatial proteomics and transcriptomics analysis methods are cell-centric, relying on cell segmentation to identify and characterize individual cells before downstream tasks. However, certain biological questions may be better addressed using cell-free approaches, which also eliminate unnecessary computations when cell segmentation is not essential. Results To address this need, we developed GRIDGENE (Guided Region Identification based on Density of GENEs), an approach for defining regions of interest based on transcript density. GRIDGENE enables the identification of biologically relevant tissue compartments, including interfaces between regions, phenotype-enriched areas, and zones defined by specific gene signatures, supporting analyses such as pathway enrichment. We demonstrated the utility of GRIDGENE by applying it to spatial transcriptomics data from CosMx and Xenium platforms in colorectal cancer samples. By bypassing cell segmentation, our approach enables flexible analysis of spatial omics data, supporting the study of biological processes across diverse tissue structures and microenvironments. Nevertheless, GRIDGENE can be easily integrated with cell segmentation strategies for complementary analyses. GRIDGENE thus broadens the analytical toolkit for spatial omics, enabling both cell-free and cell-based insights. Availability and implementation The GRIDGENE pipeline is fully available and ready to use at https://github.com/deMirandaLab/GRIDGENE. The repository also contains all the necessary code to replicate the methods comparison, along with detailed Jupyter notebooks for easy understanding and implementation.