Unravelling spatial gene associations with SEAGAL: a Python package for spatial transcriptomics data analysis and visualization

In the era where transcriptome profiling moves toward single-cell and spatial resolutions, the traditional co-expression analysis lacks the power to fully utilize such rich information to unravel spatial gene associations. Here, we present a Python package called Spatial Enrichment Analysis of Gene Associations using L-index (SEAGAL) to detect and visualize spatial gene correlations at both single-gene and gene-set levels. Our package takes spatial transcriptomics datasets with gene expression and the aligned spatial coordinates as input. It allows for analyzing and visualizing genes' spatial correlations and cell types' colocalization within the precise spatial context. The output could be visualized as volcano plots and heatmaps with a few lines of code, thus providing an easy-yet-comprehensive tool for mining spatial gene associations.

Automated summary

In the era of single-cell and spatial transcriptome profiling, traditional co-expression analysis is insufficient for exploring spatial gene associations. The SEAGAL Python package presented in this study allows for detecting and visualizing spatial gene correlations at both single-gene and gene-set levels. By inputting spatial transcriptomics datasets containing gene expression and spatial coordinates, SEAGAL enables the analysis and visualization of both gene-gene and cell-type colocalization within the precise spatial context. The output can be easily transformed into volcano plots and heatmaps, providing a user-friendly tool for mining spatial gene associations.