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bioRxiv · 10.1101/2023.02.13.528331

Unraveling Spatial Gene Associations with SEAGAL: a Python Package for Spatial Transcriptomics Data Analysis and Visualization

Wang, L.·Liu, C.·Liu, Z.
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Abstract

SummaryIn the era where transcriptome profiling moves towards 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 data sets with gene expression and the aligned spatial coordinates as input. It allows for analyzing and visualizing spatial correlations at both single-gene and gene-set levels. 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. Availability and ImplementationThe Python package SEAGAL can be installed using pip: https://pypi.org/project/seagal/. The source code and step-by-step tutorials are available at: https://github.com/linhuawang/SEAGAL. Contactlinhuaw@bcm.edu

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Wang, L., Liu, C., Liu, Z.. 2023-02-13. Unraveling Spatial Gene Associations with SEAGAL: a Python Package for Spatial Transcriptomics Data Analysis and Visualization. https://doi.org/10.1101/2023.02.13.528331

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