Unravelling Spatial Gene Associations With SEAGAL: A Python Package for Spatial Transcriptomics Data Analysis and Visualization

Authors

Linhua Wang
Chaozhong Liu
Yang Gao
Xiang H-F Zhang
Zhandong Liu

Publication Date

7-1-2023

Journal

Bioinformatics

DOI

10.1093/bioinformatics/btad431

PMID

37436699

PMCID

PMC10363022

PubMedCentral® Posted Date

7-12-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Computational Biology, Transcriptome, Gene Expression Profiling, Software, Data Analysis

Abstract

SUMMARY: 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.

AVAILABILITY AND IMPLEMENTATION: The 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.