Unravelling spatial gene associations with SEAGAL: a Python package for spatial transcriptomics data analysis and visualization
Year: 2023
Type: software-paper
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.
Source: Bioinformatics
Institutions: Baylor College of Medicine, Texas Children's Hospital
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,618
Awards: Translational Research in Breast Cancer, Harnessing neutrophils to improve the efficacy of immune checkpoint inhibitors in breast cancer, Center for functional analysis of human UDN gene homologs in Drosophila and zebrafish, Baylor College of Medicine Intellectual and Developmental Disabilities Research Center
OpenAlex ID: W4383998869