Language
English
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
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.
Keywords
Computational Biology, Transcriptome, Gene Expression Profiling, Software, Data Analysis
Published Open-Access
yes
Recommended Citation
Xiang H-F Zhang and Zhandong Liu, "Unravelling Spatial Gene Associations With SEAGAL: A Python Package for Spatial Transcriptomics Data Analysis and Visualization" (2023). Faculty, Staff and Students Publications. 2079.
https://digitalcommons.library.tmc.edu/baylor_docs/2079