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Related Experiment Video

Updated: Jul 23, 2025

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Spatially Compact Arrangement of Larval Zebrafish Sections for Spatial Transcriptomic Analysis

Published on: May 16, 2025

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

Linhua Wang¹, Chaozhong Liu¹, Yang Gao²

¹Graduate School of Biomedical Sciences, Program in Quantitative and Computational Biosciences, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030, United States.

Bioinformatics (Oxford, England)

|July 12, 2023

Summary

This summary is machine-generated.

SEAGAL is a new Python package that analyzes spatial gene correlations in single-cell and spatial transcriptomics data. It helps researchers visualize gene associations and cell colocalization within their precise spatial context.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Traditional co-expression analysis struggles with high-resolution spatial transcriptomics data.
Unraveling spatial gene associations requires advanced analytical tools.

Purpose of the Study:

Introduce SEAGAL, a Python package for spatial gene association analysis.
Enable detection and visualization of spatial gene correlations at single-gene and gene-set levels.
Facilitate analysis of cell type colocalization within spatial context.

Main Methods:

SEAGAL accepts spatial transcriptomics data with gene expression and spatial coordinates.
The package utilizes the L-index for detecting spatial gene correlations.
Outputs include volcano plots and heatmaps for visualization.

Main Results:

SEAGAL effectively detects and visualizes spatial gene correlations.
The package allows for gene-set level analysis of spatial associations.
Enables visualization of cell type colocalization in spatial context.

Conclusions:

SEAGAL provides an easy-to-use yet comprehensive tool for spatial gene association mining.
The package enhances the analysis of single-cell and spatial transcriptomics data.
Facilitates discovery of spatial relationships in gene expression data.