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Related Experiment Video

Updated: Aug 9, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

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

Linhua Wang¹, Chaozhong Liu¹, Zhandong Liu^2,3

¹Graduate School of Biomedical Sciences, Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, USA.

Biorxiv : the Preprint Server for Biology

|February 24, 2023

Summary

This summary is machine-generated.

SEAGAL is a new Python package for analyzing spatial gene expression data. It helps researchers find and visualize spatial gene correlations at single-gene and gene-set levels.

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell and spatial transcriptomics generate rich data for gene association studies.
Traditional co-expression analysis struggles to leverage this spatial information effectively.

Approach:

Introduced Spatial Enrichment Analysis of Gene Associations using L-index (SEAGAL), a Python package.
SEAGAL analyzes spatial transcriptomics data, including gene expression and spatial coordinates.
Enables detection and visualization of spatial gene correlations at single-gene and gene-set levels.

Key Points:

SEAGAL provides easy-to-use tools for analyzing spatial gene correlations.
Outputs include volcano plots and heatmaps for intuitive data interpretation.
Facilitates mining of spatial gene associations from complex transcriptomic datasets.

Conclusions:

SEAGAL offers a comprehensive solution for spatial gene association analysis.
Empowers researchers to explore gene relationships within spatial contexts.
Enhances the utility of high-resolution transcriptomic data.