Spatial Enrichment Analysis of Gene Association using L-index

Cite

This work has been published in Oxford Bioinformatics as:

Linhua Wang, Chaozhong Liu, Yang Gao, Xiang H-F Zhang, Zhandong Liu, Unravelling spatial gene associations with SEAGAL: a Python package for spatial transcriptomics data analysis and visualization, Bioinformatics, Volume 39, Issue 7, July 2023, btad431, https://doi.org/10.1093/bioinformatics/btad431

Install

SEAGAL works for Python3.9, it is advised to create a conda environment with Python3.9 to avoid conflicts of dependencies. Conda installation guide can be found at https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html. After installing conda, run the following command to create a seagal environment:

conda create -n seagal python=3.9

Then install seagal through:

conda activate seagal
conda install -n seagal pip3
pip3 install seagal

Please also install other necessary packages such as jupyter for your research needs. If Error: module seagal not found occurs when importing, please check if echo $PATH includes the path to python3.9 site packages.

Input data format

1. For 10X Visium, Space Ranger Folder with the following contents:

• [Folder]/spatial/tissue_positions_list.csv
• [Folder]/filtered_feature_bc_matrix.h5

2. CSV-format files:

• count.csv - gene expression data frame in Pandas.DataFrame format.
• meta.csv - spot meta data frame, with x, y columns denoting coordinates.

Running SEAGAL with Visium Data

Please read Tutorial1 for instructions.

Running SEAGAL with user-defined marker genes

Please read Tutorial2 for details.

Running SEAGAL with CSV-format Data

Please read Tutorial3 for details.