This repository contains a comprehensive spatial transcriptomics analysis pipeline for investigating African swine fever virus (ASFV) infection in pig lung and spleen tissues. The analysis reveals dynamic immune and molecular landscape changes between early and late infection stages through high-resolution spatial profiling.
- Spatial transcriptomics data processing from raw imaging to expression matrices
- Multi-tissue analysis covering both lung and spleen samples
- Temporal dynamics comparing pre-infection, early, and late infection stages
- Cell-type annotation and spatial clustering
- Comprehensive visualization tools for spatial gene expression patterns
The analysis pipeline is organized into modular Jupyter notebooks, each handling a specific aspect of the spatial omics workflow:
Notebook: decode_innereye.ipynb
Decodes raw spatial transcriptomics imaging data and extracts spatial gene expression matrices for downstream analysis.
Key steps:
- Raw image processing
- Spot detection and quality control
- Gene expression matrix generation
Notebook: downstream_analysis.ipynb
Performs comprehensive data analysis including normalization, clustering, cell-type annotation, and integrated visualization of ASFV-infected and control samples.
Key steps:
- Data normalization and quality filtering
- Dimensionality reduction (PCA, UMAP)
- Spatial clustering and cell-type identification
- Differential expression analysis
- Integrated multi-sample visualization
Notebook: scatterplot.ipynb
Generates customized scatter plots for spatial feature visualization and regional expression patterns.
Key steps:
- Spatial coordinate mapping
- Gene expression overlay
- Custom color schemes and annotations
Notebook: spleen_analysis.ipynb
Complete workflow for analyzing ASFV-induced molecular and spatial alterations specifically in spleen tissues.
Key steps:
- Spleen-specific preprocessing
- Immune cell population analysis
- Spatial organization of immune responses
- Infection stage comparison
- Python 3.8+
- Jupyter Notebook/Lab
- Key dependencies: scanpy, squidpy, numpy, pandas, matplotlib, seaborn
git clone https://github.com/yourusername/ASFV_spatialomics_analysis.git
cd ASFV_spatialomics_analysis
pip install -r requirements.txtEach notebook can be run independently while maintaining consistent data structures. Start with decode_innereye.ipynb for initial data processing, then proceed to downstream analysis notebooks based on your research questions.
This project is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0) - see the LICENSE file for details.
For questions or collaborations, please open an issue or contact the repository maintainer.