🧬 GZSper: Illuminating Single-Cell Landscapes with GWAS Insights

GZSper (GWAS Z-score to Single-cell Phenotypes for Expression Research) is a Python toolkit designed to bridge the gap between GWAS and single-cell RNA sequencing analysis. By ingeniously translating GWAS Z-scores into single-cell phenotypes, GZSper empowers researchers to unravel the cellular intricacies of complex traits and diseases with unprecedented resolution.

✨ Key Features

• 🔗 Integration of GWAS Z-scores with scRNA-seq data
• 🧬 Translation of genetic associations to single-cell phenotypes
• 🎨 Visualizations of GWAS-informed single-cell landscapes
• 🚀 Processing of large-scale genomic and transcriptomic datasets
• 🔍 Exploration of trait-associated gene expression patterns at single-cell resolution
• 📊 Robust statistical analysis and data normalization
• 🔄 Adaptable to a wide spectrum of traits and diseases studied in GWAS

🛠️ Installation

git clone https://github.com/Benjamin-JHou/GZSper.git
cd GZSper
pip install -r requirements.txt

🚀 Usage

Embark on your GZSper journey with these simple steps:

1. 📥 Data Fusion

adata, zscore_df = load_data(adata_path, zscore_path)

Problem Solved: Seamlessly merges single-cell RNA-seq data with GWAS Z-scores, laying the foundation for integrated analysis.

2. 🧮 Phenotype Inference

adata = calculate_phenotype_score(adata, zscore_df)

Problem Solved: Translates GWAS signals into meaningful single-cell phenotypes, revealing cells most influenced by trait-associated genetic variations.

3. 🌈 Phenotypic Landscape Visualization

plot_umap(adata, color='cell_type', title='Cellular Diversity', save_path='cell_types_umap.png')
plot_umap(adata, color='gwas_phenotype', title='GWAS-Informed Cellular Phenotype', save_path='gwas_phenotype_umap.png')

Problem Solved: Crafts intuitive visualizations of cell type distributions and GWAS-informed phenotypes, unveiling trait-relevant cellular populations.

4. 🎻 Phenotypic Distribution Analysis

plot_violin(adata, groupby='cell_type', values='gwas_phenotype', title='GWAS-Informed Phenotype by Cell Type', save_path='gwas_phenotype_violin.png')

Problem Solved: Compares GWAS-informed phenotypes across cell types, highlighting cellular populations most relevant to the studied trait or disease.

5. 🔵 Gene-Phenotype Interplay

plot_dotplot(adata, groupby='cell_type', var_names=top_genes_in_adata, title='Expression of Top GWAS-Identified Genes Across Cell Types', save_path='top_genes_dotplot.png')

Problem Solved: Visualizes expression patterns of key GWAS-identified genes across cell types, revealing cell-specific contributions to complex traits and diseases.

📊 Example Output

🧪 Versatility Across Traits and Diseases

GZSper's adaptability spans the entire spectrum of GWAS-studied phenomena. By simply swapping GWAS Z-scores and single-cell datasets, researchers can illuminate cellular mechanisms underlying:

• Complex diseases (e.g., cardiovascular disorders, autoimmune conditions, neuropsychiatric illnesses)
• Quantitative traits (e.g., anthropometric measures, physiological parameters)
• Molecular phenotypes (e.g., gene expression variability, metabolomic profiles)

📄 License

GZSper is released under the GNU General Public License v3.0, which allows you to:

• Use the code for any purpose, including research and commercial projects.
• Modify and distribute the code, provided that derivative works are also released under the GPL-3.0 license.

For more information about the GPL-3.0 license and its permissions and restrictions, please refer to the official GNU GPL-3.0 page.

📚 Citation

If GZSper empowers your research, please cite:

Junyu Zhou (2024). GZSper: Illuminating Single-Cell Landscapes with GWAS Insights. GitHub. https://github.com/Benjamin-JHou/GZSper

🌟 Star Us!

If GZSper sparks your scientific curiosity, star our repository to help fellow researchers discover this powerful tool!