Single-Cell Transcriptome Analysis for Pyroptosis in COVID-19

Project Overview

This repository hosts the R scripts and workflows used in our study on pyroptosis in COVID-19, as detailed in our published paper View Article. These scripts cover data preprocessing, analysis, and visualization techniques that underpin our findings on the role of pyroptosis in COVID-19 severity.

Tools and Techniques

Data Handling and Analysis

• Dimensionality Reduction: Utilized Seurat for principal component analysis (PCA) and uniform manifold approximation and projection (UMAP).
• Data Integration: Employed Harmony to integrate datasets from multiple studies to minimize batch effects and enhance comparability.

Visualization

• Graphical Representations: Leveraged ggplot2 for generating expressive data visualizations.
• Plot Arrangements: Used patchwork to effectively arrange multiple plots in a cohesive layout.

Statistical and Enrichment Analysis

• Statistical Testing: Applied functions from Seurat and dplyr for subsetting, normalizing, and statistically analyzing single-cell datasets.
• Enrichment Analysis: Conducted gene set enrichment analysis using fgsea to identify crucial biological pathways involved in the condition.

Repository Structure

• Code/: Contains all R scripts for analysis.
  • calculate_pyroptosis.r: Scripts for calculating pyroptosis scores.
  • figure*.r: Scripts for generating figures illustrating the analysis.
  • prep_*.r: Scripts for data preprocessing from various data sources.
  • reply_to_reviewers/: Responses and revisions based on peer review.
• data/: Directory for raw and processed data files (as referenced in the scripts).
• results/: Output directory for results including statistical summaries and figures.

Getting Started

To run these analyses:

1. Ensure R and all required packages are installed.
2. Clone this repository.
3. Execute scripts within the Code/ directory in sequential order as listed.

Contributions

Contributions are welcome. Please fork the repository and submit pull requests with any enhancements. For major changes, open an issue first to discuss what you would like to change.

Citation

If you utilize this repository, please cite the associated publication:

Xu, Q., Yang, Y., Zhang, X., & Cai, J. J. (2022). Association of pyroptosis and severeness of COVID-19 as revealed by integrated single-cell transcriptome data analysis. ImmunoInformatics, 6, 100013.

License

This project is open-sourced under the MIT License.