OrganInteraction

This project is based on our study: Atlas of the Human Organ Interaction Network.

Overview

This study provides a comprehensive atlas of human organ interactions, exploring the dynamic relationships between 9 major organs and their implications in multi-organ diseases. By utilizing multi-omics and population-level phenotypic data, we reveal key genetic loci and molecular mediators influencing organ interactions and establish its predictive value for various diseases.

Highlights

• Comprehensive Atlas: Maps out the interactions between 9 human organs.
• Age and Gender Effects: The perturbation of organ interactions increases with age, with more pronounced effects in males than females.
• Genetic Insights: Identifies 143 genetic loci regulating multi-organ interactions, with 24 loci co-localizing with common diseases.
• Inflammatory and Metabolic Pathways: Enrichment and mediation analyses suggest that inflammatory and metabolic factors are critical mediators of organ interactions.
• Predictive Value: Organ interaction perturbations serve as a new phenotype with predictive potential for major diseases.

Supporting Packages

This repository contains supporting code for the study and utilizes the following major packages:

R Packages:

• ggplot2: For data visualization and plotting.
• dplyr: For data manipulation and transformation.
• tidyr: For tidying data.
• mediation: For performing mediation analysis.
• MendelianRandomization: For Mendelian Randomization analysis.

Python Packages:

• hail: For process gwas data
• numpy: For numerical computations.
• pandas: For data manipulation and analysis.
• scipy: For scientific computing and statistical analysis.
• statsmodels: For statistical modeling and regression analysis.
• matplotlib: For generating plots and visualizations.

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Installation and Setup

1. Clone the repository:

   git clone https://github.com/FD-ImageLab/OrganInteraction.git
   cd organ-interaction-atlas

2. Install required dependencies:

   For Python: for example

   pip install hail

   For R: for example:

   install.packages("forestploter")

3. Run the analysis:

   • For data preprocessing, correlation analysis, and Mendelian Randomization, use the respective scripts located under the scripts directory.