Centromere Clustering Analysis

This repository accompanies the manuscript "Simulation and Quantitative Analysis of Spatial Centromere Distribution Patterns" by Adib Keikhosravi, Krishnendu Guin, Gianluca Pegoraro, and Tom Misteli. It provides tools and scripts for analyzing centromere clustering patterns using high-throughput imaging data and spatial distribution modeling.

Overview

Centromeres exhibit non-random spatial distribution in the nucleus. Understanding their clustering is critical for studying chromosome behavior, nuclear organization, and associated functional processes. This repository includes:

• Clustering Metrics: Tools for analyzing centromere clustering using Ripley's K score, Moran's I, modularity, mean nearest neighbor distance (MNND), etc.
• Synthetic Data Generation: Scripts for generating simulated centromere distribution patterns.
• Modeling Approaches: Radial and Gaussian-based models for simulating centromere spatial organization.
• Visualization Tools: Methods for generating 2D and 3D visualizations of centromere distributions.

Installation

To install the required dependencies, follow these steps:

1. Clone this repository:

   git clone https://github.com/CBIIT/centromere_clustering_analysis.git
   cd centromere_clustering_analysis

2. Install dependencies using the provided genome.yml file:

   conda env create -f genome.yml
   conda activate genome