This repository is part of a summer project with Dr. Emily Davenport from Pennsylvania State University, where we developed a model to study disease dynamics in individuals. We utilized the TwinsUK dataset for this project, which includes 16S sequencing data of bacterial communities.
This R Markdown file contains the code for generating a SPIEC-EASI network for bacterial communities using 16S sequencing data. It includes a generic workflow that can be used for network construction based on sequencing data.
- Key Features:
- Network generation using SPIEC-EASI (Sparse Inverse Covariance Estimation for Ecological Association Inference).
- Designed for 16S sequencing data of bacterial communities.
This R Markdown file is still a work in progress. It contains algorithms to calculate and visualize important network statistics for the generated SPIEC-EASI network. Key metrics include:
- Modularity
- Transitivity
- Centrality
These statistics help in understanding the structure and relationships within the bacterial communities.
- Key Features:
- Calculates various network statistics.
- Visualizes key properties such as modularity, transitivity, and centrality.
This file contains the network visualization generated by the SPIEC-EASI modeling technique. It provides a visual representation of the bacterial community network based on the analysis.
- Key Features:
- Visual representation of the bacterial network.
- Shows connections between various bacterial species based on ecological relationships inferred from the data.
