Quantifying the intra- and inter-species community interaction in a microbiome by dynamic covariance mapping

This is the repository archiving data and scripts for reproducing results presented "Quantifying the intra- and inter-species community interaction in a microbiome by dynamic covariance mapping"

Developed by the http://www.serohijoslab.org/

Dynamic Covariance Mapping (DCM) Framework

The Dynamic Covariance Mapping (DCM) introduces a parameter-free methodology for estimating the community interaction matrix directly from abundance time-series data of microbial community members. This framework is designed to address the complexity of microbial interactions in their natural environments where multiple species interactions and intra-species diversity significantly influence the community dynamics.

To estimate the community interaction matrix and analyze stability changes across different phases using Dynamic Covariance Mapping (DCM), simply execute the following script:src/general_DCM.R

Setup and Configuration

Dependencies and Libraries:

Before beginning the analysis, ensure all necessary libraries are loaded. This can be done by running the script located at: src/visualization/0_config/0_config.R

Importing Data

Barcode Clustering Data Import and Reshaping:

To start analyzing your data, first import and reshape the barcode clustering data into a convenient format using scripts in: src/visualization/1_intersection/*

Analysis and Visualization

Barcode Dynamics

Visualize barcode dynamics using Muller-style area plots and log-line plots. Scripts for these visualizations are found at: src/visualization/2_dynamics/1_plotDynamics.R

Barcode Diversity

Calculate the diversity of barcodes for all samples and plot the results with the following scripts: Calculate Diversity: src/visualization/3_diversity/1_calculateDiversity.R Plot Diversity: src/visualization/3_diversity/2_plotDiversity.R

Clone Dynamics

Determine the dynamics of clones through analysis scripts located in: src/visualization/4_clustering/

16S rRNA Analysis

16S rRNA Gene Sequencing Data Analysis: Analyze and visualize 16S rRNA gene sequencing data to study bacterial compositions. Scripts for these analyses are available at: src/visualization/5_16S/

Co-clustering Analysis

Co-clustering Community and Clone Dynamics: Examine the co-clustering of community dynamics with clone dynamics using scripts in: src/visualization/6_coclustering/

Dynamical Covariance Mapping (DCM) Analysis

DCM Analysis: Perform Differential Condition Matrix analyses to further understand the conditions affecting the microbiome. Relevant scripts are located at: src/visualization/7_DCM/