Mucosal Pan-Omics: Differential Abundance and Network Topology Analysis of IBD Microbiome

This repository contains the computational pipeline, statistical analysis, and visualization scripts for investigating the mucosal microbiome in Crohn's Disease (CD) using 16S rRNA amplicon sequencing data.

Our analysis identifies key compositional shifts and theoretical metabolic master regulators driving disease-associated dysbiosis, serving as a framework for mucosal pan-omics.

Overview and Methodology

The pipeline takes raw FASTQ files through a rigorous bioinformatics workflow:

1. Preprocessing & Denoising: Amplicon Sequence Variants (ASVs) are inferred using the DADA2 pipeline within the QIIME 2 ecosystem.
2. Differential Abundance Analysis: Robust estimation of logarithmic fold changes (log2FC) and exact testing of ASV abundance between Healthy vs. CD states using DESeq2 (via phyloseq).
3. Co-occurrence Network Topology: Construction of microbial co-occurrence networks utilizing Spearman/SparCC-style approximations on CLR-transformed relative abundances to identify keystone taxa.
4. Publication Visualization: Algorithmic generation of high-fidelity, Nature Publishing Group (NPG)-styled vector graphics summarizing ecological characteristics.

Repository Structure

mucosal-pan-omics/
├── data/                  # Ignored from version control due to file size constraints
│   ├── 01_raw/            # Raw multiplexed FASTQ reads
│   ├── 02_interim/        # Demultiplexed QIIME artifacts (.qza/.qzv)
│   └── 03_processed/      # DADA2 output feature tables and sequences
├── docs/                  # Experimental protocols and methodology definitions
├── envs/                  # Conda environment specifications (R and QIIME 2)
├── notebooks/             # Exploratory Python and RMarkdown notebooks
├── results/               
│   ├── figures/           # Generated statistical plots and vector graphics
│   └── tables/            # Differential abundance & topological dataframes
└── scripts/               # Modular executable pipeline scripts (bash/R)

Analytical Results

1. Alpha Diversity

Analysis of mucosal ecosystem richness and evenness across Healthy and CD states.

2. Beta Diversity (PCoA - Bray-Curtis)

Principal Coordinate Analysis (PCoA) highlighting the centroid shift in microbial topologies.

3. Differential Abundance (Volcano Plot)

Identification of significantly enriched and depleted ASVs ($P_{adj} < 0.05$, $|Log_2FC| > 1$).

4. Expression Landscape (MA Plot)

Base mean dispersion vs. fold change variance tracking.

5. Differential Significance Clustered Heatmap

Z-score relative abundance distributions grouped by condition phenotypes.

6. Core Pathogenic ASV Distributions

Direct normalized variance tracking of the top 4 biologically significant ASVs driving the CD phenotype.

Reproducibility

To regenerate these analyses from the pre-processed BIOM tables:

1. Install Dependencies:

conda env create -f envs/environment_r.yml
conda activate mucosal-r-env

2. Execute Analysis Script: Calculates DESeq2 models, extracts topology algorithms, and generates statistical summary tables.

Rscript scripts/03_run_DE.R
Rscript scripts/04_network_MRA.R

3. Generate Publication Graphics: Outputs the precise Nature-themed graphics shown above.

Rscript scripts/05_publication_figures.R

References

• Bolyen, E., et al. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology, 37(8), 852-857.
• Callahan, B. J., et al. (2016). DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods, 13(7), 581-583.
• Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550.
• McMurdie, P. J., & Holmes, S. (2013). phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE, 8(4), e61217.
• Friedman, J., & Alm, E. J. (2012). Inferring correlation networks from genomic survey data. PLoS Computational Biology, 8(9), e1002687.

License

This project is licensed under the MIT License - see the LICENSE file for details.