Group 4 – DNA Methylation Analysis Project

Table of Contents

• Project Overview
• Assigned Parameters
• Tools and Technologies
• Repository Structure
• Workflow Summary
• Resources and References
• License
• Contact

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Project Overview

This repository contains the DNA methylation analysis developed by Group 4 for the DNA/RNA Dynamics course (Module 2, Prof. Francesco Ravaioli), MSc in Bioinformatics – University of Bologna.

The project investigates genome-wide CpG methylation patterns using data from the Illumina HumanMethylation450K BeadChip, with the goal of identifying methylation changes associated with disease. The analysis, entirely performed in R using Bioconductor packages, integrates statistical testing, quality control, and biological interpretation.

Designed as both a scientific case study and an educational exercise, the repository provides a reproducible framework for exploring epigenetic variation and its potential role in disease mechanisms.

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Assigned Parameters

Parameter	Value
Group ID	4
Probe Address	44666390
Detection p-value cut-off	0.01
Normalization method	preprocessFunnorm

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Tools and Technologies

• Language: R
• Platform: Illumina HumanMethylation450K
• Packages: minfi, BiocManager, gplots, factoextra, qqman, genefilter, ggplot2, ggpubr, cluster, factoMineR

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Repository Structure

/Input_data/ — Raw Data & Metadata

• *.idat (16 files): Red and green channel raw data files containing probe intensities per sample.
• SampleSheet_Report_II.csv: Sample metadata including IDs, experimental groups, batch information, and corresponding .idat file references.

/scripts/ — Main Pipeline & Report

• DRD_project_script.R: Standalone R script with the core analysis code, separated from the RMarkdown report. Useful for re-running the analysis or integrating it into other workflows.
• DRD_project_final.html: HTML version of the report generated from the .Rmd file. Allows for interactive viewing in a web browser.
• DRD_project_final.Rmd: RMarkdown source file containing the full project report, including code, results, and explanations. Can be compiled into HTML or PDF format.
• DRD_project_final.pdf: PDF version of the final report. Useful for printing or sharing as a static document.

/outputs/ — Results & Visualizations

QC & Intensity

• beta_m_values.png: Distribution of Beta and M values (CTRL vs DIS).
• qc_plot.png: Raw MSet data distribution before normalization.
• Raw_normalised_beta.png: Comparison of raw vs normalized values (mean, SD, boxplot).
• controlStripPlot.png: Background control plot using negative probes.
• df_address.pdf: Sample plate addresses for QC.
• df_failed.pdf: Summary of failed or excluded samples.

PCA

• PCA_batch.png: PCA plot colored by batch.
• PCA_groups.png: PCA plot colored by experimental group (e.g., CTRL/DIS).
• PCA_sex.png: PCA plot colored by sex (Female/Male).
• scree_plot.png: Scree plot showing explained variance per principal component.

Clustering

• Average_linkage_heatmap.png: Heatmap with average linkage clustering.
• Complete_linkage_heatmap.png: Heatmap with complete linkage clustering.
• Single_linkage_heatmap.png: Heatmap with single linkage clustering.

Statistics

• Histogram_pvalues.png: Histogram of raw p-values (t-tests).
• Boxplot_corrections.png: Comparison of raw and adjusted p-values (BH, Bonferroni).
• manhattan_plot.png: Manhattan plot of –log₁₀ p-values across genomic positions.
• volcano_plot.png: Volcano plot of ΔBeta vs –log₁₀ p-value (effect size vs significance).

/diagram_workflow/ — Workflow Overview

• workflow.png: Diagram illustrating the main steps of the DNA methylation analysis pipeline, from raw data input to final output and visualization.

/report_pipeline/ — Report Guidelines

• 20250605_Report_pipeline_FINAL.pdf: Document containing the professor’s official instructions and structure for writing the final project report.

DNAmethylation_analysis_manual.pdf: Manual (LaTeX) including R usage guide, function descriptions, package references, and pipeline instructions.

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Workflow Summary

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Resources and References

• BiocManager
  Used to install and manage packages from the Bioconductor project.
  Vignette: BiocManager Vignette
  Citation:
  Shepherd L. (2024). BiocManager: Access the Bioconductor Project Package Repository. R package version 1.30.22.

• minfi
  Core package for analyzing Illumina 450K/EPIC methylation arrays. Includes preprocessing, QC, DMP analysis, and visualization tools.
  Vignette: minfi Vignette
  Citation: Aryee, M. J., et al. (2014). Minfi: a flexible and comprehensive Bioconductor package for the analysis of Infinium DNA methylation microarrays. Bioinformatics, 30(10), 1363–1369. DOI: 10.1093/bioinformatics/btu049

• factoextra
  Simplifies extraction and visualization of multivariate analyses (e.g., PCA).
  Documentation: factoextra Website
  Citation:
  Kassambara, A. (2020). factoextra: Extract and Visualize the Results of Multivariate Data Analyses. R package version 1.0.7.

• qqman
  Produces Manhattan and Q-Q plots, primarily used in GWAS and EWAS visualizations.
  Vignette: qqman Vignette
  Citation:
  Turner, S. D. (2014). qqman: an R package for visualizing GWAS results using Q-Q and Manhattan plots. bioRxiv. DOI: [10.1101/005165] (https://doi.org/10.1101/005165)

• gplots
  Offers plotting tools including heatmap.2() for hierarchical clustering and visualizations.
  CRAN Page: gplots on CRAN
  Citation:
  Warnes, G. R., et al. (2022). gplots: Various R Programming Tools for Plotting Data. R package version 3.1.3.

• genefilter Bioconductor package for high-throughput filtering and statistical testing. genefilter

• Illumina 450K Product Files
  Official documentation and downloads: manifest files, annotation files, control probe info, and sample sheets.
  support.illumina.com – Product Files

• Infinium HumanMethylation450 BeadChip – Datasheet (PDF)
  Technical summary of the 450K array platform, including probe design, detection chemistry, and assay performance metrics.
  Download Datasheet

• minfi::preprocessFunnorm()
  Official function documentation describing how to apply functional normalization to HumanMethylation arrays using internal control probes.
  Reduces unwanted technical variation while preserving biological signals.
  Function Reference

• ggplot2
  Grammar of graphics implementation for elegant and layered data visualization.
  ggplot2 – tidyverse.org

• ggpubr
  Publication-ready plots built on top of ggplot2, with simplified syntax.
  ggpubr – datanovia.com

• cluster
  Core clustering algorithms and validation methods for statistical computing.
  cluster – CRAN

• FactoMineR
  Multivariate exploratory data analysis including PCA, MCA, and CA.
  FactoMineR – Bioconductor

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License

This project is licensed under the MIT License.
See the LICENSE file for more details.

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Contact

For questions, feedback, or reproducibility concerns, feel free to reach out to the project members:

• Martina Castellucci — martina.castellucci@studenti.unibo.it
• Alessia Corica — alessia.corica@studenti.unibo.it
• Sofia Natale — sofia.natale@studenti.unibo.it
• Andrea Pusiol — andrea.pusiol@studenti.unibo.it
• Perla Lucaboni — perla.lucaboni@studenti.unibo.it
• Aurora Mazzoni — aurora.mazzoni2@studenti.unibo.it
• Bianca Mastroddi — bianca.mastroddi@studenti.unibo.it

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This repository documents a reproducible methylation analysis workflow combining theoretical insights and practical bioinformatics skills.