Datasets:

VerisimilitudeX
/

mobius-data

license: mit
task_categories:
  - feature-extraction
  - token-classification
  - zero-shot-classification
language:
  - en
tags:
  - biology
  - medical
size_categories:
  - 100B<n<1T

Epigenomic Data for ME/CFS and Long COVID Classification

Dataset Overview

This dataset contains DNA methylation data from blood samples of individuals with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), Long COVID (LC), and healthy controls. The data is derived from Illumina HumanMethylation450 BeadChip and Illumina MethylationEPIC arrays and has been organized to facilitate research into epigenetic biomarkers for these conditions.

Dataset Structure

root/
├── ME/                   # Myalgic Encephalomyelitis/Chronic Fatigue Syndrome samples
│   ├── GSE102504/        # GEO Series accession number
│   │   └── GSE102504_RAW/    # Raw data directory
│   │       └── *.idat    # Illumina IDAT files
│   ├── GSE111183/
│   │   └── GSE111183_RAW/
│   │       └── *.idat
│   ├── GSE112905/
│   │   └── GSE112905_RAW/
│   │       └── *.idat
│   ├── GSE153667/
│   │   └── GSE153667_RAW/
│   │       └── *.idat
│   ├── GSE156792/
│   │   └── GSE156792_RAW/
│   │       └── *.idat
│   ├── GSE166592/
│   │   └── GSE166592_RAW/
│   │       └── *.idat
│   ├── GSE59489/
│   │   └── GSE59489_RAW/
│   │       └── *.idat
│   └── GSE93266/
│       └── GSE93266_RAW/
│           └── *.idat
├── LC/                   # Long COVID samples
│   ├── GSE161678/
│   │   └── GSE161678_RAW/
│   │       └── *.idat
│   ├── GSE174818/
│   │   └── GSE174818_RAW/
│   │       └── *.idat
│   ├── GSE188573/
│   │   └── GSE188573_RAW/
│   │       └── *.idat
│   ├── GSE197152/
│   │   └── GSE197152_RAW/
│   │       └── *.idat
│   └── GSE210430/
│       └── GSE210430_RAW/
│           └── *.idat
└── controls/             # Healthy control samples
    ├── GSE112905/
    │   └── GSE112905_RAW/
    │       └── *.idat
    ├── GSE40279/
    │   └── GSE40279_RAW/
    │       └── *.idat
    └── GSE42861/
        └── GSE42861_RAW/
            └── *.idat

Data Description

File Format: .idat (Illumina's proprietary binary file format for BeadArray data)
Platforms: Illumina HumanMethylation450 BeadChip and Illumina MethylationEPIC arrays
Sample Composition: Approximately 30 ME/CFS patients, 20 Long COVID patients, and 20 healthy controls
Tissue Type: Peripheral blood
Data Type: DNA methylation beta values (representing the ratio of methylated probe intensity to total probe intensity)

About the Conditions

ME/CFS (Myalgic Encephalomyelitis/Chronic Fatigue Syndrome)

Chronic multi-system illness affecting approximately 0.5-1% of the population worldwide
Often triggered by viral illnesses
Characterized by long-term fatigue, post-exertional malaise, cognitive impairment, and autonomic dysfunction
Currently diagnosed by clinical exclusion due to lack of definitive biomarkers

Long COVID (Post-Acute Sequelae of COVID-19)

Refers to prolonged symptoms after acute SARS-CoV-2 infection
Affects approximately 10-20% of COVID-19 survivors
Shares some clinical features with ME/CFS
Similarly lacks definitive biomarkers and is diagnosed primarily by clinical assessment

Data Processing Guidelines

To process this methylation data, we recommend the following pipeline:

Quality Control:
- Filter out probes on sex chromosomes
- Remove known polymorphic or cross-reactive probes
- Perform sample quality checks (detection p-values, intensity distributions)
Normalization:
- Beta-value calculation
- BMIQ normalization for type I/II bias correction
- Optional: ComBat batch correction if combining multiple datasets
Feature Selection:
- Differential methylation analysis to identify disease-associated CpG sites
- Optional: Variance filtering to remove low-variability probes
Analysis:
- Recommended: Apply the Epigenomic Transformer Pipeline or similar methods
- Alternative approaches: Logistic regression, random forest, XGBoost

Usage Example

Here's a simple example of how to load and process this data using the minfi package in R:

library(minfi)
library(limma)
library(wateRmelon)

# Load raw data
baseDir <- "path/to/dataset/ME/GSE111183/GSE111183_RAW"
targets <- read.metharray.sheet(baseDir)
rgSet <- read.metharray.exp(targets=targets)

# Preprocess
mSet <- preprocessRaw(rgSet)
beta <- getBeta(mSet)

# Normalization
beta_norm <- BMIQ(beta)

# Visualization example
densityPlot(beta_norm, sampGroups=targets$Group)

Research Applications

This dataset is suitable for:

Developing and validating diagnostic classifiers for ME/CFS and Long COVID
Identifying epigenetic biomarkers specific to each condition
Studying the biological mechanisms underlying these post-viral illnesses
Comparing epigenetic patterns between ME/CFS and Long COVID
Training machine learning models for disease classification

Citation Information

If you use this dataset in your research, please cite:

Acharya, P., & Jacoby, D. (2025). Epigenomic Transformer Pipeline for ME/CFS and Long COVID Classification. 
University of Victoria.

Additional Resources

For more information about the Epigenomic Transformer Pipeline developed using this data, please visit our GitHub repository.

Acknowledgements

Data was sourced from the NCBI Gene Expression Omnibus (GEO) with the following accession numbers:

ME/CFS data: GSE102504, GSE111183, GSE112905, GSE153667, GSE156792, GSE166592, GSE59489, GSE93266
Long COVID data: GSE161678, GSE174818, GSE188573, GSE197152, GSE210430
Control data: GSE112905, GSE40279, GSE42861

We thank all the researchers who contributed to generating and sharing this valuable data.