language:en license:cc-by-4.0 size_categories:1K<n<10K format:csv modality:tabular modality:text library:datasets library:pandas library:mlcroissant library:polars region:us biology Aging
---
license: cc-by-4.0
language:
  - en
tags:
  - biology
  - Aging

configs:
  - config_name: meta
    data_files:
    - split: test
      path: "human_methylation_bench_ver1_meta_test.csv"
  - config_name: main
    data_files:
    - split: test
      path: "human_methylation_bench_ver1_main_test.csv"

---

# Human DNA Methylation Dataset ver1

This dataset is a benchmark dataset for predicting the aging clock, curated from publicly available DNA methylation data. The original benchmark dataset was published by [Dmitrii Kriukov et al. (2024)](https://www.biorxiv.org/content/10.1101/2024.06.06.597715v1.full) by integrating data from 65 individual studies.

To improve usability, we ensured unique sample IDs (excluding duplicate data, GSE118468 and GSE118469) and randomly split the data into training and testing subsets (train : test = 7 : 3) to facilitate model evaluation.

## Data

Methylation profiles (stored as `main.csv`), containing CpG beta values across samples. Total : 10,386 samples and 13,655 CpG sites.

- **`main`**: Methylation data table, containing beta values per CpG site for each sample.
- **`meta`**: Metadata table, detailing sample information such as `SampleID`, `DatasetID`, `PlatformID`, `Tissue`, `CellType`, `Gender`, `Age`, `Condition`, `Class`.
- **`adata`**: Advanced format for users requiring deeper analysis.

## Files

- `human_methylation_bench_ver1_main_train.csv`, `human_methylation_bench_ver1_main_test.csv`
- `human_methylation_bench_ver1_meta_train.csv`, `human_methylation_bench_ver1_meta_test.csv`
- `human_methylation_bench_ver1_adata_train.h5ad`, `human_methylation_bench_ver1_adata_test.h5ad`

## Metadata

- **`SampleID`**: Unique identifier for each sample.
- **`DatasetID`**: GEO ID of the dataset containing the respective sample.
- **`PlatformID`**: GEO ID of the platform used for sample profiling, which can be mapped to common platform names (e.g., GPL8490 = 27K, GPL13534 = 450K).
- **`Tissue`**: Source tissue of the sample, such as peripheral blood, saliva, or buccal swab.
- **`CellType`**: Type of cells in the sample, which could be a specific cell population (e.g., immune cell subtypes) or broader categories like whole blood, buffy coat, or PBMC.
- **`Gender`**: Donor gender (M = Male, F = Female, O = Other, U = Unknown).
- **`Age`**: Chronological age of the donor in years, either rounded or calculated from smaller units.
- **`Condition`**: Health or disease status of the donor, used to distinguish between healthy controls (HC) and aging-accelerating conditions (AACs). AACs include Atherosclerosis (AS), Ischemic Heart Disease (IHD), Cerebrovascular Accident (CVA), Inflammatory Bowel Disease (IBD), Human Immunodeficiency Virus infection (HIV), Extreme Obesity (XOB, defined by BMI ≥ 40 kg/m²), Type 1 Diabetes Mellitus (T1D), Type 2 Diabetes Mellitus (T2D), Rheumatoid Arthritis (RA), Osteoporosis (OP), Alzheimer's Disease (AD), Parkinson's Disease (PD), Multiple Sclerosis (MS), Creutzfeldt-Jakob Disease (CJD), Chronic Obstructive Pulmonary Disease (COPD), Tuberculosis (TB), Werner Syndrome (WS), Hutchinson-Gilford Progeria Syndrome (HGPS), and Congenital Generalized Lipodystrophy (CGL).
- **`Class`**: Class of the sample's condition, where healthy controls are in a separate class labeled as HC.
