Datasets:
license: cc-by-sa-4.0
task_categories:
- tabular-regression
tags:
- biology
- aging
- biomarkers
- epigenetics
- bioinformatics
- life sciences
size_categories:
- 10K<n<100K
language:
- en
pretty_name: ComputAge Bench
configs:
- config_name: default
data_files:
- split: test
path: computage_bench_meta.tsv
ComputAge Bench Dataset
Introduction
DNA methylation is a chemical modification of DNA molecules that is present in many biological species, including humans. Specifically, methylation most often occurs at the cytosine nucleotides in a so-called CpG context (cytosine followed by a guanine). This modification is engaged in a variety of cellular events, ranging from nutrient starvation responses to X-chromosome inactivation to transgenerational inheritance. As it turns out, methylation levels per CpG site change systemically in aging, which can be captured by various machine learning (ML) models called aging clocks and used to predict an individual’s age. Moreover, it has been hypothesized that the aging clocks not only predict chronological age, but can also estimate biological age, that is, an overall degree of an individual’s health represented as an increase or decrease of predicted age relative to the general population. However, comparing aging clock performance is no trivial task, as there is no gold standard measure of one’s biological age, so using MAE, Pearson’s r, or other common correlation metrics is not sufficient.
To foster greater advances in the aging clock field, we developed a methodology and a dataset for aging clock benchmarking, ComputAge Bench, which relies on measuring model ability to predict increased ages in samples from patients with pre-defined aging-accelerating conditions (AACs) relative to samples from healthy controls (HC). We highly recommend consulting the Methods and Discussion sections of our paper before proceeding to use this dataset and to build any conclusions upon it.
Dataset description
This dataset was collected by combining publicly available datasets of DNA methylation in human blood and saliva samples from the NCBI Gene Expression Omnibus (GEO) data repository according to the criteria described in our publication, and it comprises samples from patients with aging-accelerating conditions and healthy controls. From all these public datasets, we selected only samples that had annotated ages and conditions (some disease vs. healthy control).
All methylation profiling platforms in this dataset represent different generations of Illumina Infinium BeadChip human methylation arrays. Detailed documentation for these platforms can be accessed at the manufacturer’s website (Methylation → Select One → Infinium Human Methylation 450K BeadChip or Infinium MethylationEPIC BeadChip).
The dataset consists of two main parts: data and meta. Data is stored with parquet in the “~/data” folder and contains methylation profiles across samples, and meta contains additional information, such as subject’s age, gender, condition, etc., and is stored as “~/computage_bench_meta.tsv”. Both parts are described further below in more details.
In total, the dataset comprises 10,404 samples and 900,449 features (DNA methylation sites) coming from 65 separate studies (some features are missing in some files). It is common for biological (omics) datasets to have N << P, so we had to put samples as columns and features as rows in order to save the dataset in the parquet format. We recommend transposing data upon loading in order to match with meta rows, as mentioned further below in the Usage Guidelines section.
Its main purpose is to be used in aging clock benchmarking (for more details on that, again, proceed to the Usage Guidelines and don’t hesitate to visit our paper). Nevertheless, you are free to use it for any other well-minded purpose you find suitable.
Repository structure:
computage_bench
|__ data # Folder with DNA methylation data
|__ computage_bench_data_<DatasetID_1>.parquet
|__ . . .
|__ README.md # This file
|__ computage_bench_meta.tsv # Meta table with sample annotations (age, condition, etc.)
Data description
DNA methylation per site can be reported in two ways: as beta values, or as M values. Briefly, beta values represent the ratio of methylated signal (probe intensity or sequencing read counts) to total signal per site (sum of methylated and unmethylated probe intensities or sequencing read counts), while M value is the log2 ratio of the methylated signal versus an unmethylated signal. A more thorough explanation can be found in Du et al., 2010.
In the original datasets deposited on GEO, therefore, DNA methylation values were represented either as a beta fraction (ranging from 0 to 1), beta percentages (ranging from 0 to 100), or M-values (can be both negative and positive, equals 0 when beta equals 0.5). We converted all data to the beta-value fractions ranging from 0 to 1. The values outside this range were treated as missing values (NaNs), as they are not biological.
In each dataset, only samples that appeared in the cleaned meta table (that is, were relevant for benchmarking) were retained.
Row names
DNA methylation site IDs according to the output from a methylation profiling platform.
Column names
GEO sample IDs.
Meta description
Row names
GEO sample IDs, as in data columns
Column names
DatasetID: GEO ID of a dataset that contains a respective sample.
PlatformID: GEO ID of a platform that was used to obtain a respective sample. Platform IDs can be mapped to the common platform names (that represent an approximate number of profiled methylation sites) as follows: GPL8490 = 27K, GPL13534 = 450K, GPL16304 = 450K, GPL21145 = 850K/EPIC, GPL23976 = 850K/EPIC, GPL29753 = 850K/EPIC.
Tissue: sample source tissue. “Blood” stands for peripheral blood samples, “Saliva” stands for saliva spit samples, and “Buccal” stands for buccal swab samples.
CellType: sample cell type. Either a specific cell population (mostly immune cell subtypes with cell type-specific molecular markers) or broader sample categories such as whole blood, buffy coat, peripheral blood mononuclear cells (PBMC), or peripheral blood leukocytes (PBL). Some samples lack cell type annotation.
Gender: abbreviated sample donor gender. M = Male, F = Female, O = Other, U = Unknown.
Age: sample donor chronological age in years (“float” data type). In the original datasets deposited on GEO, it can be either rounded by the researchers to full years, or converted from months, weeks, or days. Where available, we calculated years from the smaller units.
Condition: health or disease state of a sample donor.
Class: class of the respective sample condition. Healthy control samples (HC) are included in a separate healthy control class with the same abbreviation (HC).
Usage Guidelines
First install our nice library for convenient operation with datasets and other tools for aging clock design:
pip install computage
Now, suppose you trained a brand-new epigenetic aging clock model using classic scikit-learn library. You saved your model as pickle file. Then, the following block of code can be used for benchmarking your model. We also added several other published aging clocks for comparison with yours.
from computage import run_benchmark
#first define NaN imputation method for in_library models
#for simlicity here we recommend to use imputation with zeros
imputation = 'none'
models_config = {
"in_library":{
'HorvathV1':{'imputation':imputation},
'Hannum':{'imputation':imputation},
'PhenoAgeV2':{'imputation':imputation},
},
#here we should define a name of our new model as well as path
#to the pickle file (.pkl) of the model
"new_models":{
#'my_new_model_name': {'path':/path/to/model.pkl}
}
}
#now run the benchmark
bench = run_benchmark(models_config,
experiment_prefix='my_model_test',
output_folder='./benchmark'
)
#upon completion, the results will be saved in the folder you specified
Additional Information
Licensing Information
The dataset is released under the Creative Commons Attribution-ShareAlike (CC BY-SA) v4.0 license.
Citation Information
[Citation to be published]