computage/computage_bench

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 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

Benchmark a new aging clock

First, install our nice library for convenient interaction with datasets and other tools built to design aging clocks:

pip install computage

Now, suppose you have trained your brand-new epigenetic aging clock model using the classic scikit-learn library. You saved your model as pickle file. Then, the following block of code can be used to benchmark your model. We also implemented imputation of missing values from the SeSAMe package and added several published aging clock models for comparison.

from computage import run_benchmark

# first, define a method to impute NaNs for the in_library models
# we recommend using imputation with gold standard values from SeSAMe
imputation = 'sesame_450k'

# for example, take these three clock models for benchmarking
models_config = {
    'in_library':{
        'HorvathV1':{'imputation':imputation},
        'Hannum':{'imputation':imputation},
        'PhenoAgeV2':{'imputation':imputation},
                },
    # here we can define a name of our new model, as well as path
    # to the pickle file (.pkl) that contains it
    'new_models':{
        #'my_new_model_name': {'path':/path/to/model.pkl}
        }
}

# 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 have specified for output

Explore the dataset

In case you only want to explore our dataset locally, use the following commands to download it:

from huggingface_hub import snapshot_download
snapshot_download(
    repo_id='computage/computage_bench', 
    repo_type="dataset",
    local_dir='.')

Once downloaded, the dataset can be open with pandas (or any other parquet reader).

import pandas as pd

# let's choose a study id, for example `GSE100264`
df = pd.read_parquet('data/computage_bench_data_GSE100264.parquet').T 
# note that we transpose data for a more convenient perception of samples and features
# don't forget to explore metadata (which is common for all datasets):
meta = pd.read_csv('computage_bench_meta.tsv', sep='\t', index_col=0)

Additional Information

Licensing Information

The dataset is released under the Creative Commons Attribution-ShareAlike (CC BY-SA) v4.0 license.

Citation Information

Please, cite us as

ComputAgeBench: Epigenetic Aging Clocks Benchmark https://doi.org/10.1101/2024.06.06.597715