AltumAge

AltumAge is a pan-tissue DNA methylation epigenetic clock based on deep learning. For the pre-print of the paper, please click here.

Usage

In order to use AltumAge for age prediction, please follow the steps in example.ipynb. The example file also contains simple instructions to use Horvath's 2013 model for ease of comparison.

The main instructions to use AltumAge are as follows:

(1) Load required python packages:

The following packages must be installed. As of note, the model was trained with tensorflow 2.4.0, so beware of possible compatibility issues with other versions.

import tensorflow as tf
import numpy as np
import pandas as pd
from sklearn import linear_model, preprocessing

(2) Load list of CpGs, methylation data, scaler, and AltumAge model:

From your Illumina 27k or 450k array data, select the 21368 CpG sites from the file "CpGsites.csv" in the correct order.

cpgs = pd.read_csv('example_dependencies/CpGsites.csv').iloc[:,1]

Load the BMIQCalibration-normalized methylation data. It is crucial that the methylation beta values are normalized according to BMIQCalibration in R from "Horvath, S. DNA methylation age of human tissues and cell types." Genome Biol 14, 3156 (2013). https://doi.org/10.1186/gb-2013-14-10-r115. Moreover, reading the pickled example data only works in python version >= 3.8.

data = pd.read_pickle('example_dependencies/example_data.pkl')
real_age = data.age
methylation_data = data[cpgs]

Load the scaler, which transforms the distribution of beta values of each CpG site to mean = 0 and variance = 1.

scaler = pd.read_pickle('example_dependencies/scaler.pkl')

Finally, load AltumAge. There are two similar ways of reading the model. The first, which works with arm64 SoCs, is simply using the AltumAge folder contained in this GitHub repository.

AltumAge = tf.keras.models.load_model('AltumAge.h5')

(3) Scale the methylation data:

Scale the beta values of each CpG with sklearn robust scaler.

methylation_data_scaled = scaler.transform(methylation_data)

(4) Age prediction:

Finally, to predict age, simply use the following. The .flatten() command might be needed to transform the output into a 1D array.

pred_age_AltumAge = AltumAge.predict(methylation_data_scaled).flatten()

Voilà!

Supplementary Results

The summary files are CSVs containing detailed information regarding the performance of AltumAge and Horvath's 2013 model by data set in the test set.

Data availability

To access the raw data and metadata from Array Express and Gene Expression Omnibus (GEO) or the organized, non-normalized methylation data, please access our Google Drive here.

Citation

To cite our study, please use the following:

Lucas Paulo de Lima Camillo, Louis R Lapierre, Ritambhara Singh. AltumAge: A Pan-Tissue DNA-Methylation Epigenetic Clock Based on Deep Learning. bioRxiv 2021.06.01.446559; doi: https://doi.org/10.1101/2021.06.01.446559

joepalermo / AltumAge