DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Summary

DeepOmicsAE is a workflow centered on the application of a deep learning method (i.e., an autoencoder) to reduce the dimensionality of multi-omics data, providing a foundation for predictive models and signaling modules representing multiple layers of omics data.

Abstract

Large omics datasets are becoming increasingly available for research into human health. This paper presents DeepOmicsAE, a workflow optimized for the analysis of multi-omics datasets, including proteomics, metabolomics, and clinical data. This workflow employs a type of neural network called autoencoder, to extract a concise set of features from the high-dimensional multi-omics input data. Furthermore, the workflow provides a method to optimize the key parameters needed to implement the autoencoder. To showcase this workflow, clinical data were analyzed from a cohort of 142 individuals who were either healthy or diagnosed with Alzheimer's disease, along with the proteome and metabolome of their postmortem brain samples. The features extracted from the latent layer of the autoencoder retain the biological information that separates healthy and diseased patients. In addition, the individual extracted features represent distinct molecular signaling modules, each of which interacts uniquely with the individuals' clinical features, providing for a mean to integrate the proteomics, metabolomics, and clinical data.

Introduction

An increasingly large proportion of the population is aging and the burden of age-related diseases, such as neurodegeneration, is expected to sharply increase in the coming decades¹. Alzheimer's disease is the most common type of neurodegenerative disease². Progress in finding a treatment has been slow given our poor understanding of the fundamental molecular mechanisms driving the onset and progress of the disease. The majority of information on Alzheimer's disease is gained postmortem from the examination of brain tissue, which has made distinguishing causes and consequences a difficult task

Protocol

NOTE: The data used here were ROSMAP data downloaded from the AD Knowledge portal. Informed consent is not needed to download and reuse the data. The protocol presented herein utilizes deep learning to analyze multi-omics data and identify signaling modules that distinguish specific patient or sample groups based, for example, on their diagnosis. The protocol also delivers a small set of extracted features that summarize the original large-scale data and can be used for further analysis such as training a predictive mode.......

Discussion

The structure of the dataset is critical to the success of the protocol and should be carefully checked. The data should be formatted as indicated in protocol section 1. The correct assignment of column positions is also critical to the success of the method. Proteomics and metabolomics data are preprocessed differently and feature selection is conducted separately due to the different nature of the data. Therefore, it is critical to assign column positions correctly in protocol steps 1.5, 2.3, and 3.3.

Materials

Name	Company	Catalog Number	Comments
Computer	Apple	Mac Studio	Apple M1 Ultra with 20-core CPU, 48-core GPU, 32-core Neural Engine; 64 GB unified memory
Conda v23.3.1	Anaconda, Inc.	N/A	package management system and environment manager
conda environment DeepOmicsAE	N/A	DeepOmicsAE_env.yml	contains packages necessary to run the worflow
github repository DeepOmicsAE	Microsoft	https://github.com/elepan84/DeepOmicsAE/	provides scripts, Jupyter notebooks, and the conda environment file
Jupyter notebook v6.5.4	Project Jupyter	N/A	a platform for interactive data science and scientific computing
DT01-metabolomics data	N/A	ROSMAP_Metabolon_HD4_Brain 514_assay_data.csv	This data was used to generate the Results reported in the article. Specifically, DT01-DT04 were merged by matching them based on the individualID. The column final consensus diagnosis (cogdx) was filtered to keep only patients classified as healthy or AD. Climnical features were filtered to keep the following: age at death, sex and education. Finally, age reported as 90+ was set to 91, then the age column was transformed to float64. The data is available at https://adknowledgeportal.synapse.org
DT02-TMT proteomics data	N/A	C2.median_polish_corrected_log2 (abundanceRatioCenteredOn MedianOfBatchMediansPer Protein)-8817x400.csv
DT03-clinical data	N/A	ROSMAP_clinical.csv
DT04-biospecimen metadata	N/A	ROSMAP_biospecimen_metadata .csv
Python 3.11.3	Python Software Foundation	N/A	programming language