optimizing model parameters and implementing deepomicsae workflow

使用深度学习分析阿尔茨海默病中的信号模块

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&#39;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&#160;molecular mechanisms driving the onset and progress of the disease. The majority of information on Alzheimer&#39;s disease is gained postmortem from the examination of brain tissue, which has made distinguishing causes and consequences a difficult task3. The Religious Orders Study/Memory and Aging Project (ROSMAP) is an ambitious effort to gain a broader understanding of neurodegeneration, which involves the study of thousands of individuals who have committed to undergo medical and psychological examinations yearly and to contribute their brains for research after their demise4. The study focuses on the transition from the normal functioning of the brain to Alzheimer&#39;s disease2. Within the project, postmortem brain samples&#160;were analyzed with a plethora of omics approaches, including genomics, epigenomics, transcriptomics, proteomics5, and metabolomics.
Omics technologies that offer functional readouts of cellular states (i.e., proteomics and metabolomics)6,7 are key to interpreting disease8,9,10,11,12, due to the direct relationship between protein and metabolite abundance and cellular activities. Proteins are the primary executors of cellular processes, while metabolites are the substrates and products for biochemical reactions. Multi-omics data analysis offers the possibility to understand the complex relationships between proteomics and metabolomics data instead of appreciating them in isolation. Multi-omics is a discipline that studies multiple layers of high-dimensional biological data, including molecular data (genome sequence and mutations, transcriptome, proteome, metabolome), clinical imaging data, and clinical features. Particularly, multi-omics data analysis aims to integrate such layers of biological data, understand their reciprocal regulation and interaction dynamics, and deliver a holistic understanding of disease onset and progression. However, methods to integrate multi-omics data remain in the early stages of development13.
Autoencoders, a type of unsupervised neural network14, are a powerful tool for multi-omics data integration. Unlike supervised neural networks, autoencoders do not map samples to specific target values (such as healthy or diseased), nor are they used to predict outcomes. One of their primary applications&#160;lies in dimensionality reduction. However, autoencoders offer several advantages over simpler dimensionality reduction methods such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), or uniform manifold approximation and projection (UMAP). Unlike PCA, autoencoders can capture non-linear relationships within the data. Unlike tSNE and UMAP, they can detect hierarchical and multi-modal relationships within the data since they rely on multiple layers of computational units each containing non linear activation functions. Therefore, they represent attractive models to capture the complexity of multi-omics data. Finally, while the primary application of PCA, tSNE, and UMAP is that of clustering the data, autoencoders compress the input data into extracted features that are well-suited for downstream predictive tasks15,16.
Briefly, neural networks comprise several layers, each containing multiple computational units or &#34;neurons.&#34; The first and last layers are referred to as the input and output layers, respectively. Autoencoders are neural networks with an hourglass structure, consisting of an input layer, followed by one to three hidden layers and a small &#34;latent&#34; layer typically containing between two and six neurons. This structure&#39;s first half is known as the encoder and is combined with a decoder mirroring the encoder. The decoder ends with an output layer containing the same number of neurons as the input layer. Autoencoders take the input through the bottleneck and reconstruct it in the output layer, with the goal of generating an output that mirrors the original information as closely as possible. This is achieved by mathematically minimizing a parameter termed &#34;reconstruction loss.&#34; The input consists of a set of features, which in the application showcased herein will be protein and metabolite abundances, and clinical characteristics (i.e., sex, education, and age at death). The latent layer contains a compressed and information-rich representation of the input, which can be used for subsequent applications such as predictive models17,18.
This protocol presents a workflow, DeepOmicsAE, which involves: 1) preprocessing of proteomics, metabolomics, and clinical data (i.e., normalization, scaling, outlier removal) to obtain data with a consistent scale for machine learning analysis; 2) selecting appropriate autoencoder input features, since feature overload may obscure relevant disease patterns; 3) optimizing and training the autoencoder, including determining the optimal number of proteins and metabolites to select, and of neurons for the latent layer; 4) extracting features from the latent layer; and 5) utilizing the extracted features for biological interpretation by identifying molecular signaling modules and their relationship with clinical features.
This protocol aims to be simple and applicable by&#160;biologists with limited computational experience who have a basic understanding of programming with Python. The protocol focuses on analyzing multi-omics data, including proteomics, metabolomics, and clinical features, but its use can be extended to other types of molecular expression data, including transcriptomics. One important novel application introduced by this protocol is mapping the importance scores of original features onto individual neurons in the latent layer. As a result, each neuron in the latent layer represents a signaling module, detailing the interactions between specific molecular alterations and the patients&#39; clinical characteristics. Biological interpretation of the molecular signaling modules is obtained by using MetaboAnalyst, a publicly available tool that integrates gene/protein and metabolite data to derive enriched metabolic and cell signaling pathways17.

作者聚焦：推进阿尔茨海默病研究 – 探索早期检测和多组学方法

DeepOmicsAE：通过蛋白质组学、代谢组学和临床数据的深度学习分析来表示阿尔茨海默病中的信号转导模块

Alzheimer's disease is thought to initiate decades before symptoms emerge. Recent studies suggest that phenotypic traits such as obesity and hypertension, but also the level of education and social engagement can act as risk factors. Our goal is to become able to decipher their contributions and their relation to molecular drivers of disease to learn how to intervene early and in a personalized manner.Multi-omic data analysis can be used for integrating various layers of biological data such as proteomics, transcriptomics, metabolomics, and phenotypic traits to comprehensively understand a disease state. Autoencoder models use deep learning to reduce the dimensionality of multi-omics datasets, effectively summarizing the crucial information, However, it is challenging to interpret how important are individual features in the original data with respect to the summarized output. In Deep-omics AE, we built in an algorithm that derives the importance of individual multi-omics features relative to the learn reduced dimensionality representation.With this approach, we can identify molecularly similar modules and their association with patient's phenotypic traits. Deep-omics AE helps putting in relation patient's phenotypic traits with the molecular makeup of disease. For example, you can use it to ask, what are the molecular pathways that are most implicated in Alzheimer's disease in older patients, and what are those that are most implicated in younger patients?What are those implicated in developing the disease in less educated patients versus more educated patients?

优化模型参数并实施 DeepOmicsAE 工作流程

首先，在 Jupyter 笔记本主页中，单击 M02-DeepOmicsAE 模型优化。ipynb notebook 以在新标签页中打开它。在笔记本的第二个单元格中，键入数据预处理时生成的输出文件的名称，而不是M01_output_data.csv。在第五个单元格中，指定不同数据类型的列位置，例如蛋白质组学数据、代谢组学数据、临床数据和所有分子表达数据。将 col_start 和 col_end 替换为每种数据类型的相应列索引。指定包含目标变量的列的名称，而不是y_column_name作为y_label。在第六个单元格中，通过为n_comb赋值来定义模型优化的轮数。更多的优化轮次将有助于微调模型参数并提高模型性能，但我们也会增加处理时间。通过选择“单元格”，然后从菜单栏中选择“全部运行”来执行笔记本。要实现工作流程，请单击具有自定义优化参数的 M03a-DeepOmicsAE 实现。Jupyter notebook 主页上的 ipynb notebook。在笔记本的第二个单元格中，键入数据预处理时生成的输出文件的名称，而不是M01_output_data.csv。在第五个单元格中，指定不同数据类型的列位置，例如蛋白质组学数据、代谢组学数据、临床数据和所有分子表达数据。将 col_start 和 col_end 替换为每种数据类型的相应列索引。指定包含目标变量的列的名称，而不是y_column_name作为y_label。从菜单栏中选择单元格，然后选择“全部运行”。重要特征分数的 PCA 图和分布将自动保存在本地文件夹中。每个已识别的信令模块的重要功能列表也将作为文本文件存储在本地文件夹中，名称为 module_n.txt。要使用预设参数实现工作流程，请单击带有预设参数的 M03b-DeepOmicsAE 实现。Jupyter notebook 主页上的 ipynb notebook。然后按照相同的步骤操作。请注意，笔记本第七单元格中的参数 kprot、kmet 和 latent 是根据之前优化轮次的结果在脚本中自动计算的。蛋白质组、代谢组和来自142个来自健康或被诊断患有阿尔茨海默病的个体的死后人脑样本的临床数据，使用基于深度学习自动编码器模型的工作流程进行分析，该模型从高维多组学输入数据中提取一组简洁的特征。模型参数优化结果表明，选择少量蛋白质组学和代谢组学特征作为模型的输入，可以在健康患者和阿尔茨海默病患者之间实现更高程度的分离。而潜层中的神经元数量对模型的性能没有重大影响。使用最优参数，从自动编码器模型的潜在层中提取一小组汇总输入数据的特征，称为提取特征。PCA分析显示，诊断组由提取的特征分开。然而，这些组不能很好地通过原始特征来区分，这表明提取的特征捕获了对确定疾病状态至关重要的信息。

Research

JoVE Journal

Biology

Optimizing Model Parameters and Implementing DeepOmicsAE Workflow

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

Large omics datasets are becoming increasingly available for research into human health. This paper presents DeepOmicsAE, a workflow optimized for the ...