optimizing model parameters and implementing deepomicsae workflow

深層学習によるアルツハイマー病におけるシグナル伝達モジュールの解析

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.

著者スポットライト:アルツハイマー病研究の推進 – 早期発見とマルチオミクスアプローチの探求

DeepOmicsAE:プロテオミクス、メタボロミクス、臨床データの深層学習解析によるアルツハイマー病のシグナル伝達モジュールの表現

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?

モデル パラメーターの最適化と DeepOmicsAE ワークフローの実装

まず、Jupyter Notebook のホームページで、M02-DeepOmicsAE モデルの最適化をクリックします。ipynb notebook をクリックして新しいタブで開きます。ノートブックの 2 番目のセルに、データの前処理時に生成される出力ファイルの名前を M01_output_data.csv の代わりに入力します。5番目のセルでは、プロテオミクスデータ、メタボロミクスデータ、臨床データ、すべての分子発現データなど、さまざまなデータタイプの列位置を指定します。col_start と col_end を、各データ型の適切な列インデックスに置き換えます。y_column_nameの代わりに、ターゲット変数を含む列の名前を y_label として指定します。6 番目のセルで、n_comb に値を割り当てて、モデル最適化のラウンド数を定義します。最適化ラウンドを増やすと、モデル パラメーターの微調整とモデルのパフォーマンスの向上に役立ちますが、処理時間も長くなります。ノートブックを実行するには、メニュー バーから [セル] を選択し、 [すべて実行] を選択します。ワークフローを実装するには、カスタム最適化パラメータを持つM03a-DeepOmicsAE実装をクリックします。ipynb notebook を Jupyter Notebook ホームページに表示します。ノートブックの 2 番目のセルに、データの前処理時に生成される出力ファイルの名前を M01_output_data.csv の代わりに入力します。5番目のセルでは、プロテオミクスデータ、メタボロミクスデータ、臨床データ、すべての分子発現データなど、さまざまなデータタイプの列位置を指定します。col_start と col_end を、各データ型の適切な列インデックスに置き換えます。y_column_nameの代わりに、ターゲット変数を含む列の名前を y_label として指定します。メニューバーから「セル」を選択し、「すべて実行」を選択します。PCAプロットと重要な特徴量スコアの分布は、自動的にローカルフォルダに保存されます。識別された各シグナリングモジュールの重要な機能のリストも、module_n.txt名前のローカルフォルダにテキストファイルとして保存されます。プリセットパラメータを使用してワークフローを実装するには、プリセットパラメータを使用してM03b-DeepOmicsAEインプリメンテーションをクリックします。ipynb notebook を Jupyter Notebook ホームページに表示します。その後、同じ手順に従います。ノートブックの 7 番目のセルのパラメーター kprot、kmet、latent は、以前の最適化ラウンドの結果に基づいてスクリプト内で自動的に計算されることに注意してください。このワークフローでは、健常者またはアルツハイマー病と診断された人から得られた142のヒト死後脳サンプルのプロテオーム、メタボローム、および臨床データを、深層学習オートエンコーダーモデルに基づいて解析し、高次元マルチオミクス入力データから簡潔な特徴セットを抽出しました。モデルパラメータ最適化の結果は、モデルの入力として使用される少数のプロテオミクスおよびメタボロミクスの特徴を選択することで、健康な患者とアルツハイマー病患者をより高度に分離できることを示しています。一方、潜在層のニューロンの数は、モデルのパフォーマンスに大きな影響を与えませんでした。最適なパラメーターを使用して、抽出された特徴と呼ばれる入力データを要約する小さな特徴セットが、オートエンコーダー モデルの潜在層から抽出されました。PCA分析では、抽出された特徴量によって診断群が分離されていることが示されました。しかし、これらのグループは元の特徴によって十分に区別されておらず、抽出された特徴が病態を決定するために重要な情報を捉えていることが示されました。

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 ...

モデル パラメーターの最適化と DeepOmicsAE ワークフローの実装

モデルパラメーターの最適化と DeepOmicsAE ワークフローの実装