optimizing model parameters and implementing deepomicsae workflow

Analisando Módulos de Sinalização na Doença de Alzheimer com Deep Learning

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.

Autor em destaque: Avançando na pesquisa de Alzheimer - explorando abordagens de detecção precoce e multiômica

DeepOmicsAE: Representando Módulos de Sinalização na Doença de Alzheimer com Análise de Aprendizagem Profunda de Proteômica, Metabolômica e Dados Clínicos

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?

Otimizando parâmetros de modelo e implementando o fluxo de trabalho do DeepOmicsAE

Para começar, na página inicial do notebook Jupyter, clique na otimização do modelo M02-DeepOmicsAE. IPYNB para abri-lo em uma nova guia. Na segunda célula do bloco de anotações, digite o nome do arquivo de saída gerado no pré-processamento de dados no lugar de M01_output_data.csv.Na quinta célula, especifique as posições da coluna para diferentes tipos de dados, como dados proteômicos, dados metabolômicos, dados clínicos e todos os dados de expressão molecular. Substitua col_start e col_end pelos índices de coluna apropriados para cada tipo de dados. Especifique o nome da coluna que contém a variável de destino no lugar de y_column_name como y_label.Na sexta célula, defina o número de rodadas para otimização do modelo atribuindo um valor a n_comb. Mais rodadas de otimização ajudarão a ajustar os parâmetros do modelo e melhorar o desempenho do modelo, mas também aumentaremos o tempo de processamento. Execute o bloco de anotações selecionando Célula e Executar Tudo na barra de menus.Para implementar o fluxo de trabalho, clique na implementação M03a-DeepOmicsAE com parâmetros otimizados personalizados. Bloco de anotações ipynb na página inicial do bloco de anotações Jupyter. Na segunda célula do bloco de anotações, digite o nome do arquivo de saída gerado no pré-processamento de dados no lugar de M01_output_data.csv.Na quinta célula, especifique as posições da coluna para diferentes tipos de dados, como dados proteômicos, dados metabolômicos, dados clínicos e todos os dados de expressão molecular. Substitua col_start e col_end pelos índices de coluna apropriados para cada tipo de dados. Especifique o nome da coluna que contém a variável de destino no lugar de y_column_name como y_label.Selecione Célula seguida de Executar tudo na barra de menus. Os gráficos de PCA e a distribuição de pontuações de recursos importantes serão salvos automaticamente na pasta local. Listas de recursos importantes para cada módulo de sinalização identificado também serão armazenadas como arquivos de texto na pasta local com os nomes module_n.txt.Para implementar o fluxo de trabalho com parâmetros predefinidos, clique na implementação M03b-DeepOmicsAE com parâmetros predefinidos. Bloco de anotações ipynb na página inicial do bloco de anotações Jupyter. Em seguida, siga o mesmo procedimento.Observe que os parâmetros kprot, kmet e latente na sétima célula dos blocos de anotações são computados automaticamente dentro do script com base nos resultados das rodadas de otimização anteriores. Proteoma, metaboloma e dados clínicos de 142 amostras de cérebro humano post-mortem derivadas de indivíduos saudáveis ou diagnosticados com doença de Alzheimer foram analisados usando esse fluxo de trabalho baseado em um modelo de autocodificador de aprendizado profundo para extrair um conjunto conciso de recursos dos dados de entrada multi-ômicos de alta dimensão. Os resultados da otimização de parâmetros do modelo mostram que a seleção de um pequeno número de características proteômicas e metabolômicas a serem usadas como entrada para o modelo proporciona um maior grau de separação entre os pacientes saudáveis e os pacientes com doença de Alzheimer.Já o número de neurônios na camada latente não teve grande impacto no desempenho do modelo. Usando os parâmetros ótimos, um pequeno conjunto de características resumindo os dados de entrada, chamados recursos extraídos, foram extraídos da camada latente do modelo autocodificador. A análise da ACP mostrou que os grupos diagnósticos foram separados pelas características extraídas.No entanto, os grupos não se distinguiram bem pelas características originais, indicando que as características extraídas capturam as informações cruciais para determinar o estado da doença.

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 ...