optimizing model parameters and implementing deepomicsae workflow

Análisis de módulos de señalización en la enfermedad de Alzheimer con aprendizaje profundo

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 destacado: Avance en la investigación del Alzheimer: exploración de la detección temprana y los enfoques multiómicos

DeepOmicsAE: Representación de módulos de señalización en la enfermedad de Alzheimer con análisis de aprendizaje profundo de proteómica, metabolómica y datos 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?

Optimización de los parámetros del modelo e implementación del flujo de trabajo de DeepOmicsAE

Para empezar, en la página principal de Jupyter Notebook, haga clic en la optimización del modelo M02-DeepOmicsAE. ipynb notebook para abrirlo en una nueva pestaña. En la segunda celda del bloc de notas, escriba el nombre del archivo de salida generado tras el preprocesamiento de datos en lugar de M01_output_data.csv.En la quinta celda, especifique las posiciones de columna para diferentes tipos de datos, como datos proteómicos, datos metabolómicos, datos clínicos y todos los datos de expresión molecular. Reemplace col_start y col_end por los índices de columna adecuados para cada tipo de datos. Especifique el nombre de la columna que contiene la variable de destino en lugar de y_column_name como y_label.En la sexta celda, defina el número de rondas para la optimización del modelo asignando un valor a n_comb. Más rondas de optimización ayudarán a ajustar los parámetros del modelo y mejorar el rendimiento del modelo, pero también aumentaremos el tiempo de procesamiento. Para ejecutar el bloc de notas, seleccione Celda y, a continuación, Ejecutar todo en la barra de menús.Para implementar el flujo de trabajo, haga clic en la implementación de M03a-DeepOmicsAE con parámetros optimizados personalizados. ipynb notebook en la página principal de Jupyter Notebook. En la segunda celda del bloc de notas, escriba el nombre del archivo de salida generado tras el preprocesamiento de datos en lugar de M01_output_data.csv.En la quinta celda, especifique las posiciones de columna para diferentes tipos de datos, como datos proteómicos, datos metabolómicos, datos clínicos y todos los datos de expresión molecular. Reemplace col_start y col_end por los índices de columna adecuados para cada tipo de datos. Especifique el nombre de la columna que contiene la variable de destino en lugar de y_column_name como y_label.Seleccione Celda y, a continuación, Ejecutar todo en la barra de menús. Los gráficos de PCA y la distribución de las puntuaciones de características importantes se guardarán automáticamente en la carpeta local. Las listas de características importantes para cada módulo de señalización identificado también se almacenarán como archivos de texto en la carpeta local con los nombres module_n.txt.Para implementar el flujo de trabajo con parámetros preestablecidos, haga clic en la implementación M03b-DeepOmicsAE con parámetros preestablecidos. ipynb notebook en la página principal de Jupyter Notebook. A continuación, siga el mismo procedimiento.Tenga en cuenta que los parámetros kprot, kmet y latent en la séptima celda de los cuadernos se calculan automáticamente dentro del script en función de los resultados de las rondas de optimización anteriores. El proteoma, el metaboloma y los datos clínicos de 142 muestras de cerebro humano postmortem derivadas de individuos sanos o diagnosticados con la enfermedad de Alzheimer se analizaron utilizando este flujo de trabajo basado en un modelo de codificador automático de aprendizaje profundo para extraer un conjunto conciso de características de los datos de entrada multiómicos de alta dimensión. Los resultados de la optimización de los parámetros del modelo muestran que la selección de un pequeño número de características proteómicas y metabolómicas que se utilizarán como entrada para el modelo proporciona un mayor grado de separación entre los pacientes sanos y los pacientes con enfermedad de Alzheimer.Mientras que el número de neuronas en la capa latente no tuvo un gran impacto en el rendimiento del modelo. Utilizando los parámetros óptimos, se extrajo un pequeño conjunto de características que resumían los datos de entrada, denominadas entidades extraídas, de la capa latente del modelo de codificador automático. El análisis de PCA mostró que los grupos diagnósticos estaban separados por las características extraídas.Sin embargo, los grupos no se distinguieron bien por las características originales, lo que indica que las características extraídas capturan la información crucial para determinar el estado de la enfermedad.

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 ...