biological interpretation of deepomicsae output using metaboanalyst

Analyzing Signaling Modules in Alzheimer&#039;s Disease with 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.

Author Spotlight: Advancing Alzheimer&#039;s Research &amp;#8211; Exploring Early Detection and Multi-Omics Approaches

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

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?

Biological Interpretation of DeepOmicsAE Output Using MetaboAnalyst

To begin, open a web browser and navigate to the link to access the Joint Pathway Analysis functionality on the MetaboAnalyst website. Access the folder where the output files from implementing DeepOmicsAE are saved, and open the text files named module_n. txt for each signaling module generated.From the text files, locate the list of proteins and copy them. On the MetaboAnalyst webpage, paste the copied list of proteins into the gene's proteins with optional fold changes window. Repeat for metabolites, pasting them into the compound list with optional fold changes window on the same webpage.Choose the relevant organism and the ID type. Then, submit the information by clicking Submit at the bottom of the page. On the following page, verify the ID mapping to ensure that the identifiers are correctly recognized, and then click Proceed.In the Parameter Setting page, select Metabolic pathways integrated or All pathways integrated to visualize the contribution of input to metabolic or all signaling pathways. In the Algorithm Selection panel, choose Enrichment analysis, Hypergeometric Test;Topology measure, Degree Centrality;and Integration method, Combine p values pathway-level. Click on Submit at the bottom of the page.The final page, Result View, will display the outcomes of the enrichment analysis. This includes plots of enriched pathways based on their impact and significance and a tabular list of pathways. MetaboAnalyst interpretation of the signaling modules obtained upon DeepOmicsAE on a dataset from brain samples of healthy or Alzheimer's patients revealed that for each signaling module, distinct metabolic and signaling pathways were enriched.Moreover, the characterization of interactions occurring between clinical features and signaling modules showed that the altered glycerolipid metabolism in the Alzheimer's patients was associated with their sex and age at death. Conversely, alterations of synapses and axon functionality tend to occur across Alzheimer's patients, irrespective of their sex, education level, and longevity.

biological-interpretation-of-deepomicsae-output-using-metaboanalyst

Research

JoVE Journal

Biology

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&#160;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&#39;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&#39; clinical features,&#160;providing for a mean to integrate&#160;the proteomics, metabolomics, and clinical data.

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.

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

Data Preprocessing for DeepOmicsAE Workflow

To begin, initiate a new Jupyter Notebook session by opening a new terminal window and typing Jupyter Notebook. Then press Enter. On the Jupyter Notebook homepage, select the notebook titled M01 expression data pre-processing.ipynb to open it in a new browser tab. This notebook will normalize and scale the input data, handle missing data, and remove outliers. In the second cell of the notebook, replace the placeholder your_dataset_name.csv with the actual name of the dataset file. In the last cell of the notebook, replace M01_output_data. csv with the preferred name for the output data file.For each data type, such as proteomics, metabolomics, continuous clinical data, and binary clinical data, use the command in the fourth cell to determine the indices corresponding to the first and last columns. Check the column names to locate the columns corresponding to the proteomics data, metabolomics data, and clinical data. Specify the column positions for different data types in the fifth cell by replacing col_start and col_end with the first and last column indices for each data type.Select Cell, then Run All from the menu bar in Jupyter to create the output data file in the specified folder.

Optimizing Model Parameters and Implementing DeepOmicsAE Workflow

To begin, in the Jupyter notebook homepage, click on the M02-DeepOmicsAE model optimization. ipynb notebook to open it in a new tab. In the second cell of the notebook, type the name of the output file generated upon data pre-processing in place of M01_output_data.csv.In the fifth cell, specify the column positions for different data types, such as proteomics data, metabolomics data, clinical data, and all molecular expression data. Replace col_start and col_end with the appropriate column indices for each data type. Specify the name of the column containing the target variable in place of y_column_name as y_label.In the sixth cell, define the number of rounds for model optimization by assigning a value to n_comb. More optimization rounds will help fine-tune the model parameters and improve model performance, but we'll also increase processing time. Execute the notebook by selecting Cell, then Run All from the menu bar.To implement the workflow, click on the M03a-DeepOmicsAE implementation with custom optimized parameters. ipynb notebook on the Jupyter notebook homepage. In the second cell of the notebook, type the name of the output file generated upon data pre-processing in place of M01_output_data.csv.In the fifth cell, specify the column positions for different data types, such as proteomics data, metabolomics data, clinical data, and all molecular expression data. Replace col_start and col_end with the appropriate column indices for each data type. Specify the name of the column containing the target variable in place of y_column_name as y_label.Select Cell followed by Run All from the menu bar. The PCA plots and distribution of important feature scores will be automatically saved in the local folder. Lists of important features for each identified signaling module will also be stored as text files in the local folder with the names module_n.txt.To implement the workflow with preset parameters, click on the M03b-DeepOmicsAE implementation with pre-set parameters. ipynb notebook on the Jupyter notebook homepage. Then follow the same procedure.Note that the parameters kprot, kmet, and latent in the seventh cell of the notebooks are computed automatically within the script based on the results of previous optimization rounds. Proteome, metabolome, and clinical data from 142 postmortem human brain samples derived from individuals that were either healthy or diagnosed with Alzheimer's disease were analyzed using this workflow based on a deep-learning auto-encoder model for extracting a concise set of features from the high-dimensional multi-omics input data. The results from model parameter optimization show that selecting a small number of proteomic and metabolomic's features to be used as input for the model provides for a higher degree of separation between the healthy and Alzheimer's disease patients.Whereas the number of neurons in the latent layer did not have a major impact on the performance of the model. Using the optimal parameters, a small set of features summarizing the input data, called extracted features, were extracted from the auto-encoder model's latent layer. PCA analysis showed that the diagnostic groups were separated by the extracted features.However, the groups were not distinguished well by the original features, indicating that the extracted features capture the information crucial for determining the disease state.