What are the methods to correlate/study metabolomics data to proteomics data?
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3 months ago

What are the methods to correlate/study metabolomics data to proteomics data?

Could you answer with some references.

metabolomics proteomics multiomics • 445 views
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11 weeks ago

There are various methods to correlate and study metabolomics data to proteomics data. These methods can be broadly categorized into:

  1. Correlation-based methods:

    • Canonical correlation analysis (CCA): This method finds linear combinations of variables in both datasets that are maximally correlated. It helps identify relationships between sets of metabolites and proteins that co-vary across different samples.

    • Regularized sparse principal component analysis (sPCA): This method identifies the principal components that explain the most variance in both datasets while accounting for potential sparsity in the data.

    • Sparse PLS discriminant analysis (sPLS-DA): This method is used for classification tasks, where it identifies the most informative features from both datasets that can differentiate between different groups of samples.

    • Correlation network analysis: This method involves building a network where nodes represent metabolites and proteins, and edges represent significant correlations between them. Analyzing the network can reveal co-regulated modules and identify potential interactions between metabolites and proteins.

  2. Pathway-based methods:

    • Pathway enrichment analysis: This method identifies pathways that are significantly enriched for differentially expressed metabolites and proteins. This can help identify key metabolic pathways involved in a biological process or disease.

    • Subnetwork analysis: This method focuses on identifying subnetworks within metabolic and protein interaction networks that are associated with differentially abundant metabolites and proteins. This can help reveal specific metabolic pathways and protein complexes that are affected in a specific condition.

  3. Ontology-based methods:

    • Gene Ontology (GO) analysis: This method uses GO terms to annotate metabolites and proteins based on their biological functions. This allows for identifying shared functions between differentially abundant metabolites and proteins.

    • Metabolomics Ontology (MO): This ontology provides a standardized vocabulary for describing metabolites and their properties. It can be used to annotate metabolomics data and facilitate integration with other omics data types.

  4. Machine learning methods:

    • Random Forest: This method can be used to build predictive models that can predict the abundance of metabolites based on the expression of proteins.

    • Support Vector Machines (SVM): This method can be used for classification tasks, such as identifying samples with a specific disease based on their metabolomics and proteomics profiles.

    • Neural networks: These algorithms can learn complex relationships between metabolites and proteins, and can be used for tasks such as identifying metabolic pathways involved in a specific disease.

  5. Additional considerations:

    • Data preprocessing: Both metabolomics and proteomics data require careful preprocessing before analysis, including normalization, scaling, and missing value imputation.

    • Data integration platforms: Several software tools are available to facilitate the integration and analysis of metabolomics and proteomics data, such as mixOmics, MetaboAnalyst, and XCMS Online.

    • Interpretation of results: The results of any analysis should be carefully interpreted and validated using other methods, such as targeted metabolomics or proteomics experiments.

Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer

Guide to Metabolomics Analysis: A Bioinformatics Workflow

From correlation to causation: analysis of metabolomics data using systems biology approaches

Genomic, Proteomic, and Metabolomic Data Integration Strategies

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thank you, WUSCHEL, for the comprehensive reply.


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