Advanced strategies for Metabolomics Data Analysis
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The document discusses advanced strategies for metabolomic data analysis, highlighting multivariate analysis techniques such as clustering, projection, and modeling. Key methods include hierarchical and non-hierarchical clustering, principal components analysis, and partial least squares, which are crucial for visualizing data and uncovering relationships. Additionally, it emphasizes the importance of network analysis in interpreting biological connections among metabolites and offers resources for implementing these techniques.
Advanced strategies for Metabolomics Data Analysis
- 1. Advanced Strategies for Metabolomic Data Analysis Dmitry Grapov, PhD MetabolomicDataAnalysis
- 2. Analysis at the Metabolomic Scale
- 4. Multivariate Analysis • Visualization • Clustering • Projection • Modeling • Networks Simultaneous analysis of many variables
- 5. Clustering Identify •patterns •group structure •relationships •Evaluate/refine hypothesis •Reduce complexity Artist: Chuck Close
- 6. Cluster Analysis Use the concept similarity/dissimilarity to group a collection of samples or variables Approaches •hierarchical (HCA) •non-hierarchical (k-NN, k-means) •distribution (mixtures models) •density (DBSCAN) •self organizing maps (SOM) Linkage k-means Distribution Density
- 7. Hierarchical Cluster Analysis • similarity/dissimilarity defines “nearness” or distance X Y euclidean X Y manhattan Mahalanobis X Y * non-euclidean
- 8. Hierarchical Cluster Analysis single complete centroid average Agglomerative/linkage algorithm defines how points are grouped
- 9. Hierarchical Cluster Analysis (cont.) Similarity x x x x
- 10. Overview Confirmation How does my metadata match my data structure? Hierarchical Cluster Analysis (cont.)
- 11. Multidimensional Scaling PLoS ONE 7(11): e48852. doi:10.1371/journal.pone.0048852
- 12. Projection of Data The algorithm defines the position of the light source Principal Components Analysis (PCA) • unsupervised • maximize variance (X) Partial Least Squares Projection to Latent Structures (PLS) • supervised • maximize covariance (Y ~ X)
- 13. PCA: Goals Principal Components (PCs) •non-supervised •projection of the data which maximize variance explained Results 1.eigenvalues = variance explained 2.scores = new coordinates for samples (rows) 3.loadings = linear combination of original variables James X. Li, 2009, VisuMap Tech.
- 14. Interpreting PCA Results Variance explained (eigenvalues) Row (sample) scores and column (variable) loadings
- 15. PCA Example *no scaling or centering glucose 219021
- 16. How are scores and loadings related?
- 17. Centering and Scaling van den Berg RA, Hoefsloot HC, Westerhuis JA, Smilde AK, van der Werf MJ (2006) Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 7: 142.
- 18. Data scaling is very important! *autoscaling (unit variance and centered) glucose (GC/TOF) glucose (clinical) 219021
- 19. Use PLS to test a hypothesis Loadings on the first latent variable (x-axis) can be used to interpret the multivariate changes in metabolites which are correlated with time time = 0 120 min.
- 20. Modeling multifactorial relationships dynamic changes among groups~two-way ANOVA
- 21. “goodness” of the model is all about the perspective Determine in-sample (Q2 ) and out- of-sample error (RMSEP) and compare to a random model •permutation tests •training/testing
- 22. Biological Interpretation • Visualization • Enrichment • Networks – biochemical – structural – spectral – empirical Projection or mapping of analysis results into a biological context.
- 23. Ingredients for Network Analysis 1. Determine connections • biochemical (substrate/product) • chemical similarity • spectral similarity • empirical dependency (correlation) 2. Determine vertex properties • magnitude • importance • direction • relationships
- 24. Organism specific biochemical relationships and information Multiple organism DBs •KEGG •BioCyc •Reactome •Human •HMDB •SMPDB Making Connections Based on Biochemistry
- 26. •Use structure to generate molecular fingerprint •Calculate similarities between metabolites based on fingerprint •PubChem service for similarity calculations •http://pubchem.ncbi.nlm.nih.gov//score_matrix/score_matrix.cgi •online tools •http://uranus.fiehnlab.ucdavis.edu:8080/MetaMapp/homePage BMC Bioinformatics 2012, 13:99 doi:10.1186/1471-2105-13-99 Making Connections Based on structural similarity
- 28. Making Connections Based on spectral similarity Watrous J et al. PNAS 2012;109:E1743-E1752 •Connect molecules based on EI or MS/MS spectral similarity •Useful for linking annotated analytes (known) to unknown
- 29. Spectral Similarity Network Watrous J et al. PNAS 2012;109:E1743-E1752
- 30. Making connections based on empirical relationships •Connect molecules based on strength of correlation or partial- correlation
- 31. Treatment Effects Network = Metabolites Shape = increase/decrease Size = importance (loading) Color = correlation Connections red = Biochemical relationships violet = Structural similarity
- 32. Summary Multivariate analysis is useful for: •Visualization •Exploration and overview •Complexity reduction •Identification of multidimensional relationships and trends •Mapping to networks •Generating holistic summaries of findings
- 33. Resource •Mapping tools (review) • Brief Bioinform (2012) doi: 10.1093/bib/bbs055 •Tutorials and Examples • http://imdevsoftware.wordpress.com/category/uncategorized/ • https://github.com/dgrapov/TeachingDemos •Chemical Translations Services •CTS: http://cts.fiehnlab.ucdavis.edu/ •R-interface: https://github.com/dgrapov/CTSgetR •CIR: http://cactus.nci.nih.gov/chemical/structure •R-interface: https://github.com/dgrapov/CIRgetR