Network mapping 101 course
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Full course: https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/ The course covered all of the steps required to go from `raw data` to a rich `mapped biochemical network` incorporating statistical, multivariate and machine learning results. This included [examples](https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/#topics) and tutorials for: * Preparing raw data for analysis * Multivariate data exploration * Supervised clustering * Machine learning – classification model validation and feature selection * Network analysis - biochemical, structural similarity and correlation networks * Network mapping – putting it all together to create a publication quality network url: https://github.com/CreativeDataSolutions/CDS.courses/blob/gh-pages/courses/network_mapping_101/materials/lectures/tutorial.pdf
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Network mapping 101 course
- 1. Welcome to network mapping 101 Welcome to network mapping 101 In the following course you will learn how to integrate statistical, multivariate and machine learning results within a publication quality biochemical network. 1
- 2. Tutorials Tutorials https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/#topics 2 •Preparing raw data for analysis •Statistical analysis •Multivariate data exploration •Supervised clustering •Machine learning •classification •model validation •feature selection •Network analysis •biochemical •structural similarity •correlation •Network mapping – putting it all together
- 3. Analysis at the metabolomic scale 3
- 5. Identify what matters Univariate Group 1 Group 2 Multivariate Predictive Modeling ANOVA PCA PLS 5
- 6. Topics Topics https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/#topics 6 •Data preparation •Differential expression •Hierarchical Clustering •Principal Components Analysis (PCA) •Statistical analysis •Machine learning •Network analysis •Network mapping
- 7. How to think about data complexity n m 1-D 2-D m-D Data samples variables complexity Meta Data Experimental Design = Variable # = dimensionality 7
- 8. Data preprocessing Data preprocessing https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/partial/preprocess/ 8 define • data • row meta data • column meta data remove and/or impute • missing values
- 9. Follow along with the following tutorial: https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping _101/docs/partial/preprocess/ 9 Your turn Your turn
- 10. Differential expression Differential expression https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/partial/statistics/ 10 compare • class means identify • significant differences visualize • volcano plots • violin plots
- 11. Follow along with the following tutorial: https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping _101/docs/partial/statistics/ 11 Your turn Your turn
- 12. Hierarchical clustering (HCA) Hierarchical clustering (HCA) https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/partial/clustering/#heirarchical-clustering 12 group • samples and/or variables define similarity • correlation • distance • linkage visualize • heatmaps • dendrograms
- 13. Use the concept similarity/dissimilarity to group a collection of samples or variables approaches •hierarchical (linkage) •non-hierarchical (k-NN, k-means) •distribution (mixtures models) •density (DBSCAN) •self organizing maps (SOM) Linkage k-means Distribution Density 13 Clustering basics Clustering basics
- 14. identify •patterns •group structure •relationships evaluate and refine hypothesis reduce complexity Artist: Chuck Close 14 HCA goals HCA goals
- 15. distance • defines “nearness” or similarity X Y Euclidean X Y Manhattan Mahalanobis X Y * non-Euclidean HCA methods HCA methods
- 16. distance • defines “nearness” or similarity HCA methods HCA methods https://towardsdatascience.com/9-distance-measures-in-data-science-918109d069fa
- 17. HCA methods HCA methods single complete centroid average linkage or agglomeration • how samples or variables are connected or grouped
- 18. HCA methods HCA methods linkage or agglomeration https://scikit-learn.org/stable/auto_examples/cluster/plot_linkage_comparison.html
- 20. HCA interpretation HCA interpretation overview confirmation How does my metadata* match my data structure? * * *
- 21. Follow along with the following tutorial: https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping _101/docs/partial/clustering/#heirarchical-clustering 21 Your turn Your turn
- 22. https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/partial/multivariate/#pca 22 reduce • dimensionality maximize • variance explained visualize • variance explained • outliers • sample scores • variable loadings Principal Components Analysis (PCA) Principal Components Analysis (PCA)
- 23. James X. Li, 2009, VisuMap Tech. PCA goals 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 23
- 24. PCA interpretation PCA interpretation Variance explained (eigenvalues) Row (sample) scores and column (variable) loadings 24
- 25. *no scaling or centering glucose 219021 25 PCA example PCA example
- 26. Relationship between scores and loadings Relationship between scores and loadings 26 loadings scores top loading variable’s scatterplot
- 27. Follow along with the following tutorial: https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping _101/docs/partial/multivariate/#pca Your turn Your turn 27
- 28. https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/partial/model/ 28 predict • sample classification optimize • model performance select • important features visualize • model performance • feature importance Machine learning Machine learning
- 29. 29 model validation Machine learning Machine learning https://scikit-learn.org/stable/modules/cross_validation.html
- 30. 30 model training and validation Machine learning Machine learning https://en.wikipedia.org/wiki/Learning_curve_(machine_learning)
- 31. https://www.researchgate.net/publication/359896672_A_Review_of_Machine_Learning_Methods_Applied_to_Structural_Dynamics_and_Vibroacoustic/figures?lo=1 31 bias vs. variance Machine learning Machine learning
- 32. https://en.wikipedia.org/wiki/Evaluation_of_binary_classifiers 32 classification performance Machine learning Machine learning
- 34. 34 feature selection Machine learning Machine learning https://scikit-learn.org/stable/modules/feature_selection.html#feature-selection
- 35. 35 Random Forest • cross-validation • feature selection Machine learning Machine learning https://mlu-explain.github.io/random-forest/
- 36. 36 Random Forest • decision path Machine learning Machine learning https://github.com/parrt/dtreeviz
- 37. 37 Random Forest • decision path Machine learning Machine learning https://github.com/parrt/dtreeviz
- 39. Follow along with the following tutorial: https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping _101/docs/partial/model/ 39 Your turn Your turn
- 40. https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/partial/network/ 40 network mapping • transform variables network calculation • regularized correlation • biochemical • structural similarity • model performance visualize • interactive networks Network analysis Network analysis
- 41. Components for network mapping Components for network mapping connections (edges) • empirical dependency (correlation) • biochemical (substrate/product) • chemical similarity • … nodes (vertices) • magnitude • importance • direction • relationships • … 41
- 42. Network data structures Network data structures adjacency matrix 42 https://www.steveclarkapps.com/graphs/
- 43. Network data structures Network data structures adjacency list 43 https://www.steveclarkapps.com/graphs/
- 44. Network data structures Network data structures edge list and vertices list (node attributes) 44 source target … index …
- 45. Correlation networks Correlation networks Connect molecules based on strength of their correlation or partial-correlation 45 bivariate multivariate network
- 46. regularized correlation network showing relationships in metabolic timeseries measurements for two classes of samples 46 Correlation example Correlation example
- 51. •Use structure to generate molecular fingerprint •Calculate similarities between metabolites based on fingerprint BMC Bioinformatics 2012, 13:99 doi:10.1186/1471-2105-13-99 Structural similarity networks Structural similarity networks 51
- 52. 52 Structural similarity example Structural similarity example
- 53. Treatment Effects Network 53 Metabolites Shape = increase/decrease Size = importance (loading) Color = correlation Connections violet = Biochemical relationships green = Structural similarity Combining networks Combining networks https://dgrapov.github.io/MetaMapR/gallery.html
- 54. Follow along with the following tutorial: https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping _101/docs/partial/network/ 54 Your turn Your turn
- 55. learn • Cytoscape basics map variables to • node attributes • edge attributes optimize • layout • legend • publication quality figure https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/partial/cytoscape/ 55 Network refinement and visualization Network refinement and visualization
- 56. Follow along with the following tutorial: https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping _101/docs/partial/cytoscape/ 56 Your turn Your turn