This document discusses advanced strategies for metabolomic data analysis. It describes multivariate analysis techniques like visualization, clustering, projection, and modeling that allow for the simultaneous analysis of many variables. Specifically, it covers hierarchical and non-hierarchical clustering methods to identify patterns and group structures in data. It also explains principal component analysis (PCA) and partial least squares projection to latent structures (PLS) for dimensional reduction and maximizing variance or covariance in data. Finally, it discusses mapping analysis results onto biological networks to aid in interpretation and generate holistic summaries of findings.

1. Advanced Strategies for
Metabolomic Data Analysis
Dmitry Grapov, PhD

2. Analysis at the Metabolomic Scale

3. Multivariate Analysis
Samples
variables

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. Visualization: Dendrogram
Similarity
x
x
x
x

10. Implementation of Clustering
Overview Confirmation
How does my metadata
match my data structure?

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
Non-supervised dimensional
reduction technique
Principal Components (PCs)
• projection of the data which
maximize variance explained
Results
•eigenvalues = variance explained
•scores = new coordinates for
samples (rows)
•loadings = linear combination of
original variables which
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
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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)
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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
– empirical
Projection or mapping of analysis results
into a biological context.

23. Ingredients for Network Mapping
1. Determine connections
• Substrate/product (KEGG, biocyc)
• chemical similarity (Tanimoto
similarity)
• dependency (partial correlation)
2. Determine vertex properties
• magnitude
• importance
• direction
• relationships
• etc.

24. Making Connections Based on Biochemistry
•Organism specific
biochemical
relationships
•KEGG
•paid API
•download free
KGML file
•BioCyc
•Free API

25. Making Connections Based on Structural Similarity
•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
•Metamapp online tool
for data formatting
•http://uranus.fiehnlab.ucdavis.edu:8080/MetaMapp/homePage
BMC Bioinformatics 2012, 13:99 doi:10.1186/1471-2105-13-99

26. Ingredients for Mapped Networks
1. edge list
• biochemical
• structural
• empirical
2. vertex attributes
• user-defined
• based on
analysis
results
3. Visualization

27. Treatment effects
Connections
Biochemical (red)
Structural (violet)
Vertices
Shape (direction)
Size (magnitude)
Color (relationship)

28. Dynamic changes
connections (structural)

29. Gaussian Markov Network (intervention)Variable
Relationships

30. 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

31. 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