Analysis at the Metabolomic Scale

Dmitry Grapov, PhD

Tools for Metabolomic Data Analysis and Visualization

1. Visualization (how does it look?)

• histograms, density plots, box plots, line plots, scatter plots, networks, etc.

2. Statistical Analysis (what is statistically significant?)

• summary tables, ANOVA, FDR adjustment, power analysis, etc.

3. Exploration (what are the major patterns/trends?)

• clustering, PCA, ICA, etc.

4. Predictive Modeling (what explains my hypothesis?)

• mixed effects, partial least squares (O-/PLS/-DA), etc.

5. Network Analysis (how are things related?)

• Pathway Enrichment

• Biochemical, mass spectral, empirical, etc.

6. Network Mapping

Common Data Analysis Tasks

Featured Tools

Data Analysis and Visualization• DeviumWeb- Dynamic multivariate data analysis and visualization

platformurl: https://github.com/dgrapov/DeviumWeb

• imDEV- Microsoft Excel add-in for multivariate analysisurl: http://sourceforge.net/projects/imdev/

Network Analysis• MetaMapR- Network analysis tools for metabolomics

url: https://github.com/dgrapov/MetaMapR

Network Mapping• MetaMapR + DeviumWeb + Cytoscape

DeviumWebhttps://github.com/dgrapov/DeviumWeb

Network Mapping

Visualization and analysis of statistical and multivariate results within a biochemical and/or empirical context.

Conduct Data Analysis

Generate Network

Map Results to Network

Grapov D., Fiehn O., Multivariate and network tools for analysis and visualization of metabolomic data, ASMS, June 08, 2013, Minneapolis, MN

Network Generation

Biochemical • enzymatic transformations

Structural Similarity• structural fingerprints • mass spectra

Empirical (dependency)• correlation• partial-correlation

BMC Bioinformatics 2012, 13:99 doi:10.1186/1471-2105-13-99

Metabolic Perturbations in Tumorigenesis

Biochemical and chemical similarity network comparing changes in metabolites between tumor and control tissue

Mappings:• direction of change • statistical significance • multivariate importance• molecular biochemical

domain

Biochemical Relationships

http://www.genome.jp/dbget-bin/www_bget?rn:R00975

Structural Similarity

http://pubchem.ncbi.nlm.nih.gov//score_matrix/score_matrix.cgi

Empirical NetworksExperiment specific data driven relationships can offer novel insight into biochemical perturbations urea cycle

nucleotide

synthesis

protein

glycosylation

Mass Spectral NetworksUse mass spectra as a proxy for structure to help make sense of unknown compounds’ biochemical identities.

Mix and match different relationships to help narrow down structures of unknowns

Partial derivatization products of glucose

MetaMapRhttps://github.com/dgrapov/MetaMapR

Data visualization as form of data analysis

DM

Liver CYP2D6

Dextromethorphan = additives in

dextrorphan

• high fructose corn syrup

• antioxidants

• flavor

Identification of treatment effects

Differential metabolic responses

Treatment 1 Treatment 2

Other Resources

• TeachingDemos- Tutorials and Demonstrations in R• url: http://sourceforge.net/projects/teachingdemos/?source=directory• url: https://github.com/dgrapov/TeachingDemos

• CDS Blog- Data analysis Case Studies and Tutorialsurl: http://imdevsoftware.wordpress.com/

dgrapov@ucdavis.edu metabolomics.ucdavis.edu

This research was supported in part by NIH 1 U24 DK097154