Dmitry Grapov and Oliver FiehnUniversity of California, Davis

Multivariate Analysis and Visualization Tools for

Metabolomic Data

State of the art facility producing massive amounts of biological data…

>20-30K samples/yr>200 studies

Sam

ple

Variable

Data Analysis and Visualization

Quality Assessment• use replicated mesurements

and/or internal standards to estimate analytical variance

Statistical and Multivariate• use the experimental design

to test hypotheses and/or identify trends in analytes

Functional• use statistical and multivariate

results to identify impacted biochemical domains

Network• integrate statistical and

multivariate results with the experimental design and analyte metadata

experimental design - organism, sex, age etc.analyte description and metadata- biochemical class, mass spectra, etc.

VariableSample

Sam

ple

Variable

Data Analysis and Visualization

Quality Assessment• use replicated mesurements

and/or internal standards to estimate analytical variance

Statistical and Multivariate• use the experimental design

to test hypotheses and/or identify trends in analytes

Functional• use statistical and multivariate

results to identify impacted biochemical domains

Network• integrate statistical and

multivariate results with the experimental design and analyte metadata

Network Mapping

experimental design - organism, sex, age etc.analyte description and metadata- biochemical class, mass spectra, etc.

VariableSample

Principal Component Analysis (PCA) of all analytes, showing QC sample scores

Data Quality AssessmentDrift in >400 replicated measurements across >100 analytical batches for a single analyte

Acquisition batch

Abun

danc

e QCs embedded among >5,5000 samples (1:10) collected over 1.5 yrs

If the biological effect size is less than the analytical variance

then the experiment will incorrectly yield insignificant results

Data Quality AssessmentAnalyte specific data quality

overviewSample specific normalization can be used to estimate and remove analytical variance

Raw Data Normalized Data

Normalizations need to be numerically and visually validated

log mean

low precision

%RS

D

high precision

SamplesQCs

Network Mapping

Ranked statistically significant differences within a a biochemical

context

Statistics

Multivariate

Context

++=

Statistical and Multivariate AnalysesGroup 1

Group 2

What analytes are different between the

two groups of samples?

Statistical

significant differences lacking rank and

context

t-Test

Multivariate

ranked differences lacking significance

and context

O-PLS-DA

Network Mapping

Statistics

Multivariate

Context

++=

Statistical and Multivariate AnalysesGroup 1

Group 2

What analytes are different between the

two groups of samples?

Statistical

t-Test

Multivariate

O-PLS-DA

To see the big picture it is necessary too view the data from multiple different angles

DeviumWebhttps://github.com/dgrapov/DeviumWeb

• visualization• statistics• clustering • PCA• O-PLS

DeviumWebhttps://github.com/dgrapov/DeviumWeb

• visualization• statistics• clustering • PCA• O-PLS

Functional Analysis

Nucl. Acids Res. (2008) 36 (suppl 2): W423-W426.doi: 10.1093/nar/gkn282

Identify changes or enrichment in biochemical domains

• decrease• increase

Functional Analysis: opportunity for ‘Omic integration

Use domain knowledge databases to integrate genomic, proteomic and metabolomic data

Current approaches can be limited to pathway level analyses

Networks

Biochemical•reaction•domain

Structural •molecular fingerprints• mass spectra

Empirical •correlation•partial correlation

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

Mapped Network

- displaying metabolic differences in control vs.

malignant lung tissue

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 NetworksUse experiment specific or data driven relationships to gain novel insight

into biochemical relationshipsurea cycle

nucleotide

synthesis

protein

glycosylation

Mass Spectral NetworksUse mass spectra as a proxy for structure to help make sense of

unknown compounds’ biochemical identities

Watrous J et al. PNAS 2012;109:E1743-E1752

unknown compounds are likely phytosterol esters

Mass Spectral NetworksUse mass spectra and empirical relationships to narrow down the

biochemical roles for unknown compounds

Rigorous chemical experiments identified the unknown compounds as partial derivatization products of glucose

MetaMapRhttps://github.com/dgrapov/MetaMapR

Analysis at the Metabolomic Scale and Beyond

pyruvate lactate

enzyme

gene Bgene A

Pathway independent metabolomic (known and unknown), proteomic and genomic data integration

Software and Resources•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/

•MetaMapR: Network analysis tools for metabolomicsurl: https://github.com/dgrapov/MetaMapR

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

•Data analysis case studies and Examplesurl: http://imdevsoftware.wordpress.com/

dgrapov@ucdavis.edu metabolomics.ucdavis.edu

This research was supported in part by NIH 1 U24 DK097154