Mapping to themetabolOMIC-Manifold

Dmitry Grapov, PhD

Metabolomics: study of small molecules

Metabolome: a proxy for phenotype

PMID:24204828

2009

~10% variance explained

Many diseases, including aging, have dominant metabolic components (e.g. metabolic syndrome)

Genotype + metabolome >40% variance explained

Type 2 Diabetes

Integromics

Nature Genetics 46, 543–550 (2014) doi:10.1038/ng.2982

Variance in SNPs mapped to variance in metabolite concentrations

Empirical metabolic network displaying gene-metabolite associations

Utilize network manifold to uncover latent relationships

Applications of Metabolomics: DiabetesType 2 Diabetes x genotype

Grapov et. al., PLoS ONE (2012) doi:10.1371/journal.pone.0048852

•mitochondrial function is a determinant of T2D severity

•signaling lipids are stored in adipose triglycerides

Type 1 Diabetes non-progressors

Grapov et. al., Metabolomics (2014) doi:10.1007/s11306-014-0706-2

•genetically and environmentally identical animals avoid T1D onset and display significant metabolic differences

TEDDY: The Environmental Determinants of Type 1 Diabetes in the Young

Time•multi-Omic longitudinal study involving > 15,000 samples acquired over 3 yrs

http://teddy.epi.usf.edu/TEDDY/Time

Applications of Metabolomics: Early Life

J Matern Fetal Neonatal Med. (2014) PMID 2528417

Markers of Autism in Twins Birth Weight

J Matern Fetal Neonatal Med. (2014) PMID 25284173

•Metabolomics can offer non-genetic insight into into pathpphysiological states with complex heritability patterns

Milk Glycans and Immune Markers

J Nutr (2013) PMID: 24047700

•Maternal phenotype has a large impact on milk protein expression, modification (e.g. glycosylation) and function

Milk Proteins

Grapov et. al.,Journal of Proteome Research (2014, in Press)

•Changes in milk protein composition can lead to lasting perturbations in infant gut microbiota and energy metabolism

Biochemical

Applications of Metabolomics: CancerLung Cancer

Grapov et. al., Cancer Prevention Research (2014, under review )

•Multifactorial diseases such as cancer require unique of combinations of algorithms and analyses to identify important drivers of biochemical changes associated with these complex states

Empirical

Applications of Metabolomics: Interventions

Grapov et. al., Circ. Cardiovasc. Genet. (2014, in press).doi:10.1161/CIRCGENETICS.114.000606

Drug Response

Lifestyle (diet and exercise)

Grapov et. al.,PLoS ONE (2014) doi:10.1371/journal.pone.0084260

Journal of Proteome Research (2014, revision)

Drug effects

•Metabolomics can offer real-time insight into treatment efficacy and drive personalized medicine decisions

Analysis at the Metabol-OMIC ScaleDynamic a priori or a posteriori network construction, visualization and analysis

MappingsNetwork Mapped Network

Grapov D.,American Society of Mass Spectrometry Conference (2013, 2014)

Network Mapping

+ =

Network Mapping

Ranked statistically significant differences within a a biochemical

context

Statistics

Multivariate

Context

+

+

=

Statistical and Multivariate AnalysisGroup 1

Group 2

What analytes are different between the

two groups of samples?

Statistical

significant differenceslacking rank and

context

t-Test

Multivariate

ranked differences lacking significance

and context

O-PLS-DA

Network Mapping

Ranked statistically significant differences within a a biochemical

context

Statistics

Multivariate

Context

+

+

=

Statistical and Multivariate AnalysisGroup 1

Group 2

What analytes are different between the

two groups of samples?

Statistical

significant differenceslacking rank and

context

t-Test

Multivariate

ranked differences lacking significance

and context

O-PLS-DA

To see the big picture it is necessary to view the data from many different angles

Data Visualization

http://uncyclopedia.wikia.com/wiki/Pac-Man_(walkthrough)

Seems like a legitimate solution, but how can we confirm?Hint: Visualize!

Data Visualization

TROLL LVL 99

http://uncyclopedia.wikia.com/wiki/Pac-Man_(walkthrough)

Can not be the solution because it does not conform to square boundaries. (Level 8)

Data Quality Assessment• accuracy, precision, etc.

Statistical• hypotheses testing, FDR• power analysis, design of experiments

Multivariate• exploratory, non- or semi-supervised

• clustering, dimensional reduction, feature selection

• predictive modeling, classification, machine learning

Functional• biochemical enrichment or overrepresentation

Network• relationships, graph analyses

Network Mapping• data integration, visual data mining• pattern recognition

Data Analysis and Visualization

DeviumDynamic MultivariatE Data Analysis and VIsUalization PlatforM

https://github.com/dgrapov/DeviumWeb

• Interactive visualizations• Statistics• Clustering • Multivariate• Predictive modeling• Machine Learning• Pathway analysis• Etc.

DeviumDynamic MultivariatE Data Analysis and VIsUalization PlatforM

https://github.com/dgrapov/DeviumWeb

Metabolomic Networks

Biochemical (substrate/product)•Local Database •Web services

Chemical (structural or spectral similarity )•fingerprint generation•similarity calculation

Empirical (dependency)•correlation•partial-correlation

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

MetaMapRMetabolomic Network Analysis Tool

https://github.com/dgrapov/MetaMapR

MetaMapRMetabolomic Network Analysis Tool

https://github.com/dgrapov/MetaMapR

Pathway Based Omic-Integration

Biochemical Pathway Biochemical Ontology

Pathway Independent Omic-Integration

Modified from Barend Mons, 2012

Concept:

Use metabolic networks as a foundation to form the core of large-scale small molecule, protein and gene ‘interaction’ networks

Challenges:

•Database optimization

•Visualization

Domain independent network generation

Topological Data Analysis (TDA): mapping multivariate properties of data (nodes) to a network like manifold

Test hypotheses on the manifold representation of the data

Questions?

dgrapov@gmail.com