Mapping to the Metabolomic Manifold

Mapping to the
metabolOMIC-Manifold
Dmitry Grapov, PhD

Metabolomics: study of small molecules

Metabolome: a proxy for phenotype

Type 2 Diabetes
PMID:24204828
2009
~10%
variance
explained
Many diseases, including aging,
have dominant metabolic
components (e.g. metabolic
syndrome)
Genotype +
metabolome
>40% variance
explained

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: Diabetes
Type 2 Diabetes x genotype
•mitochondrial
function is a
determinant of
T2D severity
•signaling lipids
are stored in
adipose
triglycerides
Grapov et. al., PLoS ONE (2012) doi:10.1371/journal.pone.0048852
Type 1 Diabetes non-progressors
•genetically and
environmentally
identical animals
avoid T1D onset
and display
significant
metabolic
differences
Grapov et. al., Metabolomics (2014) doi:10.1007/s11306-014-0706-2
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://Time teddy.epi.usf.edu/TEDDY/

Applications of Metabolomics: Early Life
Milk Glycans and Immune Markers
Markers of Autism in Twins Birth Weight
J Matern Fetal Neonatal Med. (2014) PMID 2528417
J Matern Fetal Neonatal Med. (2014) PMID 25284173
•Metabolomics can offer
non-genetic insight into
into pathpphysiological
states with complex
heritability patterns
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

Applications of Metabolomics: Cancer
Biochemical
Lung Cancer
•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
Grapov et. al., Cancer Prevention Research (2014, under review )

Applications of Metabolomics: Interventions
Drug effects
Drug Response
Grapov et. al., Circ. Cardiovasc. Genet. (2014, in press).
doi:10.1161/CIRCGENETICS.114.000606
Lifestyle (diet and exercise)
Grapov et. al.,PLoS ONE (2014) doi:10.1371/journal.pone.0084260
Journal of Proteome Research (2014, revision)
•Metabolomics
can offer real-time
insight into
treatment efficacy
and drive
personalized
medicine
decisions

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

Network Mapping
+ =
Network Mappings Mapped Network
Grapov D.,American Society of Mass Spectrometry Conference (2013, 2014)

Statistical and Multivariate Analysis
Group 1
Statistics
+
+
=
Multivariate
Context
Network Mapping
Ranked statistically
significant differences
within a a biochemical
context
Group 2
What analytes are
different between the
two groups of samples?
Statistical
t-Test
lacking rank and
context
Multivariate
O-PLS-DA
ranked differences
lacking significance
and context

Statistical and Multivariate Analysis
Group 1
Statistics
+
+
=
Multivariate
Context
Network Mapping
Ranked statistically
within a a biochemical
To see the big picture it is necessary to view
context
Group 2
What analytes are
different between the
two groups of samples?
Statistical
t-Test
lacking rank and
context
Multivariate
O-PLS-DA
ranked differences
lacking significance
and context
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
Can not be the
solution because it
does not conform to
square boundaries.
(Level 8)
http://uncyclopedia.wikia.com/wiki/Pac-Man_(walkthrough)

Data Analysis and Visualization
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

Devium
Dynamic MultivariatE Data Analysis and VIsUalization PlatforM
https://github.com/dgrapov/DeviumWeb
• Interactive visualizations
• Statistics
• Clustering
• Multivariate
• Predictive modeling
• Machine Learning
• Pathway analysis
• Etc.

Devium
Dynamic 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

MetaMapR
Metabolomic 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

Mapping to the Metabolomic Manifold

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