https://www.youtube.com/watch?v=4AhBN5Q1oMs

1. Webinar Session 5
Metabolomics and Beyond: Challenges and Strategies for
Next-Gen Omic Analyses
Dr. Dmitry Grapov
Data Scientist,
CDS- Creative Data Solutions and
Genome Data Analytics,
Monsanto, USA
dgrapov@gmail.com
Please note that the Webinars are presently free, courtesy of the Metabolomics Society and will be uploaded to the society's website. Please feel free to contact us
with any questions or suggestions via info.emn@metabolomicssociety.org

2. Metabolomics and Beyond
Challenges and Strategies
for Next-gen Omic Analyses
Dmitry Grapov, PhD
CDS- Creative Data Solutions

3. Background
Born: Minsk, Belarus in 1981
Minsk, Belarus
University of Utah (2000-2007)
•B.S. Biology
•B.S. Chemistry
Salt Lake City, UT
University of California, Davis
(2007-2012)
•Ph.D. Analytical Chemistry
with Emphasis in
Biotechnology
•Post doc, Oliver Fiehn Lab
Davis, CA
Interests:
•Omics, integromics, microbials and big biological data
•Multivariate data analysis and visualization, machine learning and software design
WCMC
•Principal Statistician at the NIH West
Coast Metabolomics Center (WCMC)
Data Scientist
•CDS - Creative Data Solutions
•Genome Analytics, Monsanto
St. Louis, MO

4. Experience: Omic’ data analysis and visualization
Grapov et. al., Circ. Cardiovasc. Genet. 2014
Network Analysis
Multivariate Modeling
Grapov et. al.,PLoS ONE (2014) doi:10.1371/journal.pone.0084260
J. Proteome Res., 2015, 14 (1), pp 557–566 DOI: 10.1021/pr500782g
Biomarker validation
•Metabolomics
can offer real-time
insight into
treatment efficacy
and drive
personalized
medicine
decisions

5. Metabolomics: study of small molecules

6. Metabolome: a proxy for phenotype

7. • Large and complex
studies
• Integration of multiple
biochemical domains
• Interpretation of
experimental results
within a biological
context
Challenges for Next-gen Omic Analyses

8. Large longitudinal studies may be required to identify
small phenotypic and environmental effects
http://teddy.epi.usf.edu/TEDDY/
TEDDY: The Environmental Determinants of Type 1 Diabetes in the Young
multi-Omic longitudinal study involving > 15,000
samples acquired over 3 yrs
Time
Time
Analytical batch effects can hide smaller
biological effects

9. Data normalization strategies should be
considered during experimental design
Analyte specific data quality
overview
normalizations can be used to remove
analytical variance
Raw Data Normalized Data
log mean
low precision
%RSD
high precision

10. Data normalization may require a combination of approaches
Internal standard (ISTD) based normalization
Retention time of
normalized compounds
Number of analytes optimally
normalized by each ISTD
(qcISTD)
qcISTD: analytical replicate
optimize QC selection

11. Data normalization may require a combination of approaches
Internal standard (ISTD) based normalization may not fully
remove analytical batch effects
Analytical replicate-based normalizations
can be used to estimate and remove
analytical variance
Raw Data Normalized Data
Samples
QCs
LOESS

12. Quality Control (QC) based normalization
Optimal method should use no sample knowledge
Across-batch
performance
Within-batch
performance
14,526 measurements of 443
variables acquired
over 2 years
Comparison of
normalization methods
Raw (RSD ~75)
Normalized (25)

13. Normalizations need to be numerically and visually validated
Good
Bad: QCs don’t match samples
Bad: overtrained
Challenge: getting appropriate QCs and
implementation of normalizations

14. Identification of systems of changes requires integration
of multiple analytical platforms
Am J Clin Nutr. 2015 Aug;102(2):433-43. doi: 10.3945/ajcn.114.103804. Epub 2015 Jul 8.

15. Modern metabolomic analyses often require
combinations of multiple measurement platforms
American Journal of Physiology - Endocrinology and Metabolism 2015 Vol. no. , DOI: 10.1152/ajpendo.00019.2015

16. 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
Need for Integromics

17. Omic’ data integration strategies
Biomarker Insights 2015:Suppl. 4 1-6 DOI: 10.4137/BMI.S29511
Empirical
correlation
Network
based
Biochemical
pathway

18. Pathway analysis

19. Metabolomic network analysis

20. MetaMapR: Metabolomic network calculation
http://dgrapov.github.io/MetaMapR/

21. MetaMapR: Metabolomic network calculation
• Biochemical
reactions
• Structural
similarity
• Mass
spectral
similarity
• Empirical
relationships

22. MetaMapR: Network visualization

23. Omic’ network analysis
http://kwanjeeraw.github.io/grinn/

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

25. DeviumWeb: Data analysis and visualization
https://github.com/dgrapov/DeviumWeb

26. DeviumWeb: Interactive visualization

27. DeviumWeb: Statistical Analysis

28. DeviumWeb: Cluster Analysis

29. DeviumWeb: Exploratory Analysis

30. DeviumWeb: Predictive Modeling

31. DeviumWeb: Pathway analysis

32. Thank you:
Metabolomics Society
Dr. Biswapriya Misra
Collaborators
Dr. Johannes Fahrmann
Dr. Kwanjeera Wanichthanarak
Dr. Oliver Fiehn
Dr. Suzanne Miyamoto
David Liesenfeld

33. Questions?
dgrapov@gmail.com
More information:
https://imdevsoftware.wordpress.com/
Software:
https://github.com/dgrapov
Hire me:
createdatasol@gmail.com