Strategies for Metabolomics Data AnalysisDmitry Grapov
Part of a lectures series for the international summer course in metabolomics 2013 (http://metabolomics.ucdavis.edu/courses-and-seminars/courses). Get more material and information here (http://imdevsoftware.wordpress.com/2013/09/08/sessions-in-metabolomics-2013/).
Advanced strategies for Metabolomics Data AnalysisDmitry Grapov
Part of a lectures series for the international summer course in metabolomics 2013 (http://metabolomics.ucdavis.edu/courses-and-seminars/courses). Get more material and information here (http://imdevsoftware.wordpress.com/2013/09/08/sessions-in-metabolomics-2013/).
Data Normalization Approaches for Large-scale Biological StudiesDmitry Grapov
Overview of how to estimate data quality and validate normalization approaches to remove analytical variance.
See here for animations used in the presentation:
http://imdevsoftware.wordpress.com/2014/06/04/using-repeated-measures-to-remove-artifacts-from-longitudinal-data/
Metabolomic Data Analysis Workshop and Tutorials (2014)Dmitry Grapov
Get more information:
http://imdevsoftware.wordpress.com/2014/10/11/2014-metabolomic-data-analysis-and-visualization-workshop-and-tutorials/
Recently I had the pleasure of teaching statistical and multivariate data analysis and visualization at the annual Summer Sessions in Metabolomics 2014, organized by the NIH West Coast Metabolomics Center.
Similar to last year, I’ve posted all the content (lectures, labs and software) for any one to follow along with at their own pace. I also plan to release videos for all the lectures and labs.
Metabolomic data analysis and visualization toolsDmitry Grapov
A description of data analysis and visualization tools for metabolomic and other high dimensional data sets, developed at the NIH West Coast Metabolomics Center.
Strategies for Metabolomics Data AnalysisDmitry Grapov
Part of a lectures series for the international summer course in metabolomics 2013 (http://metabolomics.ucdavis.edu/courses-and-seminars/courses). Get more material and information here (http://imdevsoftware.wordpress.com/2013/09/08/sessions-in-metabolomics-2013/).
Advanced strategies for Metabolomics Data AnalysisDmitry Grapov
Part of a lectures series for the international summer course in metabolomics 2013 (http://metabolomics.ucdavis.edu/courses-and-seminars/courses). Get more material and information here (http://imdevsoftware.wordpress.com/2013/09/08/sessions-in-metabolomics-2013/).
Data Normalization Approaches for Large-scale Biological StudiesDmitry Grapov
Overview of how to estimate data quality and validate normalization approaches to remove analytical variance.
See here for animations used in the presentation:
http://imdevsoftware.wordpress.com/2014/06/04/using-repeated-measures-to-remove-artifacts-from-longitudinal-data/
Metabolomic Data Analysis Workshop and Tutorials (2014)Dmitry Grapov
Get more information:
http://imdevsoftware.wordpress.com/2014/10/11/2014-metabolomic-data-analysis-and-visualization-workshop-and-tutorials/
Recently I had the pleasure of teaching statistical and multivariate data analysis and visualization at the annual Summer Sessions in Metabolomics 2014, organized by the NIH West Coast Metabolomics Center.
Similar to last year, I’ve posted all the content (lectures, labs and software) for any one to follow along with at their own pace. I also plan to release videos for all the lectures and labs.
Metabolomic data analysis and visualization toolsDmitry Grapov
A description of data analysis and visualization tools for metabolomic and other high dimensional data sets, developed at the NIH West Coast Metabolomics Center.
Case Study: Overview of Metabolomic Data Normalization StrategiesDmitry Grapov
Five normalization methods were compared, of which the combination of qc-LOESS and cubic splines showed the best performance based on within-batch and between-batch variable relative standard deviations for QCs. This approach was used to normalize sample measurements the results of which were analyzed using principal components analysis.
Automation of (Biological) Data Analysis and Report GenerationDmitry Grapov
I've been experimenting with automating simple and complex data analysis and report generation tasks for biological data and mostly using R and LATEX. You can see some of my progress and challenges encountered.
3 data normalization (2014 lab tutorial)Dmitry Grapov
Get more information:
http://imdevsoftware.wordpress.com/2014/10/11/2014-metabolomic-data-analysis-and-visualization-workshop-and-tutorials/
Recently I had the pleasure of teaching statistical and multivariate data analysis and visualization at the annual Summer Sessions in Metabolomics 2014, organized by the NIH West Coast Metabolomics Center.
Part of a lectures series for the international summer course in metabolomics 2013 (http://metabolomics.ucdavis.edu/courses-and-seminars/courses). Get more material and information here (http://imdevsoftware.wordpress.com/2013/09/08/sessions-in-metabolomics-2013/).
Prote-OMIC Data Analysis and VisualizationDmitry Grapov
Introductory lecture to multivariate analysis of proteomic data.
Material from the UC Davis 2014 Proteomics Workshop.
See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/
Harnessing The Proteome With Proteo Iq Quantitative Proteomics Softwarejatwood3
Learn how successful researchers are using ProteoIQ to streamline their proteomic data analysis.
Centralize data analysis on a single software platform
Most laboratories have multiple MS platforms with different software packages. ProteoIQ simplifies data analysis as a vendor independent software platform supporting qualitative and quantitative analysis.
Learn how to achieve robust peptide and protein quantification
ProteoIQ is the only commercial software platform supporting all popular forms of quantification. Learn how ProteoIQ performs protein and peptide quantification using isobaric tags, isotopic labels and label free methods including intensity based peptide profiling.
Elucidate biological significance
Learn how to integrate biological databases with ProteoIQ. Quickly move from MS results to the discovery of novel biological insights through an integrated biological annotation pipeline.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Case Study: Overview of Metabolomic Data Normalization StrategiesDmitry Grapov
Five normalization methods were compared, of which the combination of qc-LOESS and cubic splines showed the best performance based on within-batch and between-batch variable relative standard deviations for QCs. This approach was used to normalize sample measurements the results of which were analyzed using principal components analysis.
Automation of (Biological) Data Analysis and Report GenerationDmitry Grapov
I've been experimenting with automating simple and complex data analysis and report generation tasks for biological data and mostly using R and LATEX. You can see some of my progress and challenges encountered.
3 data normalization (2014 lab tutorial)Dmitry Grapov
Get more information:
http://imdevsoftware.wordpress.com/2014/10/11/2014-metabolomic-data-analysis-and-visualization-workshop-and-tutorials/
Recently I had the pleasure of teaching statistical and multivariate data analysis and visualization at the annual Summer Sessions in Metabolomics 2014, organized by the NIH West Coast Metabolomics Center.
Part of a lectures series for the international summer course in metabolomics 2013 (http://metabolomics.ucdavis.edu/courses-and-seminars/courses). Get more material and information here (http://imdevsoftware.wordpress.com/2013/09/08/sessions-in-metabolomics-2013/).
Prote-OMIC Data Analysis and VisualizationDmitry Grapov
Introductory lecture to multivariate analysis of proteomic data.
Material from the UC Davis 2014 Proteomics Workshop.
See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/
Harnessing The Proteome With Proteo Iq Quantitative Proteomics Softwarejatwood3
Learn how successful researchers are using ProteoIQ to streamline their proteomic data analysis.
Centralize data analysis on a single software platform
Most laboratories have multiple MS platforms with different software packages. ProteoIQ simplifies data analysis as a vendor independent software platform supporting qualitative and quantitative analysis.
Learn how to achieve robust peptide and protein quantification
ProteoIQ is the only commercial software platform supporting all popular forms of quantification. Learn how ProteoIQ performs protein and peptide quantification using isobaric tags, isotopic labels and label free methods including intensity based peptide profiling.
Elucidate biological significance
Learn how to integrate biological databases with ProteoIQ. Quickly move from MS results to the discovery of novel biological insights through an integrated biological annotation pipeline.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
Metabolomics: The Next Generation of Biochemistry Metabolon, Inc.
This brief eBook explores the benefits of incorporating the science of metabolomics into contemporary biology research as a stand-alone tool or as a compliment to genomics or other types of molecular biology research.
Step by step tutorial for conducting GO enrichment analysis and then creating a network from the results.
Material from the UC Davis 2014 Proteomics Workshop.
See more at: http://sourceforge.net/projects/teachingdemos/files/2014%20UC%20Davis%20Proteomics%20Workshop/
Metabolites have various functions, including fuel, structure, signaling, stimulatory and inhibitory effects on enzymes, catalytic activity of their own (usually as a cofactor to an enzyme), defense, and interactions with other organisms (e.g. pigments, odorants, and pheromones).
Metabolome refers to the complete set of chemical compounds involved in an organism's metabolism (such as metabolic intermediates, hormones and other signaling molecules, and secondary metabolites)
Metabolomics is the scientific study of chemical processes involving metabolites. Metabolomics is a relatively new member to the ‘-omics’ family of systems biology technologies.
https://www.youtube.com/watch?v=Y_-o-4rKxUk
Machine learning powered metabolomic network analysis
Dmitry Grapov PhD,
Director of Data Science and Bioinformatics,
CDS- Creative Data Solutions
www.createdatasol.com
Metabolomic network analysis can be used to interpret experimental results within a variety of contexts including: biochemical relationships, structural and spectral similarity and empirical correlation. Machine learning is useful for modeling relationships in the context of pattern recognition, clustering, classification and regression based predictive modeling. The combination of developed metabolomic networks and machine learning based predictive models offer a unique method to visualize empirical relationships while testing key experimental hypotheses. The following presentation focuses on data analysis, visualization, machine learning and network mapping approaches used to create richly mapped metabolomic networks. Learn more at www.createdatasol.com
Golden Helix’s SNP & Variation Suite (SVS) has been used by researchers around the world to do association testing and trait analysis on large cohorts of samples in both humans and other species. As samples size increase to do population-scale genomics, the analysis methods need to adapt to remain computable on your analysis workstation.
One of the most popular methods for determining population structure in SVS is Principal Component Analysis. In this webcast, we review the fundamentals of this methodology, as well as how we have advanced the state of the art by implementing a new “Large Data PCA” capability in SVS, handling over 10 times as many samples as previously possible at a fraction of the time. Join us as we cover:
A review of SVS association testing and trait analysis capabilities
Usage of Principle Component Analysis to discern population structure
Scaling PCA beyond the limitations of computer hardware Other SVS improvements based on ongoing feedback from the user community
SVS continues to move forward as a flexible and powerful tool to perform genotype and Large-N variant analysis. We hope you enjoy this webcast highlighting the exciting new features and select enhancements we have made.
Enhance Genomic Research with Polygenic Risk Score Calculations in SVSGolden Helix
Golden Helix’s SNP & Variation Suite (SVS) has been used by researchers around the world to do trait analysis and association testing on large cohorts of samples in both humans and other species. The latest SVS release introduces a significant leap in capabilities, with a focus on advanced Polygenic Risk Score (PRS) calculations. PRS has become a fundamental tool in genomic research, enabling the identification of correlations between genotypic variants and phenotypes across large populations.
This enhancement is particularly relevant for researchers working on large cohorts and meta-analysis. Please join us as we explore:
-SVS Workflow Review: A review of the extensive capabilities of SVS to meaningful insights from large cohorts and association test result datasets
-Computing Polygenic Risk Scores: An overview of the PRS capabilities in SVS, including Clumping and Thresholding and creation of multiple PRS models
-Evaluating and Applying PRS: Evaluating PRS models in-sample and out-of-sample and applying PRS models to perform trait prediction
-Future Implications: Brief exploration of how these advancements in SVS could influence future genomic research.
This webcast will explore how SVS facilitates the creation of multiple PRS models from large-scale genomic data, such as those obtained from extensive cohort studies or comprehensive meta-analyses. Join us to discover how these latest updates in SVS are supporting large-scale genomic research.
MedChemica Active Learning - Combining MMPA and MLAl Dossetter
Describes MedChemica research on combining Matched Molecular Pair Analysis (MMPA) and Machine Learning (ML) into a closed loop to find and optimize new hits for drug discovery. The talks describes the MMPA and Regression Forest models and how they were combined and some early conclusion. Of these permutative MMPA is the clear winner (Free Wilson ++)
Golden Helix’s SNP & Variation Suite (SVS) has been used by researchers around the world to do trait analysis and association testing on large cohorts of samples in both humans and other species. As Next-Generation Sequencing of whole genomes becomes more affordable, large cohorts of Whole Genome Sequencing (WGS) samples are available to search for additional trait association signals that were not found in array-based testing. In fact, recent papers have shown that WGS analysis using advanced GREML (Genomic Relatedness Restricted Maximum Likelihood) techniques is able to outperform micro-array based GWAS methods in the analysis of complex traits and proportion of the trait heritability explained.
Our latest update release of SVS has expanded the exiting maximum likelihood and GRM methods to support these new techniques. We have also enhanced various other association testing and prediction methodologies. This webcast showcases:
- Newly supported analysis workflow for whole genome variants using LD binning and enhanced GBLUP analysis
- Enhanced gender correction using REML
- Additional capabilities for genomic prediction and phenotype prediction
We are continually improving our products based on our customer’s feedback. We hope you enjoy this recording highlighting the exciting new features and select enhancements we have made.
The goal of this project is to find the best tool for predicting the life expectancy of people with Hepatitis B. Different Machine Learning methods have been completely studied and various Machine Learning methods have been carried out by different experimenters. Hepatitis B is a worldwide disease with a high mortality rate. Different methods have been used by different researchers to predict the life expectancy of Hepatitis B patients. The Machine Learning models and algorithms such as the Classification model, Logistic Regression model, Recursive Feature Elimination Algorithm, Cirrhosis Mortality model, Extreme Gradient Boosting, Random Forest, Decision Tree have been utilized by different researchers to predict the life expectancy of Hepatitis B patients. Some algorithms and models showed very interesting and proving results whereas some were not that good. Area Under Curve analysis was used to assess the estimation of various models. The AUROC value of the PSO model was minimal, while the ADT model had the highest accuracy. XGBoost showed appropriate predictive performance. All other models showed good calibration.
Presentation by Justin Zook at GRC/GIAB ASHG 2017 workshop "Getting the most from the reference assembly and reference materials" on benchmarks for indels and structural variants.
Next Generation Sequencing for Identification and Subtyping of Foodborne Pat...Nathan Olson
"Next Generation Sequencing for Identification and Subtyping of Foodborne Pathogens" presentation at the Standards for Pathogen Identification via NGS (SPIN) workshop hosted by the National Institute for Standards and Technology October 2014 by Rebecca Lindsey, PhD from Enteric Diseases Laboratory Branch of the CDC.
The 10th Annual Utah Health Services Research Conference: Data Quality in Multi-Site Health Services and Comparative Effectiveness Research: Lessons from PHIS+ By: Ram Gouripeddi
Health Services Research Conference: March 16, 2015
Patient Centered Research Methods Core, University of Utah, CCTS
Similar to Metabolomics and Beyond Challenges and Strategies for Next-gen Omic Analyses (20)
Full course: https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/
The course covered all of the steps required to go from `raw data` to a rich `mapped biochemical network` incorporating statistical, multivariate and machine learning results. This included [examples](https://creativedatasolutions.github.io/CDS.courses/courses/network_mapping_101/docs/#topics) and tutorials for:
* Preparing raw data for analysis
* Multivariate data exploration
* Supervised clustering
* Machine learning – classification model validation and feature selection
* Network analysis - biochemical, structural similarity and correlation networks
* Network mapping – putting it all together to create a publication quality network
url:
https://github.com/CreativeDataSolutions/CDS.courses/blob/gh-pages/courses/network_mapping_101/materials/lectures/tutorial.pdf
Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integratio...Dmitry Grapov
Machine learning (ML) is being ubiquitously incorporated into everyday products such as Internet search, email spam filters, product recommendations, image classification, and speech recognition. New approaches for highly integrated manufacturing and automation such as the Industry 4.0 and the Internet of things are also converging with ML methodologies. Many approaches incorporate complex artificial neural network architectures and are collectively referred to as deep learning (DL) applications. These methods have been shown capable of representing and learning predictable relationships in many diverse forms of data and hold promise for transforming the future of omics research and applications in precision medicine. Omics and electronic health record data pose considerable challenges for DL. This is due to many factors such as low signal to noise, analytical variance, and complex data integration requirements. However, DL models have already been shown capable of both improving the ease of data encoding and predictive model performance over alternative approaches. It may not be surprising that concepts encountered in DL share similarities with those observed in biological message relay systems such as gene, protein, and metabolite networks. This expert review examines the challenges and opportunities for DL at a systems and biological scale for a precision medicine readership.
current: https://drive.google.com/open?id=0B51AEMfo-fh9M3FmWXVlb05pdm8
I am always looking for the next data science, machine learning and visualization challenge.
Here is a link to my up to date
resume:
https://drive.google.com/open?id=0B51AEMfo-fh9M3FmWXVlb05pdm8
cv:
https://drive.google.com/open?id=0B51AEMfo-fh9Z05aM2p6XzFIOFE
DERIVATION OF MODIFIED BERNOULLI EQUATION WITH VISCOUS EFFECTS AND TERMINAL V...Wasswaderrick3
In this book, we use conservation of energy techniques on a fluid element to derive the Modified Bernoulli equation of flow with viscous or friction effects. We derive the general equation of flow/ velocity and then from this we derive the Pouiselle flow equation, the transition flow equation and the turbulent flow equation. In the situations where there are no viscous effects , the equation reduces to the Bernoulli equation. From experimental results, we are able to include other terms in the Bernoulli equation. We also look at cases where pressure gradients exist. We use the Modified Bernoulli equation to derive equations of flow rate for pipes of different cross sectional areas connected together. We also extend our techniques of energy conservation to a sphere falling in a viscous medium under the effect of gravity. We demonstrate Stokes equation of terminal velocity and turbulent flow equation. We look at a way of calculating the time taken for a body to fall in a viscous medium. We also look at the general equation of terminal velocity.
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
What is greenhouse gasses and how many gasses are there to affect the Earth.moosaasad1975
What are greenhouse gasses how they affect the earth and its environment what is the future of the environment and earth how the weather and the climate effects.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
Comparing Evolved Extractive Text Summary Scores of Bidirectional Encoder Rep...University of Maribor
Slides from:
11th International Conference on Electrical, Electronics and Computer Engineering (IcETRAN), Niš, 3-6 June 2024
Track: Artificial Intelligence
https://www.etran.rs/2024/en/home-english/
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
Nucleophilic Addition of carbonyl compounds.pptxSSR02
Nucleophilic addition is the most important reaction of carbonyls. Not just aldehydes and ketones, but also carboxylic acid derivatives in general.
Carbonyls undergo addition reactions with a large range of nucleophiles.
Comparing the relative basicity of the nucleophile and the product is extremely helpful in determining how reversible the addition reaction is. Reactions with Grignards and hydrides are irreversible. Reactions with weak bases like halides and carboxylates generally don’t happen.
Electronic effects (inductive effects, electron donation) have a large impact on reactivity.
Large groups adjacent to the carbonyl will slow the rate of reaction.
Neutral nucleophiles can also add to carbonyls, although their additions are generally slower and more reversible. Acid catalysis is sometimes employed to increase the rate of addition.
Metabolomics and Beyond Challenges and Strategies for Next-gen Omic Analyses
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
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
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
32. Thank you:
Metabolomics Society
Dr. Biswapriya Misra
Collaborators
Dr. Johannes Fahrmann
Dr. Kwanjeera Wanichthanarak
Dr. Oliver Fiehn
Dr. Suzanne Miyamoto
David Liesenfeld