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/).
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.
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/).
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.
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/).
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/
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Parametric vs Nonparametric Tests: When to use whichGönenç Dalgıç
There are several statistical tests which can be categorized as parametric and nonparametric. This presentation will help the readers to identify which type of tests can be appropriate regarding particular data features.
Presentation made May 13, 2015 at live webinar titled Computational Modeling—Will it Rescue AD Clinical Trials? and hosted by Alzforum - http://www.alzforum.org/webinars/computational-modeling-will-it-rescue-ad-clinical-trials
Roche Quantitative Systems Pharmacology methodology workshop
February 4th-5th, 2016, Basel, Switzerland
Bringing multi-level systems pharmacology models to life
Natal van Riel
Abstract
Computational modelling in Systems Medicine and Systems Pharmacology addresses biological processes at different levels and scales. The quantification of model parameters from experimental data is a complicated task. It will be addressed how variance in data propagates into parameter estimates and, more importantly, model predictions. The Analysis of Dynamic Adaptations in Parameter Trajectories (ADAPT) approach is discussed as method to model dynamics at multiple time-scales. Two examples will be provided: 1) modelling of longitudinal data in a cohort of Type 2 Diabetics using different medication, and 2) the application in preclinical research studying the effect of liver X receptor activation on HDL metabolism and liver steatosis.
Predictive Models for Mechanism of Action Classification from Phenotypic Assa...Ellen Berg
Predictive Models for Mechanism of Action Classification from Phenotypic Assay Data – Application to Phenotypic Drug Discovery
Presentation at SLAS 2014 conference in San Diego, 21 January 2014
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
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
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/
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
2. Case Studies
1. Data Exploration and Analysis Planning
• Lung Cancer
2. Multifactorial Design
• Mouse Cerebellum
3. Time Course
• OGTT Metabolomics
3. Analysis Planning
DOD Lung Cancer Plasma (CARET)
Summary
•Analysis of plasma primary metabolites to identify circulating markers
related with lung cancer histology type.
Methods
•Exploratory data analysis using principal components analysis (PCA)
•Analysis of covariance (ANCOVA)
•Orthogonal partial least squares discriminant analysis (OPLS-DA)
•Hierarchical cluster analysis (HCA) and multidimensional scaling (MDS)
4. Lung Cancer: Exploratory Analysis
Purpose
•Overview data variance structure
Methods
•Singular value decomposition (SVD) on autoscaled data
PC1 and 2 (14% variance
explained) display 2
clusters of points
Cluster structure could not be
explained by histology or any
other metadata
Cluster structure is best
explained by instrumental
acquisition date
Black - 110629 to 110701
Red - 110702 to 110705
5. Lung Cancer: Analysis Planning
Purpose
•Identify significant changes in metabolites while adjusting for the noted batch effect, gender and
smoking status covariates.
Methods
•Shifted logarithm (natural) transformed data
•ANCOVA: batch + gender + smoking
•False Discovery Rate correction and estimation
PCA used to overview covariate
adjusted data structure
Cluster structure in the adjusted data suggests
that there is another unexplained covariate
OPLS-DA was used to evaluate covariate adjustments and
hypothesis testing strategies
Modeling histology (control in green) Modeling control/cancer and histology
6. Lung Cancer: ANCOVA
• Summary
• Optimal testing strategy was identified as :
• Using covariate adjusted data ( ~batch +gender +smoking) to test for differences between control and
cancer (adenocarcinoma, NSCLC and squamous)
OPLS-DA overview of optimized
modeling strategy
Identified 24 (8%) significantly changes species (3 post
FDR)
7. Lung Cancer: Correlation Analysis
Purpose
Identify relationships between
known and unknown metabolic
features.
Methods
•Hierarchical cluster analysis
(euclidean distances from
spearmans correlations, linked
by wards method)
Summary
•Top features could be grouped
into 8 major correlated clusters
Top changed unknown metabolites could
be linked to named species
•223566 tryptophan∝
•225405 1/ beta-alanine∝
•274174 methionine, glucuronic acid∝
•228377 tryptophan∝
•362112 tryptophan∝
8. Lung Cancer
Conclusions
• Metabolic data contained batch effects, which could be in part
explained by data acquisition date
• Univariate analyses were limited by the effects of outliers
• Multivariate modeling was used to identify 64 features (21%) which best
explain differences in plasma metabolites from patients with or without
lung cancer
• hydroxylamine, aspartic acid, and tryptophan displayed patterns of
change consistent with differences in patient cancer histology
• Correlation analysis was used to link many significant changes in
unknowns to tryptophan
9. Multifactorial Design
Mouse Cerebellum Metabolomics
Summary
•Analysis of mice carrying a gene mutation in ERCC8. Cockayne Syndrome B, rare
autosomal recessive congenital disorder, which is related to premature aging.
Mutant animals display altered glycolytic and mitochondrial metabolism which is
benefited by a high fat diet.
Study Design
•2 genotypes (WT, CSB; n=20)
•4 diets per genotype (SD, Resv, CR, HFD; n=5)
Analysis
•principal components analysis (PCA)
•two-way analysis of variance (ANOVA)
•orthogonal partial least squares discriminant analysis (OPLS-DA)
•network mapping
11. Mouse Cerebellum: Outliers
methods
Use PLS-DA to determine if
outlier samples hold when trying
to maximize the difference
between WT and CSB animals.
Findings
Noted outliers in WT should be
removed or analyzed separately
PCA
PLS-DA
12. Mouse Cerebellum: ANOVA
Methods
•shifted log transformed data
•two-way ANOVA (genotype, diet)
Findings
Identification of significant changes in metabolites due to genotype,
diet (treatment) and interaction between genotype and diet
genotype effect treatment effect interaction effect
13. Mouse Cerebellum: Multivariate Modeling
Methods
•autoscaled data
•classification of sample genotype OSC-PLS-DA/OPLS-DA
OSC-PLS-DA/OPLS-DA Validation
14. Mouse Cerebellum: Multivariate Modeling
Methods
•autoscaled data
•classification of sample genotype and diet (OPLS-DA)
•evaluation of Y construction (separate and combined)
multiple Y single Y
15. Mouse Cerebellum: Multivariate Modeling
Methods
•autoscaled data
•classification of diet (treatment) effects independently in each
genotype
WT CSB
16. Mouse Cerebellum: Network Analysis
Methods
•generate biochemical and chemical similarity network
•map statistical and OPLS-DA model results to network
•Analyze
– genotype network
– Treatment networks in WT and CSB separately
20. Mouse Cerebellum
Conclusions
Major differences between CSB and WT :
• elevation of 2-hydroxyglutaric acid in CSB
• 2-hydroxyglutaric aciduria is either autosomal recessive or autosomal
dominant
• perturbations in methionine and (potentially) single-carbon
metabolisms.
– Increase in the related species methionine, homoserine and serine and
decrease in adenosine-5'phosphate may point to decreases in s-
adenosyl methionine (SAM-e) synthesis. Reduction in SAM-e could have
detrimental effects on single carbon metabolism and methylation
reactions, which through a systemic reduction in choline would impact
phospotidylcholine synthesis.
•Independent of genotype, treatment effects can be classified on a
continuum of metabolic change from CR >HFD > Resv > SD.
– Treatment-related changes in citrulline were modified based on genotype
(strong genotype/treatment interaction).
•Similar changes due to treatment in both genotypes (e.g. 1,5-
anhydroglycitol) may be an outcome of diet composition and not
biology.
21. Time Course
Oral Glucose Tolerance Test Metabolomics
Summary
•Analysis of changes in plasma primary metabolites during an oral glucose
tolerance test (OGTT) before and after a 14 week diet and exercise intervention.
Study Design
•Overweight women (12-15, obese sedentary, glucose 100 -128 mg/dL )
–Pre and post intervention
•Clinical panel: insulin, glucose, lipids
•Primary metabolites at 0, 30, 60, 90, 120 minutes
Analysis
•principal components analysis (PCA)
•two-way analysis of variance (ANOVA)
•orthogonal partial least squares discriminant analysis (OPLS-DA)
•network mapping
24. OGTT: Data Analysis
• Identification of OGTT effects
– significant metabolomic excursions (one sample t-Test on AUC)
• pre, post or both
– intervention-adjusted PLS model
– OGTT biochemical/chemical similarity network
• Identification of treatment effects
– Univariate statics
• Two-way ANOVA time and intervention
• Mixed effects modeling (intervention as the main effect and individual subjects
as random effects)
– PLS-DA modeling and feature selection of changes in
• Baseline (t =0)
• AUC
• Combined baseline and AUC
– Analysis of correlations
25. OGTT: effects on primary metabolism
PCA
PLS-DA
(intervention adjusted data
modeling time)
32. Conclusion
• Each data analysis is unique
• Which method “should” be used is
defined by how the data “looks” and the
goal of the analysis
• Different analysis techniques are used to
get independent perspectives of the data
• Combination of similar evidence from
different techniques is used to define the
robust explanation of the experiment