lecture 3 case studies

7/29/2019 Lecture 3 Case Studies

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Metabolomic Data Analysis

Case Studies

Dmitry Grapov, PhD

Case

Stud

ies


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Case Studies

1. Data Exploration and Analysis Planning

Lung Cancer

2. Multifactorial Design Mouse Cerebellum

3. Time Course

OGTT Metabolomics


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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)


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Lung Cancer: Exploratory Analysis

Purpose

Overview data variance structureMethods

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


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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


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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 strategyIdentified 24 (8%) significantly changes species (3 post

FDR)


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Lung Cancer: Correlation Analysis

PurposeIdentify relationships betweenknown and unknown metabolicfeatures.

Methods

Hierarchical cluster analysis(euclidean distances fromspearmans correlations,linked by wards method)

Summary

Top features could begrouped into 8 majorcorrelated 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


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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


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Multifactorial Design

Mouse Cerebellum MetabolomicsSummary

Analysis of mice carrying a gene mutation in ERCC8. Cockayne Syndrome B, rareautosomal 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
http://en.wikipedia.org/wiki/Cockayne_syndromehttp://en.wikipedia.org/wiki/Cockayne_syndromehttp://en.wikipedia.org/wiki/Cockayne_syndromehttp://en.wikipedia.org/wiki/Cockayne_syndromehttp://en.wikipedia.org/wiki/Cockayne_syndromehttp://en.wikipedia.org/wiki/Cockayne_syndrome


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Mouse Cerebellum: PCA

Method

Conducted on autoscaled data

using SVD.

Findings

Identified 6 possible outliers all

of which are in the WT genotype


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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


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Mouse Cerebellum: ANOVAMethods

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


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Mouse Cerebellum: Multivariate Modeling

Methods

autoscaled data

classification of sample genotype OSC-PLS-DA/OPLS-DA

OSC-PLS-DA/OPLS-DA Validation


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Methods

autoscaled data

classification of sample genotype and diet (OPLS-DA) evaluation of Y construction (separate and combined)

multiple Y single Y


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Methods

autoscaled data

classification of diet (treatment) effects independently in eachgenotype

WT CSB


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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


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Mouse Cerebellum: Genotype Network


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Mouse Cerebellum: WT Treatment Network


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Mouse Cerebellum: CSB Treatment Network


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Mouse Cerebellum

Conclusions

Major differences between CSB and WT : elevation of 2-hydroxyglutaric acid in CSB

2-hydroxyglutaric aciduria is either autosomal recessive or autosomaldominant

perturbations in methionine and (potentially) single-carbon

metabolisms. Increase in the related species methionine, homoserine and serine anddecrease in adenosine-5'phosphate may point to decreases in s-adenosyl methionine (SAM-e) synthesis. Reduction in SAM-e could havedetrimental effects on single carbon metabolism and methylationreactions, which through a systemic reduction in choline would impactphospotidylcholine synthesis.

Independent of genotype, treatment effects can be classified on acontinuum 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.
http://en.wikipedia.org/wiki/2-Hydroxyglutaric_aciduriahttp://en.wikipedia.org/wiki/2-Hydroxyglutaric_aciduriahttp://en.wikipedia.org/wiki/2-Hydroxyglutaric_aciduriahttp://en.wikipedia.org/wiki/2-Hydroxyglutaric_aciduriahttp://en.wikipedia.org/wiki/2-Hydroxyglutaric_aciduriahttp://en.wikipedia.org/wiki/2-Hydroxyglutaric_aciduriahttp://en.wikipedia.org/wiki/2-Hydroxyglutaric_aciduriahttp://en.wikipedia.org/wiki/2-Hydroxyglutaric_aciduriahttp://en.wikipedia.org/wiki/2-Hydroxyglutaric_aciduria


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Time Course

Oral Glucose Tolerance Test MetabolomicsSummary

Analysis of changes in plasma primary metabolites during an oral glucosetolerance test (OGTT) before and after a 14 week diet and exerciseintervention.

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


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OGTT: Data Properties

Excursion

Baseline and Area

Under the Curve

(AUC)


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Time Course: Options

Baseline adjusted vs AUC

Raw (top) vs Baseline

adjusted (bottom)


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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 asrandom effects)

PLS-DA modeling and feature selection of changes in

Baseline (t =0)

AUC

Combined baseline and AUC

Analysis of correlations


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OGTT: effects on primary metabolism

PCAPLS-DA

(intervention adjusted data

modeling time)


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OGTT: effects network


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OGTT: Treatment Effects

PLS-DA


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Learning from the samples scores position


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Feature Selection onLoadingsVariable Loadings


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OGTT: Linking biology with our experiment


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OGTT: Analysis of Correlations


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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 fromdifferent techniques is used to define the

robust explanation of the experiment

lecture 3 case studies

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