Data processing strategies for large-scale

untargeted metabolomics epidemiology

Imperial College London

Department of Epidemiology and Biostatistics

Dr Ibrahim Karaman

Outline

• Introduction

– Characteristics of Metabolomics Data

– Challenges of large-scale data processing

– The COMBI-BIO Project

– The Airwave Study

• Workflow & Results

– NMR

– LCMS

• Outlook

2

Characteristics of Metabolomics Data

How to analyse?

High throughput

• High dimensionality

• N variables >> N samples

High degree of colinearity

• Biological / analytical correlations

Unknown number and identity of metabolites

• Contrast with microarray/sequence data

Multiple analytical methods

• NMR, LC-MS, GC-MS, CE-MS…

Sensitive to many effects

• Error & bias

• Confounders

3

NMR Metabolic Profiles: Problems

• Assignment

– Knowns

– Unknowns

• Peak overlap

• Peak shift

?

4

Succinate

Glutamine MalateGlutamate

Raw spectrum

Reduced spectrum

NMR preprocessing: Solutions

• Peak fitting

• Binning

• Full resolution spectrum

• With/without alignment

5

NMR – Other issues

• Normalisation– Accounting for uncontrolled

metabolome-wide effects, e.g.

dilution

– Common options

• Constant total intensity

• Constant median fold change

(‘probabilistic quotient’)

• Artefacts

Water

suppression

residual

Interfering

signals

11.522.533.544.5

0

2

4

6

8

10

12

14

16

18

20

x 106

ppm

Dilution /

amount of

sample

11.522.533.544.5

0

5

10

15

20x 10

-4

ppm

Normaliseddata

6

LC-MS preprocessingPeak detection Peak matching

Retention time

alignmentPeak table

Raw data

Peak integration

Peak filling

XCMS – Smith et al. Anal Chem 78, 779 (2006)

7

Quality Control Samples

• Representative biological sample, e.g. pool of study

samples

• Repeated analysis throughout analytical run

Gika, H. G., Theodoridis, G. A., Wingate, J. E., and Wilson, I. D.,

J. Proteome Res. 6 (8), 3291 (2007).

Study samples

Pooled

QC

sample

Run order

…

8

LC-MS: Quality Control and Data Filtering

Repeatability filter

• E.g. Filter out all features with CV<30% in QC samples

Dilution series filtering

• E.g. Filter out all features with r<0.8

Normalisation

• Correct global intensity drift

Drift correction

• Correct feature-specific drift within a batch

Batch correction

• Correct drift across batches

9

Problems with large studies• Large batches – significant analytical drift

• Batch to batch variation

• Between cohort variation

• Large data size

– Computational requirements

– High power need for more stringent control

• Implications for:

– Preprocessing

– Feature matching between cohorts

– Biomarker discovery (e.g. pooled or meta-analysis)

10

COMBI-BIO Project

• Development of COMBInatorial BIOmarkers for

subclinical atherosclerosis

– Outcomes coronary artery calcium & inter media thickness

• Serum from 3 Cohorts: MESA (4000), LOLIPOP

(2000), Rotterdam (2000)

• 3 NMR assays: NOESY + CPMG + JRES

• 4 UPLC-MS assays: LIPID & HILIC, +/- modes

• Each cohort split into 2 phases (1 cohort/phase = 1

batch)

http://www.combi-bio.eu

11

The Airwave Study

• Evaluation of possible health risks associated with the

use of TETRA (Terrestrial Trunked Radio)

• 3000 individuals with metabolomics data

• Plasma:

– 3 NMR assays: NOESY + CPMG + JRES

– 4 UPLC-MS assays: LIPID & HILIC, +/- modes

• Urine:

– 2 NMR assays: NOESY + JRES

– 4 UPLC-MS assays: LIPID & HILIC, +/- modes

http://www.police-health.org.uk/

12

NMR Workflow*

7) Cohort/batch adjustment

6) Spectral binning (optional)

5) Removal of outliers

4) Normalization

3) Removal of interfering signals

2) Spectral peak alignment

1) Integration of multi-cohort/batch data

High resolution spectra

Binned spectra

*Manuscript under review by Journal of Proteome Research

13

Alignment

befo

rea

fter

14

Removal of interferents, outliers & normalisation

water resonance

Methanol

contamination

other suspected

contaminants

Probabilistic Quotient

Normalisation

PCA score plots

Raw sample spectra

15

Cohort/phase adjustment

LOLIPOP – PH1

LOLIPOP – PH2

MESA – PH1

MESA– PH2

ROTTERDAM – PH1

ROTTERDAM– PH2

LOLIPOP – PH1

LOLIPOP – PH2

MESA – PH1

MESA– PH2

ROTTERDAM – PH1

ROTTERDAM– PH2

corrected

Rph

m

Rph

Rph

m

Rph

Mph

m

Mph

Mph

m

Mph

Lph

m

Lph

Lph

m

Lph

X

XX

XX

XX

XX

XX

XX

22

11

22

11

22

11

X

16

Results - PCA

17

Results - PCA - QCs

PC1

PC2

QC1

QC2

Monitor ratio of analytical

variance to biological variance:

var(QC scores) / var(BIO scores)

Variance

ratio

Raw

spectra

Processed

spectra

QC1 3.59% 1.64%

QC2 1.77% 0.81%

18

Results – Hotelling’s T2

Distribution

0

5

10

15

20

BIO QC1 QC2

Unaligned

Hote

lling T

2

0

5

10

15

20

BIO QC1 QC2

Normalized

Hote

lling T

2

IQR= 1.425 IQR= 0.679

IQR= 0.471 IQR= 0.355

PCA models

R2>0.95

6 components

19

LC-MS Workflow*

*Manuscript in preparation.

Variance-Stabilizing Transformation

Accounting for Heteroscedastic Noise Structure

CV filterRemoving features with %CV > 50% in iQC’s

Drift CorrectionAccounting instrumental instability

Median-Fold-Change NormalisationAccounting for Systematic Bias between Samples

Dilution Filterkeeping variables with linear property (Robust Linear Regression)

QC filterkeeping variables which appear in at least one of the QC samples

XCMS Data Table

for

each r

ack

20

Dilution Filter

• Dilution series based on

iQC pool

• Analyzed at beginning,

middle and end of each

batch

• Keep features with R2>0.6

in all series (robust linear

regression)

21

Drift Correction

• For each feature, fit

LOESS curve to intensity

vs. run order in iQCs

• Use this estimate to

correct intensity in the

biological samples

• LOESS smoothing (span)

determined via cross-

validation

22

Results – Rotterdam HILIC+ Phase 2

23

Results - CV Distributions (Phase 2)

24

Summary & Outlook

www.nihr.ac.uk 25

• Untargeted metabolomics poses special challenges

– Shifts in peak position, shape & intensity in both

NMR & LCMS

• Problems become acute with large studies

– Multi platform

– Multi cohort

• Proposed workflows improve stability & reliability of

data for further statistical analysis

COMBI-BIO: Study Design

26

Phase 1 LOLIPOP MESA ROTTERDAM

N 1000 2000 1000

NMR

NOESY NOESY NOESY

CPMG CPMG CPMG

JRES JRES JRES

LCMS

HILIC+ HILIC+ HILIC+

HILIC- HILIC- HILIC-

LIPID+ LIPID+ LIPID+

LIPID- LIPID- LIPID-

Phase 2 LOLIPOP MESA ROTTERDAM

N 1000 2000 1000

NMR

NOESY NOESY NOESY

CPMG CPMG CPMG

JRES JRES JRES

LCMS

HILIC+ HILIC+ HILIC+

HILIC- HILIC- HILIC-

LIPID+ LIPID+ LIPID+

LIPID- LIPID- LIPID-

(3+4)x3x2 = 42 data sets across 8000 individuals 24,000 NMR spectra 32,000 LCMS spectra

COMBI-BIO – QC Design

27

NMR Description Phase 1 freq Phase 2 freq

QC1 Commercial serum 1/95 (1 per rack) 1/189 (1 per 2 racks)

QC2 Pool of LOLIPOP cohort - 1/95 (1 per rack)

LCMS

iQC Cohort specific pool 1/20 1/6

eQC External serum 1/20 1/24

LTR Long Term Reference (National Phenome Centre)

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