Data Analysis in Metabolomics

Tim EbbelsImperial College London

Themes

• Overview of metabolomics data processing workflow

• Differences between metabolomics and transcriptomics data

• Approaches to improving reproducibility & data quality

• Key challenges and bottlenecks

Metabolomics

METABOLIC PROFILE

TISSUEBIOFLUID

The study of the complement of small molecules within biological systems

CELL

Hormones

Untargeted: No prior hypothesis of specific metabolites involved

Biologicalquestion

Samplepreparation

Experi-mentaldesign

Data acquisition

Data pre-processing

Biologicalinter-

pretation

DataanalysisSampling

Raw data Data table Relevant metabolites,

connectivities, models

Metabolites

SamplesProtocol

Metabolite identification

Metabolomics workflow

Transcriptomic vs. Metabolomic DataTranscriptomics (microarrays 

/ sequencing)Metabolomics

Number of genes / metabolites known?

Yes No

Identity of genes / metabolites known?

Yes (sequence/locus) No (a priori)

Coverage Whole genome Very low (few %) of metabolome

Number of platforms Single Multiple (in same experiment)

Standardisation –analytical technology

High Constantly changing

Standardisation – data analysis

Relatively high Low

Correlation between variables

Medium Very high

LC‐MS Metabolomics Data

LC-MS Metabolic Profiles

~10,000s signals,100-1000s (?) metabolites

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)

Quality Control Samples• Representative biological sample, e.g. pool of study samples

• Repeated analysis throughout analytical run

9Gika, 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…

Quality Control and Data Filtering

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

Linearity filter• E.g. Filter out all features with correlation to dilution < 0.8

Normalisation• Correct global intensity drift

Drift correction• Correct feature specific drift within a batch

Batch correction• Correct drift across batches

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Drift Correction

• Instrument response changes smoothly over the run

• Use QC samples to estimate changes

• Typically local regression (e.g. LOESS) with cross‐validation

• Requires frequent QC injections

Dunn, W. B. et al. Nat Protoc 6, 1060 (2011).

Filtering for Repeatability

• Remove features with low repeatability in QC samples (e.g. coefficient of variation, CV<30%)

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Lab C Positive ESI, 100% CV Threshold   Lab C Positive ESI, 10% CV Threshold 

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t[1]COMET2 / Rob Whiffin

Filtering for Linearity• Some metabolite concentrations will be– Outside linear range of instrument, or

– Contaminants, solvent artefacts etc…

• Use a dilution series to select features which respond linearly

CV (%)

R2

Inte

nsity

Dilution factor

NMR Metabolomics Data

NMR Metabolic Profiles

~100s signals,10-100s (?) metabolites

NMR Metabolic Profiles: Problems

• Problems:– Assignment

• Knowns• Unknowns

– Peak overlap– Peak shift

?

Peak shifts• Caused primarily by pH & 

ionic strength variations• Some peaks more 

susceptible than others• Peaks for same molecule 

generally do NOT shift – In same direction– By same amount

Restrict pH variation using buffer

Try to keep in physiological range (~7‐8)

• pH shift may be the effect you’re looking for!

Urine titration series

pH 12

pH 2

Binning• Integrate spectral intensity

in each region one variable

• Benefits: reduces problems of– Peak shift– Large number of data

points• Drawbacks

– Bins not easily assigned –can be one or several compounds

– Statistical models not easily interpreted

Raw spectrum

Binned spectrum

Full resolution spectra

• Benefits:– Reduces difficulty of assignment (still manual)

• Drawbacks: does not overcome– Overlap– Shift– Large number of data points

Full resolution + alignment

• Move peaks until positions in different spectra match

• Difficult task, usually requires manual validation

• Can produce artefacts– Misassignment– Artificial signal– Warping of peak shape and/or area

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Non-aligned data RSPA corrected data

ppm ppm

Veselkov et al. Anal. Chem. 2009

Peak fitting• E.g. Chenomx NMR suite• Manual process, requiring manual validation

Succinate

Glutamine MalateGlutamate

Normalisation

• Transformation on each sample– Removing unwanted variation– Making samples more comparable

• What variation is unwanted? Examples:– Changes in detector response– Differences in urine volume/dilution 

• Classically achieved by setting total signal to a constant ( x = 1)

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Constant sum

Raw data

Normalisation to constant sum

Normalisation

• Account for gross sample to sample changes

• Global, e.g.– Median fold change– Total intensity

• Intensity dependent, e.g.– LOESS– Quantile

Veselkov, K. A. et al. Anal. Chem. 83, 5864 (2011).

Median fold change normalise

Comparison of Normalisation Methods• Simulated data• 4 normalisations:

– Total area– Median fold change– Minimum entropy– PCA scores

• Minimum entropy– Difference (test‐ref) is 

constant for dilution variables  low entropy

• Other methods:– Histogram– Quantile– Robust regression

Hector Keun / Jake Pearce

Summary

• Metabolomic data share many characteristics with other omics– But fundamentally different: cannot copy data analysis pipeline

• Current bottlenecks/challenges:– Metabolite identification– Standardisation of

• Sample collection• Analytical procedure• Data analysis