Normalizing Gas‐Chromatography–Mass Spectrometry Data: Method Choice can Alter Biological Inference

We demonstrate how different normalization techniques in GC‐MS analysis impart unique properties to the data, influencing any biological inference. Using simulations, and empirical data, we compare the most commonly used techniques (Total Sum Normalization ‘TSN’; Median Normalization ‘MN’; Probabili...

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Bibliographic Details
Published in:BioEssays 2018-06, Vol.40 (6), p.e1700210-n/a
Main Authors: Noonan, Michael J., Tinnesand, Helga V., Buesching, Christina D.
Format: Article
Language:English
Subjects:
Animals
biomarker identification
Biomarkers
Biomarkers - chemistry
Computer simulation
Empirical analysis
Gas Chromatography-Mass Spectrometry - methods
GC–MS
Inference
log‐ratio transformations
Mass spectrometry
Mass spectroscopy
Normalizing
olfactory communication
pheromones
pre‐processing
size effects
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Summary:We demonstrate how different normalization techniques in GC‐MS analysis impart unique properties to the data, influencing any biological inference. Using simulations, and empirical data, we compare the most commonly used techniques (Total Sum Normalization ‘TSN’; Median Normalization ‘MN’; Probabilistic Quotient Normalization ‘PQN’; Internal Standard Normalization ‘ISN’; External Standard Normalization ‘ESN’; and a compositional data approach ‘CODA’). When differences between biological classes are pronounced, ESN and ISN provides good results, but are less reliable for more subtly differentiated groups. MN, TSN, and CODA approaches produced variable results dependent on the structure of the data, and are prone to false positive biomarker identification. In contrast, PQN exhibits the lowest false positive rate, though with occasionally poor model performance. Because ESN requires extensive pre‐planning, and offers only mixed reliability, and ISN, TSN, MN, and CODA approaches are prone to introducing artefactual differences, we recommend the use of PQN in GC–MS research. Gas‐chromatography–mass spectrometry data require extensive pre‐processing before data can be compared statistically. Problematically, the choice of normalization method can impart unique, and sometimes artefactual, properties to these data, influencing any biological inference. Here, the performance of the most commonly used normalization methods is compared, and recommendations for future research are made.
ISSN:0265-9247
1521-1878
DOI:10.1002/bies.201700210