Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling.
BACKGROUND
The generic metabolomics data processing workflow is constructed with a serial set of processes including peak picking, quality assurance, normalisation, missing value imputation, transformation and scaling. The combination of these processes should present the experimental data in an appropriate structure so to identify the biological changes in a valid and robust manner.


OBJECTIVE
Currently, different researchers apply different data processing methods and no assessment of the permutations applied to UHPLC-MS datasets has been published. Here we wish to define the most appropriate data processing workflow.


METHODS
We assess the influence of normalisation, missing value imputation, transformation and scaling methods on univariate and multivariate analysis of UHPLC-MS datasets acquired for different mammalian samples.


RESULTS
Our studies have shown that once data are filtered, missing values are not correlated withm/z, retention time or response. Following an exhaustive evaluation, we recommend PQN normalisation with no missing value imputation and no transformation or scaling for univariate analysis. For PCA we recommend applying PQN normalisation with Random Forest missing value imputation, glog transformation and no scaling method. For PLS-DA we recommend PQN normalisation, KNN as the missing value imputation method, generalised logarithm transformation and no scaling. These recommendations are based on searching for the biologically important metabolite features independent of their measured abundance.


CONCLUSIONS
The appropriate choice of normalisation, missing value imputation, transformation and scaling methods differs depending on the data analysis method and the choice of method is essential to maximise the biological derivations from UHPLC-MS datasets.
School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK ; MRC-ARUK Centre for Musculoskeletal Ageing Research, University of Birmingham, Birmingham, B15 2TT UK.
Riccardo
R
Di Guida
School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK ; NERC Biomolecular Analysis Facility-Metabolomics Node (NBAF-B), University of Birmingham, Birmingham, B15 2TT UK.
Jasper
J
Engel
School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK.
J William
JW
Allwood
School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK.
Ralf J M
RJ
Weber
School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK.
Martin R
MR
Jones
School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK ; NERC Biomolecular Analysis Facility-Metabolomics Node (NBAF-B), University of Birmingham, Birmingham, B15 2TT UK.
Ulf
U
Sommer
School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK ; NERC Biomolecular Analysis Facility-Metabolomics Node (NBAF-B), University of Birmingham, Birmingham, B15 2TT UK ; Phenome Centre Birmingham, University of Birmingham, Birmingham, B15 2TT UK ; Institute of Metabolism and Systems Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK.
Mark R
MR
Viant
School of Biosciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK ; MRC-ARUK Centre for Musculoskeletal Ageing Research, University of Birmingham, Birmingham, B15 2TT UK ; Phenome Centre Birmingham, University of Birmingham, Birmingham, B15 2TT UK ; Institute of Metabolism and Systems Research, University of Birmingham, Edgbaston, Birmingham, B15 2TT UK.
Warwick B
WB
Dunn
Glog transformation; KNN; Metabolomics; PQN normalisation; Random forest; UHPLC-MS