Batch effect observed in Paper III

Samples that made up the basis for the analysis in Paper III were analyzed at two different labs: locally at NTNU, Trondheim and at Bruker BioSpin in Germany. As previously described, QC samples from the same serum mix were analyzed at the lab in Trondheim and in Germany, allowing us to investigate the presence of a batch effect. A study performed by Jiménez et al. reported excellent reproducibility for lipoprotein parameters and small molecular weight metabolites, when analyzed at 11 different spectrometers, distributed across four different geographical locations [167]. In their study, nine 600 MHz Bruker Avance III HD spectrometers and two 600 MHz Bruker Avance III spectrometers were used for NMR acquisition. Even though the two labs analyzing samples for paper III followed the same protocol for sample handling, there were apparent differences in the metabolic profiles across the two sites. Only five serum metabolites had CVs < 15 %, when calculated using QC samples analyzed at the two labs, but when calculated separately for each lab, the median was CVT = 21.6 % and CVG = 56.4

%, and 14 and 6 metabolites had CV < 15 % from the Trondheim and Germany batches, respectively.

Effort has been made to understand what caused this batch effect, however no certain explanation has been found. Some of our hypothesis for this batch effect include pipetting procedures and different pipettes. Differences in the shim may also influence the B.I.Quant models, which are based on the lineshapes of the metabolite signals. Also, NMR analyses were carried out on a Bruker Avance III HD Ultrashield Plus 600 MHz spectrometer equipped with a 5 mm TCI probe in Trondheim, while at the lab in Germany they were carried out on an Avance-IVDr spectrometer. Even though the equipment at both labs has been calibrated for use of the same protocol, it is possible that the difference in the type of probe used is the origin of the batch effect [216].

The presence of a batch effect was clearly evident when the concentrations of the QC samples were plotted alone, showing variability around two different concentrations. Interestingly, the batch effect was not systematic, meaning that concentrations from samples analyzed at one lab were not consistently lower or higher than from the lab, which rules out that the batch effect is due to systematic variation from pipetting or the types of pipettes. Figure 5.2 shows the raw concentrations of a number of lipoprotein parameters colored by the lab at which they were analyzed, together with the corresponding concentrations of QC samples. In this figure the concentrations of L6PN are clearly higher for samples analyzed in Trondheim compared to the ones in Germany, while the opposite is true for L3PN. Figure 5.3 shows PCA scores plots, for the first three PCs when applied to the concentrations of the lipoprotein parameters, clearly showing a batch effect.

As mentioned earlier, in an attempt to decrease the batch effect and the CVs of the serum metabolites, NMR spectra were imported into Matlab for manual quantification. The batch effect was also observed on the raw NMR spectra, thus quantification had to be performed on the two batches

5.2 Metabolite and lipoprotein quantification and reliability

separately. In particular, there was a difference in signal intensity and metabolite peaks experienced shifts at the spectral ppm scale which were not systematic (not in the same direction for the different metabolites). As described in Section 5.2.1, we were able to lower the CVs in Paper III substantially by quantifying manually. The resulting CVs were smaller than those reported in the multicenter study mentioned previously, in which CVs < 15 % are reported for 20 out of 24 detected metabolites [167].

These values have however been calculated using the mean concentration from each lab, while looking at all samples combined, the reported average CV is 40%, which is substantially higher than what we were able to obtain in Paper III. The panel of serum metabolites differs slightly across the studies mentioned in this section, of which the majority is overlapping.

Furthermore, since the automatically quantified metabolites had a batch effect, and we used the automatically quantified values of glucose to convert spectral integrals to absolute concentrations, the batch effect was necessarily propagated on the metabolite concentrations obtained by manual quantification. As the batch effect was not systematic, correcting for it could cause unwanted alterations in the metabolic profiles. For that reason, we decided not to correct for the batch effect, so that the naturally occurring composition of metabolites and lipoprotein parameters in the serum samples would not be altered. Lab was however included as a fixed effect in the LR models. Figure 5.4 (left) shows a PCA score plot of absolute metabolic concentrations, after a thorough manual quantification in Matlab, where points are colored according to the lab at which they have been analyzed, clearly visualizing a batch effect. Figure 5.4 (right) shows the PC1 loadings for each variables, showing that in specific dimethyl-glutarate, leucine, valine and lysine cause this effect. This is in good correspondence with the CV values for these metabolites, which were small when calculated separately for the two labs, but larger when combined, and are: for dimethyl-glutarate CVT = 6.2%, CVG = 6.2%, while combined CVC = 14.3 %; leucine: CVT = 2.9 %, CVG = 3.6 % & CVC = 11.5%; valine: CVT

= 2.4 %, CVG = 2.9 % & CVC = 8.5 %; lysine: CVT = 2.4 %, CVG = 2.9 % & CVC = 4.6 %.

Figure 5.2 A selection of lipoprotein parameters from the HUNT2 cohort. Navy: samples analyzed in Trondheim;

Turquoise: samples analyzed in Germany; Yellow: QC samples analyzed in Trondheim; Red: QC samples analyzed in Germany.

5.2 Metabolite and lipoprotein quantification and reliability

Figure 5.3 PCA plots of the lipoprotein parameters of samples in the HUNT2 cohort, colored according to the lab at which they have been analyzed. Left: scores on PC1 and PC2; Right: scores on PC2 and PC3. The batch-effect on the serum metabolic profiles is seen as a shift in the PC3 scores. Navy: Samples analyzed in Trondheim;

Turquoise: samples analyzed in Germany; Yellow: QC samples run in Trondheim; Red: QC samples run in Germany.

Figure 5.4 Scores on PC1 and PC2 when performing PCA on the serum metabolic profiles of samples in the HUNT2 cohort, colored according to the lab at which they have been analyzed. Left: scores on PC1 and PC2; Navy:

Samples analyzed in Trondheim; Turquoise: samples analyzed in Germany; Yellow: QC samples run in Trondheim;

Red: QC samples run in Germany; 1: formate; 2: creatine; 3: lactate; 4: glycine; 5: methanol; 6: dimethyl-sulfone;

7: ornithine; 8: methionine; 9: glutamine; 10: citrate; 11: acetate; 12: acetoacetate; 13: glutamate; 14: pyruvate;

15: alanine; 16: ethanol; 17: isoleucine; 18: 2-methylglutarate; 19: leucine; 20: phenylalanine; 21: glucose; 22:

tyrosine; 23: creatinine; 24: valine; 25: proline-betaine; 26: histidine; 27; lysine; 28: 3-hydroxybutyrate.

Data analysis

For examining disease characteristics in a metabolomics study, multivariate analysis methods are frequently applied, largely due to the nature of the chemical signals [217]. The main advantage of these methods is their ability to identify patterns of several metabolites simultaneously. Especially PLS-DA is commonly used for creating discriminatory models [218, 219] and PCA for identifying groups in data. This is because the data often contains few samples compared to the number of variables and because these methods are able to overcome the multicolinearity problem. These methods utilize the correlation structure of the data to extract the principal components [138] and project the multivariate data onto a lower-dimensional space, and were used extensively in this thesis (PCA was used in all papers, while PLS-DA was used in Papers I and III).