Double-check: validation of diagnostic statistics for PLS-DA models in metabolomics studies
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Partial Least Squares-Discriminant Analysis (PLS-DA) is a PLS regression method with a special binary ‘dummy’ y-variable and it is commonly used for classification purposes and biomarker selection in metabolomics studies. Several statistical approaches are currently in use to validate outcomes of PLS-DA analyses e.g. double cross validation procedures or permutation testing. However, there is a great inconsistency in the optimization and the assessment of performance of PLS-DA models due to many different diagnostic statistics currently employed in metabolomics data analyses. In this paper, properties of four diagnostic statistics of PLS-DA, namely the number of misclassifications (NMC), the Area Under the Receiver Operating Characteristic (AUROC), Q2 and Discriminant Q2 (DQ2) are discussed. All four diagnostic statistics are used in the optimization and the performance assessment of PLS-DA models of three different-size metabolomics data sets obtained with two different types of analytical platforms and with different levels of known differences between two groups: control and case groups. Statistical significance of obtained PLS-DA models was evaluated with permutation testing. PLS-DA models obtained with NMC and AUROC are more powerful in detecting very small differences between groups than models obtained with Q2 and Discriminant Q2 (DQ2). Reproducibility of obtained PLS-DA models outcomes, models complexity and permutation test distributions are also investigated to explain this phenomenon. DQ2 and Q2 (in contrary to NMC and AUROC) prefer PLS-DA models with lower complexity and require higher number of permutation tests and submodels to accurately estimate statistical significance of the model performance. NMC and AUROC seem more efficient and more reliable diagnostic statistics and should be recommended in two group discrimination metabolomic studies.
KeywordsPLS-DA AUROC DQ2 Q2 Misclassifications Diagnostic statistics Metabolomics
The goal of systems biology is to explore the interaction between various components in a biological system. Metabolomics measurements provide quantitative information on the metabolic level of the system. This metabolic level has proven an important area of systems biology with the aim to pinpoint putative metabolites related to disease, genetic variation or nutritional interventions (Weckwerth et al. 2004; Yang et al. 2004; Kind et al. 2007; van Velzen et al. 2008; Bernini et al. 2009).
In metabolomics studies different analytical platforms are often used to provide information on large groups of metabolites. Most metabolomics studies result in complex multivariate datasets with varying correlations between the measured metabolite levels so that multivariate data analysis methods are needed to explore these complex datasets.
In the search for metabolic biomarkers, multivariate discrimination models between two classes of subjects/samples are used. One of the most used methods is Partial Least Squares-Discriminant Analysis (PLS-DA) (Barker and Rayens 2003; van Velzen et al. 2008). If a statistically significant discrimination between two classes e.g. the cases and controls classes can be found, then the model parameters can be interpreted for their discriminating power and metabolic biomarkers can be found. In PLS-DA models, a relationship between the metabolomics data and the categorical variable y is developed in such a way that categorical variable values can be predicted for samples of unknown origin given the metabolomics data. Here, the categorical variable y is a vector which values indicate class membership of each sample included in the study e.g. a vector with values of −1 and 1 where −1 represents each sample belonging to the class of controls and 1 represents each sample belonging to the class of cases. However, due to the properties of regression models, the prediction ŷi of the i-th element of y can take any value, not necessarily exactly −1 or 1. Translation of these values of ŷ to class membership (classification procedure) is a critical point of PLS-DA analysis and can be done, e.g. by applying a threshold above which the sample will be assigned to the cases class and below to the control class.
Another challenge of PLS-DA analysis is the accurate estimation of the quality of the obtained models and thereby differences between two classes. Many diagnostic statistics have been introduced over the time to convert values of ŷ obtained for all the study samples into a single number representing the overall quality of the discrimination model. In this paper we investigate the performance of the four different diagnostic statistics which are usually used for this purpose in metabolomics when PLS-DA is applied. They are: the number of misclassifications (NMC), the Area Under the Receiver Operating Characteristics (AUROC), Q2 and Discriminant Q2 (DQ2). The natures of these diagnostic statistics are very different. Whereas the Q2/DQ2 are derived directly from the (ratio-scaled) model predictions ŷ of y, the NMC/AUROC are derived from the (nominal-scaled) class memberships translated from ŷ. It is debatable which measurement scale should be used for diagnostic statistic of PLS-DA (Stevens 1946).
The power of each of diagnostic statistics is investigated in terms of its ability to provide a statistically significant measure of the discrimination between two classes of subjects (e.g. the cases and the controls) when known multivariate effects of different magnitudes are present in the data. This is accomplished by superimposing known multivariate effects of increasing magnitude on the metabolic profiles of subjects from the cases class and calculating the PLS-DA models: one PLS-DA model per each data set with different magnitude of superimposed effect and diagnostic statistics used. In order to obtain unbiased estimates of model performance, PLS-DA is applied in a double cross validation scheme. This means that the four diagnostic statistics are used not only to assess the final quality of the PLS-DA models but also for the optimization of the model, e.g. to select the optimal complexity of model (optimal number of latent variables, #LV). Statistical significance of each PLS-DA model is estimated by comparing the value of the diagnostic statistics (Q2, DQ2, NMC or AUROC) to values of its null reference distribution H0 obtained by permutation tests.
Datasets obtained by two different analytical platforms commonly used in the metabolomics studies: UPLC-MS and NMR were used to evaluate properties of the four diagnostic statistics. The multivariate effects superimposed into data sets were intended to represent two situations that can occur in real life metabolomics data analysis: investigating a nutritional effect (in the case of the UPLC-MS data set) and investigating an effect of exposure to a chemical pollutant (in the case of the NMR data set). Moreover, datasets of different size were used to draw general conclusions independent of data set size.
2.1 PLS-DA modeling with a double-cross validation scheme
Partial least squares discriminant analysis (PLS-DA) and its extensions like multilevel PLS-DA (MPLS-DA, (van Velzen et al. 2008)) and orthogonal PLS-DA (OPLS-DA, (Trygg and Wold 2002)) are the most used classification methods in metabolomics. PLS-DA consists of a classical PLS regression where the dependent variable y is categorical and represents samples class membership e.g. y can be a vector with values of −1 and 1 where −1 represents each sample belonging to the class of controls and 1 represents each sample belonging to the class of cases (Barker and Rayens 2003). By making use of class information, PLS-DA tends to improve the separation between the (two) groups of samples.
2.1.2 PLS-DA with double cross validation schema
Two steps are critical when building a PLS-DA model: the selection of the optimal model complexity e.g. optimal number of latent variables (#LV) and the assessment of the overall quality of the model. In the PLS-DA context, the #LV needs to be optimized in such a way that a suitable number of latent variables is used to build the model. Suitable means that it provides the best description of data thus the best discrimination between samples from two different classes.
Model optimization (i.e. selection of the optimal #LV) and model quality assessment should be always carried out in a double cross validation schema because then assessment of model quality and the model optimization are independent. Samples which are used in final model assessment are not used in the model optimization (calibration): moreover the calibration of the model is carried on in a similar unbiased way (Smit et al. 2007; Westerhuis et al. 2008).
Training, validation and test sets (in both CV1 and CV2 loops) are defined by partitioning the samples in k disjoint subsets. In this study, k = 8 was chosen for the outer loop (CV2) and k = 7 for the inner loop (CV1). This is the most commonly used partition in double cross validation procedure applied to metabolomic data sets. Samples of both classes were always represented in a 1:1 ratio in test, validation and training sets.
As many different disjoint partitions of a data set are possible, the overall procedure was repeated M times (30 in the case of the UPLC-MS dataset and 20 in the case of the NMR data set) resulting in M submodels. That gives M repetitions of the ŷ vector: ŷ1,…, ŷM. (Fig. 1b). This procedure enables to track the reproducibility of the PLS-DA output (see Sect. 4.2.3). The final measures of quality are given as average values over the M values of chosen diagnostics statistics. The choice of an 8:7 data split and M = 30 (20) is a tradeoff between accuracy and computational time.
2.2 Diagnostic statistics
2.2.1 The Q2 statistic
The Q2 is de facto the default diagnostic statistic to validate PLS-DA models in metabolomics included in commercial or academic statistical packages like SIMCA (Umetrics Inc, Kinnelon NJ), the PLS-toolbox for Matlab (Eigenvector Research Inc, Wenatchee WA), SAS (SAS Institute Inc, Cary NC) or Metaboanalyst (Xia et al. 2009).
In a PLS regression the values of ŷi are not bounded in the range [−1, +1] but, in principle, can assume any value in the range [−∞, +∞]. Any deviation of ŷi from yi contributes to the PRESS: for instance, a prediction of ŷi = −2 for a sample with yi = −1 will result in a contribution of 12 to the PRESS even if this corresponds to a correct classification when the discrimination border is set at ŷi = 0. The same happens if a prediction of 0 (ŷi = 0) is given to this sample, then the contribution to the PRESS is still 1 = (−1)2. This drawback is (partially) overcome by the so called Discriminant Q2 (DQ2).
2.2.2 Discriminant Q2 statistics, DQ2
This correction is effective only when the prediction is in the direction of the true class label, for instance when a sample with yi = −1 is predicted to be ŷi = −1.5. If this sample is predicted with ŷi = 0 or +1, the prediction error contributes to the PRESSD. It is then clear that the larger the prediction error, the larger the PRESSD which in turn implies a smaller value of DQ2.
2.2.3 Number of misclassifications (NMC)
In the PLS-DA predicted values of ŷi can be transformed into a class membership (i.e. cases/controls) by relating them to a set discrimination threshold (classification boundary). This threshold is usually set at 0 when two classes have similar size and variance and when y is a vector of −1 (for samples from class of controls) and 1 (for samples from class of cases). If these conditions are not met the discriminative threshold can be adjusted to other values (Lloyd et al. 2009). The predicted values ŷi for the i-th sample is related to the 0 threshold: the sample is assigned to class of cases if ŷi ≥ 0 or to class of controls if ŷi < 0. The assigned class is then compared with the true class membership and classified either as a True Positive (TP), a True Negative (TN), a False Positive (FP) or a False Negative (FN). When all samples have been predicted and assigned to a class, the total number of True Negatives, False Positives, False Negatives, and True Positives can be computed to create a Confusion Matrix (Broadhurst and Kell 2006) (see also Supplementary Fig. 1) which summarizes the prediction ability of the model.
The NMC is the most intuitive of all diagnostic statistics as it simply indicates the number of samples which are wrongly classified by the model.
2.2.4 Area under the receiver operator characteristic
Apart from the NMC, several criteria can be derived from the confusion matrix (Lloyd et al. 2009) and the specificity (Sp) and the sensitivity (Se) (Altman and Bland 1994) are two of the mostly used, especially in assessing the performance of diagnostic tests.
The sensitivity is a measure of how well the model is able to correctly classify samples of the class of cases, while the specificity measures how well the model can predict samples from the class of controls. The Receiver Operator Characteristic (ROC) (Fawcett 2004; Davis and Goadrich 2006) combines these two parameters. By plotting the sensitivity against 1-specificity for different values of the discrimination threshold a ROC curve can be defined. The ROC curve provides a spectrum of performance assessments and the area under the ROC (AUROC) is commonly used as diagnostic statistics of PLS-DA models. The AUROC values range from 1 (perfect discrimination between classes) and 0 (0.5 and lower usually means no discrimination at all).
2.2.5 Differences between NMC/AUROC and Q2/DQ2
Class membership can be coded as 1 and −1 in categorical variable y: a sample belongs either to class 1 (e.g. cases) or −1 (e.g. controls). These classes could also have been indicated by class A and B showing that the numerical values 1 and −1 are irrelevant (they are only used as dummy variables). Predicted class memberships (ŷ) are also categorical variables and the NMC/AUROC statistics are directly derived from these memberships and are so-called permissible statistics (Stevens 1946). For instance, the interpretation of means and variances are problematic for categorical variables while they are well-defined for ratio-scaled variables.
The Q2 and Discriminant Q2 are derived from predictions (ŷi) and are allowable statistics if we assume that the ŷi values are ratio-scaled variables. It is interesting to note that the definition of Q2 and DQ2 relies on the calculation of the mean of the categorical vector y (Eq. 2), a statistic which is not permissible for categorical variables (Stevens 1946). This is a fundamental problem of using these statistics in the PLS-DA.
It appears that NMC and AUROC are not sensitive to the magnitude of the error ε while Q2 and DQ2 strongly depend on the magnitude ε. For example an error ε = 6 gives a lower Q2 than an error ε = 2 where NMC is equal to 1 for both errors. Values of Q2 (and DQ2) are sensitive to outliers with high errors ε.
2.2.6 Permutation test
Although an NMC = 0 or a Q2 = 0.99 can be thought to correspond to good models with a high discriminating power, these values of the diagnostic statistics can be attained purely by chance due to a lucky random choice of samples in the test, validation and training sets. This means that it is not known which value of these diagnostic statistics really corresponds to a good discrimination between groups (Westerhuis et al. 2008). To overcome these problems and to give a measure of the statistical significance of the diagnostic statistics (P-value), a permutation test was introduced (Lindgren et al. 1996; Golland et al. 2005; Mielke and Berry 2007; Pesarin and Salmaso 2010). Permutation tests assume that there is no difference among two groups that are randomly formed (Westerhuis et al. 2008). In a permutation test the labels of the samples are randomly permuted and a new classification model is calculated (Lindgren et al. 1996). The performance of the model obtained with is assessed by one of the four diagnostic statistics and the values of diagnostic statistics are expected to be higher than for original (unpermuted) data for NMC and lower for Q2, DQ2 and AUROC. By repeating this procedure N times, a null distribution of H0 for each of four diagnostic statistic is obtained. H0 is then a distribution of diagnostic statistics of models that are expected to be insignificant (Fisher 1937).
When using the permutation distribution to infer P-values, the left tail (in the case of NMC) and right tail (in the case of of Q2, DQ2 and AUROC) are of interest. This means that the number of permutations needs to be “large enough” to sample the tails of the distribution. The lower limit of the number of permutations is dictated by the required statistical significance: for instance, to attain a P-value <0.01 at least 100 permutations are necessary but cannot be sufficient to a proper sampling of the distributions tails. An optimal number is difficult to be inferred: (Churchill and Doerge 1994) suggested that to estimate a permutation P-value of 0.01 as many as 104 permutations are needed in genetics applications. The true permutation P-value can be calculated by taking into account all the possible permutations (Sun and Wright 2010) which is actually dictated by the number of samples: with N samples, N! are permutations possible. With N = 60 (the size of a typical small metabolomics dataset) there are >1080 possible permuted data sets that obviously cannot all be screened. On the other hand, a limited number of samples can hamper the sampling of the tails because extreme values of the distribution may not be detected. This issue is discussed further in Sect. 4.1.
3 Materials and methods
3.1 Data sets
3.1.1 UPLC-MS data set
The UPLC-MS data set consists of 96 samples × 101 lipids levels measured at the Demonstration and Competence Laboratory, Netherlands Metabolomics Centre at Leiden University, Leiden, The Netherlands. Technical details of the UPLC-MS lipidomics platform are described in (Hu, van Dommelen et al. 2008) and in the Supplementary Material. 96 samples are serum samples collected from healthy subjects before the start of the nutritional intervention study in the frame of BCL study (more information available on request). To 48 randomly selected samples nutritional effects were added as described in Sect. 3.1.3.
3.1.2 NMR data set
Ten different data sets, each consisting of 60 NMR spectra (small NMR data sets), have been constructed by randomly selecting the spectra from a pool of 256 homogenous NMR serum spectra of subjects of the DiOGenes study (Larsen et al. 2009). Technical details of 1H NMR spectra acquisition are presented in the Supplementary Material. Each small NMR dataset was composed of 60 spectra (samples) each with 420 data points. A multivariate effect has been subsequently added to the 30 spectra randomly selected from 60 spectra (the case group). Using the same strategy, ten larger NMR data sets (large NMR data sets), consisting of 200 NMR spectra (100 + 100) have also been generated.
3.1.3 Superimposed multivariate effects
220.127.116.11 Nutritional effects
Original multivariate nutritional effects were changes in levels of 101 lipids calculated for each of 33 healthy subjects participating in the nutritional study (group of 33 subjects with the largest nutritional effect in BCL study). For each of the 33 subjects changes between lipid levels before and after nutritional intervention were calculated. On that basis 33 different original multivariate nutritional effects were derived. Ten different magnitudes of these effects were obtained by multiplication of the original effects by constant numbers: 1 (original effects), 0.75, 0.626, 0.55, 0.5, 0.375, 0.25, 0.15, 0.1, 0.05. To each of 48 samples (randomly selected from UPLC-MS data set) one of these 33 multivariate nutritional effects (randomly selected) or their magnitudes were added. In that way ten different data sets with different magnitudes of superimposed nutritional effects were obtained. Each of them consisted of 48 lipid profiles with superimposed effects (the class of cases) and 48 lipid profiles without superimposed effects (the class of controls).
18.104.22.168 Exposure to a chemical pollutant
Aldrin, an isomer of hexachlorohexahydrodimethanonaphthalene, C12H8Cl6 (Martin 1958; Younos and Weigmann 1988) is an organochlorine pesticide whose use is severely limited in most countries and banned within the EU (http://www.pesticides.gov.uk/approvals.asp?id=55). Despite the strict regulation, the presence of this compound, as well of other organochlorine pollutants, has been reported in the sera of healthy subjects, suggesting that exposure to some organochlorine compounds is strongly related to environmental contamination (Lino and Silveira 2006; Carreño et al. 2007). (Lino and Silveira 2006) reported levels of Aldrin in the blood of healthy subjects ranging from <5 to 400 μg/l with an average concentration of 13 ± 42 μg/l.
The Aldrin spectrum was simulated for the average concentration of this compound in blood (13 μg/L) and was the linear combination of Lorentzian peaks as previously described (Günther and Gleason 1980; Cloarec et al. 2005). Aldrin resonance positions where retrieved from the SDBS online database (SDBSWeb: http://riodb01.ibase.aist.go.jp/sdbs/ (National Institute of Advanced Industrial Science and Technology, accessed in August 2010). A simulated NMR spectrum of Aldrin contains 53 out of 420 data points which are not equal to zero.
Exposure to this pollutant was introduced by superimposing the simulated NMR spectrum of Aldrin to the NMR spectra of serum samples of healthy subjects from the group of cases (randomly selected subjects: 30 out of 60 for small NMR data sets and 100 out of 200 for large NMR data sets). For the small NMR data sets the magnitudes of pollutant levels were chosen to range from 0 (no exposure to pollutant) to 50 times (0, 10, 15, 20, 25, 30, 35, 40, 45, 50) of the average observed concentration of Aldrin in blood. For the large NMR data sets, the exposure intensity ranged from 0 to 20 times (0, 2, 2.5, 5, 7.5, 10, 12.5, 15, 17.5, 20) of average concentration of Aldrin in blood.
3.2 Data analysis procedure and software
PLS-DA with a double cross validation procedure and four different diagnostic statistics was used. This procedure was applied M-times to each of UPLC-MS and NMR data sets with superimposed nutritional or exposure effects (10 UPLC-MS data sets, 100 small NMR data sets and 100 large NMR data sets). That resulted in M submodels for each data set (M = 30 for each of UPLC-MS data set and M = 20 for each of NMR data set). The performance of the PLS-DA model of each data set was evaluated on the basis of means of diagnostic statistics calculated across M submodels (see Fig. 1b) and related to means of diagnostic statistics of permutation tests using Eq. 7 to obtain P-value (for more information see Supplementary Material). A number of 3000 permutation tests for each of UPLC-MS data sets and 2000 permutation tests for each of NMR data sets were calculated using the same procedure as described above but with permuted y. All analyses were done in Matlab 2010a (The Mathworks Inc., Natick, Massachusetts, USA), using in-house routines, partly based on the PLS Toolbox (Eigenvector Research Inc, Wenatchee WA). Permutation tests have been performed on the LISA-SARA Dutch super-computer (www.sara.nl).
4 Results and discussion
4.1 Statistical significance of PLS-DA models vs. magnitudes of superimposed effects and used diagnostic statistics
Performance of PLS-DA model depends not only on the data set used, thus differences between two classes (e.g. magnitude of nutritional effects present in lipid profiles of subjects from the class of cases). It can also depend on values of diagnostic statistics used in optimization and performance assessment of the PLS-DA model (see Fig. 1). When differences between two classes are becoming very small, the power of each of the diagnostic statistics can be easily investigated in terms of their ability to provide a statistically significant measure of the discrimination between the two classes. This is accomplished by superimposing known multivariate effects of decreasing magnitude onto data of subjects from the class of cases and calculating a series of PLS-DA models. In this series, a single PLS-DA model is obtained for each of many data sets with different magnitudes of superimposed effects and one of four diagnostic statistics used in optimization and performance assessment. The most powerful diagnostic statistic is the one which provides a statistically significant PLS-DA model calculated for data with the smallest superimposed effect.
4.1.1 UPLC-MS data sets with superimposed nutritional effects
Performance of PLS-DA models of UPLC-MS data sets (96 samples and 101 metabolites) with different magnitudes of superimposed effects
Effect magnitude/diagnostic statistics
Interestingly, the DQ2 and Q2P-values for very small effects are not equal to 0.5. This fact may be related either to inadequate number of PLS-DA submodels or to an undersampling of the DQ2 and Q2 H0 distributions due to a limited number of permutations. The number of PLS-DA submodels (30 in our case) can be insufficient to obtain a representative mean value of the DQ2 and Q2 statistics. That is highly probable when distribution of 30 values is not symmetric. On the other hand, distributions of diagnostic statistics in permutation tests can also be essential in estimating P-value. Distributions of permutation tests of ŷi and diagnostic statistics for models of UPLC-MS data set with 0.75 × effect were plotted in Supplementary Fig. 2. Shapes of distributions of permutation tests of NMC and AUROC are symmetric in contrary to DQ2 and Q2 distributions which are left-side skewed. Distributions of permutation tests of DQ2 and Q2 should be chi-square distributions because they are distributions of sum of squares (Eqs. 3 and 5) but there are not many values of permutation tests in the right tail of those H0 distributions when 3000 permutation tests are used. That makes an accurate estimation of the P-value of diagnostic statistics such as DQ2 and Q2 of the original models difficult and raises a question about the number of submodels and permutation tests required to properly estimate P-values. Another solution can be to apply resampling methods such as bootstrap in combination with permutation testing.
4.1.2 NMR data sets with superimposed exposure effect
Performance of PLS-DA models of small NMR data sets (60 samples and 420 data points) with different magnitudes of superimposed effects
Effect magnitude/diagnostic statistics
Interestingly, the averaging over ten different data sets leads to P-values ≈0.5 for the Q2 and DQ2 when no effect is present, as it should be when no differences between classes is expected. That was not the case for the UPLC-MS data sets where only one data set is used for each effect magnitude. There number of PLS-DA models and permutation tests was not enough to properly estimate P-values.
4.2 Properties of PLS-DA models and diagnostic statistics
In order to explain observed differences in the performances of the models optimized and assessed by different diagnostic statistics, other properties of obtained models were evaluated further. Models complexity, distributions and reproducibility of models predictions were studied.
4.2.1 Complexity of PLS-DA models in CV1
The complexity of the PLS-DA models has a direct impact on model interpretation. PLS-DA models can be used for biomarker discovery, for instance by looking at the relative importance variables used in the PLS-DA model. This can done by ranking the variables according the value of their PLS regression coefficients: the variable with the largest (in absolute value) coefficient gets rank 1, the second one rank 2 and so on (Breitling, Armengaud et al. 2004)
In case of NMR data sets where simulated Aldrin spectra was added, 53 biomarkers associated with exposure to Aldrin (non-zero data points of Aldrin spectrum) are expected to be found by the PLS-DA models. Figure 4b shows the ranks of those 53 variables (for small NMR dataset with magnitude of added effects equal 45) for six PLS-DA models with different model complexity (from 1 to 6 latent variables). Each horizontal line presents a rank of one of 53 biomarkers. Minimal rank is in this case 1 (the most important variable out of 420 variables used in PLS-DA model) and maximal rank is 420 (the least important variable out of 420 variables used in PLS-DA model). Statistical significance of presented ranks was assessed by 10000 permutation tests and the corresponding P-values were calculated as detailed in Sect. 2.2.6. Ranks of biomarkers which obtained a P-value <0.05 are marked in blue and those with P-value >0.05 are marked in red.
Figure 4b shows that the complexity of the model does influence ranks of variables but most importantly influences statistical significance of variables with low ranks (see variables with rank 1–30). It appears that simple models built with fewer latent variables (LV from 1 to 3, as those usually selected by Q2/DQ2 in CV1) fail in providing statistical significance for a great number of these low rank variables, thus those variables will be omitted during biomarker selection. On the contrary, models built with more latent variables (LV from 4 to 6, as those usually selected by NMC/AUROC in CV1) are able to provide statistical significance to those most important variables. In this light it appears that more complex models (selected by NMC/AUROC) provide not only better discrimination of case and control group but also are more informative and accurate in term of biomarker discovery.
4.2.2 Distribution of predicted class membership vs. model complexity in CV1
When NMC and AUROC values are calculated it is only important on which side of discrimination threshold value is ŷi (< or >0) but not its value itself. In this way the values of ŷi greater than 1 and lower than −1 do not influence NMC and AUROC values more than the values of ŷi between −1 and 1 do. This is in contrary to Q2 and DQ2 which do not base on threshold value but on prediction error between values of ŷi and yi treating their values as values of quantitative variable. In this case, the values of ŷi greater than 1 and lower than −1 do increase prediction error and decrease values of Q2 (DQ2) prominently. Complex PLS-DA models (#LV > 3) have wider ranges of ŷi, greater prediction error and lower values of Q2 and DQ2. That explains why when DQ2 and Q2 are used in the model optimization in CV1 less complex models are selected. When DQ2 and Q2 are used, the phrase “better safe than sorry” is followed. Model with the smallest prediction error e.g. the majority of ŷi in a “safe” range −1 to 1 is selected and a number of samples with correctly predicted class labels is not taken into account.
4.2.3 Reproducibility of predictions of PLS-DA models in CV2
As detailed in Sect. 2.1.2 (see also Fig. 1b), for UPLC-MS data set 30 different prediction vector ŷ of the original class membership vector y are generated by 30 submodels after CV2 procedure. That assures that the finally considered ŷ is independent of random combinations of samples used in double cross-validation procedure. Reproducibility of ŷs across different submodels can be easily employed in describing PLS-DA models stability. For each study sample the variance across 30 prediction values ŷi of different submodels could be estimated and used in assessment of PLS-DA model stability.
In conclusion, for small statistically insignificant effects in data sets, Q2/DQ2 optimized models tend to give very reproducible predictions what is in contrary to less reproducible predictions NMC and AUROC optimized models. That indicates that Q2/DQ2 optimized models are more stable and conservative than NMC and AUROC optimized models. This property can be associated with lower complexity of those models described in Sects. 4.2.1 and 4.2.2.
5 Conclusion remarks
NMC, AUROC and DQ2, Q2 belong to two separate groups of diagnostic statistics used in optimization and performance assessment of PLS-DA models. Several theoretical and practical differences between those diagnostic statistics were presented in this paper.
PLS-DA models using NMC or AUROC as diagnostic statistics are more powerful in detecting small differences between two groups than models using DQ2 or Q2. This phenomenon is related to two factors: complexity of PLS-DA models optimized during CV1 and distributions of submodels and permutation tests used to calculate P-value. During CV1, due to assumptions of (D)Q2 diagnostics statistics, models with lowest prediction error of class membership are selected and these are not always the models with best discrimination power. Additionally, number of PLS-DA submodels as well as number of permutation tests sufficient for estimation P-values of NMC and AUROC is usually not enough to properly estimate P-values of DQ2 or Q2. Finally, PLS-DA models with NMC or AUROC as diagnostic statistics are more accurate in finding biomarkers responsible for two classes discrimination with PLS-DA method.
Our recommendation for metabolomic studies with two classes discrimination problem is to use NMC or AUROC as diagnostic statistics of PLS-DA models.
This project was financed by the Netherlands Metabolomics Centre (NMC) which is a part of the Netherlands Genomics Initiative/Netherlands Organisation for Scientific Research. Authors gratefully acknowledge Ferdi van Dorsten (NMC, Unilever), Rob Vreeken, Jorne Troost and Iryna Paliukhovich (NMC, LACDR/Leiden University) for providing UPLC-MS data set and Jacques Vervoort (NMC, Wageningen University) for providing NMR data set. John Donners and Willem Vermin (SARA, the Dutch National High Performance Computing and e-Science Support Center, www.sara.nl) are also acknowledged for the help in implementing multi-thread calculations.
This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
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