Jasper Engel

Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling

IntroductionThe 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.ObjectivesCurrently, 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.MethodsWe 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.ResultsOur studies have shown that once data are filtered, missing values are not correlated with m/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.ConclusionThe 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.

Confirmation of brand identity of a trappist beer by mid-infrared spectroscopy coupled with multivariate data analysis

Authentication of foods is of importance both to consumers and producers for e.g. confidence in label descriptions and brand protection, respectively. The authentication of beers has received limited attention and in most cases only small data sets were analysed. In this study, Fourier-transform infrared attenuated total reflectance (FT-IR ATR) spectroscopy was applied to a set of 267 beers (53 different brands) to confirm claimed identity for samples of a single beer brand based on their spectral profiles. Skewness-adjusted robust principal component analysis (ROBPCA) was deployed to detect outliers in the data. Subsequently, extended canonical variates analysis (ECVA) was used to reduce the dimensionality of the data while simultaneously achieving maximum class separation. Finally, the reduced data were used as inputs to various linear and non-linear classifiers. Work focused on the specific identification of Rochefort 8° (a Trappist beer) and both direct and indirect (using an hierarchical approach) identification strategies were studied. For the classification problems Rochefort vs. non-Rochefort, Rochefort 8° vs. non-Rochefort 8° and Rochefort 8° vs. Rochefort 6° and 10°, correct prediction abilities of 93.8%, 93.3% and 97.3%, respectively were achieved.

Regularized MANOVA (rMANOVA) in untargeted metabolomics.

Many advanced metabolomics experiments currently lead to data where a large number of response variables were measured while one or several factors were changed. Often the number of response variables vastly exceeds the sample size and well-established techniques such as multivariate analysis of variance (MANOVA) cannot be used to analyze the data. ANOVA simultaneous component analysis (ASCA) is an alternative to MANOVA for analysis of metabolomics data from an experimental design. In this paper, we show that ASCA assumes that none of the metabolites are correlated and that they all have the same variance. Because of these assumptions, ASCA may relate the wrong variables to a factor. This reduces the power of the method and hampers interpretation. We propose an improved model that is essentially a weighted average of the ASCA and MANOVA models. The optimal weight is determined in a data-driven fashion. Compared to ASCA, this method assumes that variables can correlate, leading to a more realistic view of the data. Compared to MANOVA, the model is also applicable when the number of samples is (much) smaller than the number of variables. These advantages are demonstrated by means of simulated and real data examples. The source code of the method is available from the first author upon request, and at the following github repository: https://github.com/JasperE/regularized-MANOVA.

Towards the Disease Biomarker in an Individual Patient Using Statistical Health Monitoring

In metabolomics, identification of complex diseases is often based on application of (multivariate) statistical techniques to the data. Commonly, each disease requires its own specific diagnostic model, separating healthy and diseased individuals, which is not very practical in a diagnostic setting. Additionally, for orphan diseases such models cannot be constructed due to a lack of available data. An alternative approach adapted from industrial process control is proposed in this study: statistical health monitoring (SHM). In SHM the metabolic profile of an individual is compared to that of healthy people in a multivariate manner. Abnormal metabolite concentrations, or abnormal patterns of concentrations, are indicated by the method. Subsequently, this biomarker can be used for diagnosis. A tremendous advantage here is that only data of healthy people is required to construct the model. The method is applicable in current–population based –clinical practice as well as in personalized health applications. In this study, SHM was successfully applied for diagnosis of several orphan diseases as well as detection of metabotypic abnormalities related to diet and drug intake.

An overview of large-dimensional covariance and precision matrix estimators with applications in chemometrics

The covariance matrix (or its inverse, the precision matrix) is central to many chemometric techniques. Traditional sample estimators perform poorly for high‐dimensional data such as metabolomics data. Because of this, many traditional inference techniques break down or produce unreliable results. In this paper, we selectively review several modern estimators of the covariance and precision matrix that improve upon the traditional sample estimator. We focus on 3 general techniques: eigenvalue‐shrinkage estimation, ridge‐type estimation, and structured estimation. These methods rely on different assumptions regarding the structure of the covariance or precision matrix. Various examples, in particular using metabolomics data, are used to compare these techniques and to demonstrate that in concert with, eg, principal component analysis, multivariate analysis of variance, and Gaussian graphical models, better results are obtained.

Application of a cocktail approach to screen cytochrome P450 BM3 libraries for metabolic activity and diversity

AbstractIn the present study, the validity of using a cocktail screening method in combination with a chemometrical data mining approach to evaluate metabolic activity and diversity of drug-metabolizing bacterial Cytochrome P450 (CYP) BM3 mutants was investigated. In addition, the concept of utilizing an in-house-developed library of CYP BM3 mutants as a unique biocatalytic synthetic tool to support medicinal chemistry was evaluated. Metabolic efficiency of the mutant library towards a selection of CYP model substrates, being amitriptyline (AMI), buspirone (BUS), coumarine (COU), dextromethorphan (DEX), diclofenac (DIC) and norethisterone (NET), was investigated. First, metabolic activity of a selection of CYP BM3 mutants was screened against AMI and BUS. Subsequently, for a single CYP BM3 mutant, the effect of co-administration of multiple drugs on the metabolic activity and diversity towards AMI and BUS was investigated. Finally, a cocktail of AMI, BUS, COU, DEX, DIC and NET was screened against the whole in-house CYP BM3 library. Different validated quantitative and qualitative (U)HPLC-MS/MS-based analytical methods were applied to screen for substrate depletion and targeted product formation, followed by a more in-depth screen for metabolic diversity. A chemometrical approach was used to mine all data to search for unique metabolic properties of the mutants and allow classification of the mutants. The latter would open the possibility of obtaining a more in-depth mechanistic understanding of the metabolites. The presented method is the first MS-based method to screen CYP BM3 mutant libraries for diversity in combination with a chemometrical approach to interpret results and visualize differences between the tested mutants. Graphical abstractGeneral worklfow in screening mutant enzyme libraries for catalytic efficiency and diversity

Evaluation of metabolomic changes during neoadjuvant chemotherapy combined with bevacizumab in breast cancer using MR spectroscopy

IntroductionMetabolomics investigates biochemical processes directly, potentially complementing transcriptomics and proteomics in providing insight into treatment outcome.ObjectivesThis study aimed to use magnetic resonance (MR) spectroscopy on breast tumor tissue to explore the effect of neoadjuvant therapy on metabolic profiles, determine metabolic effects of the antiangiogenic drug bevacizumab, and investigate metabolic differences between responders and non-responders.MethodsBreast tumors from 122 patients were profiled using high resolution magic angle spinning MR spectroscopy. All patients received neoadjuvant chemotherapy, and were randomized to receive bevacizumab or not. Tumors were biopsied prior, during, and after treatment.ResultsPrincipal component analysis showed clear metabolic changes indicating a decline in glucose consumption and a transition to normal breast adipose tissue as an effect of chemotherapy. Partial least squares-discriminant analysis revealed metabolic differences between pathological minimal residual disease patients and pathological non-responders after treatment (accuracy of 77%, p < 0.001), but not before or during treatment. Lower glucose and higher lactate was observed in patients exhibiting a good response (≥90% tumor reduction) compared to those with no response (≤10% tumor reduction) before treatment, while the opposite was observed after treatment. Bevacizumab-receiving and chemotherapy-only patients could not be discriminated at any time point. Linear mixed-effects models revealed a significant interaction between time and bevacizumab for glutathione, indicating higher levels of this antioxidant in chemotherapy-only patients than in bevacizumab receivers after treatment.ConclusionMR spectroscopy showed potential in detecting metabolic response to treatment and complementing other molecular assays for the elucidation of underlying mechanisms affecting pathological response.

Application of passive sampling to characterise the fish exometabolome

The endogenous metabolites excreted by organisms into their surrounding environment, termed the exometabolome, are important for many processes including chemical communication. In fish biology, such metabolites are also known to be informative markers of physiological status. While metabolomics is increasingly used to investigate the endogenous biochemistry of organisms, no non-targeted studies of the metabolic complexity of fish exometabolomes have been reported to date. In environmental chemistry, Chemcatcher® (Portsmouth, UK) passive samplers have been developed to sample for micro-pollutants in water. Given the importance of the fish exometabolome, we sought to evaluate the capability of Chemcatcher® samplers to capture a broad spectrum of endogenous metabolites excreted by fish and to measure these using non-targeted direct infusion mass spectrometry metabolomics. The capabilities of C18 and styrene divinylbenzene reversed-phase sulfonated (SDB-RPS) Empore™ disks for capturing non-polar and polar metabolites, respectively, were compared. Furthermore, we investigated real, complex metabolite mixtures excreted from two model fish species, rainbow trout (Oncorhynchus mykiss) and three-spined stickleback (Gasterosteus aculeatus). In total, 344 biological samples and 28 QC samples were analysed, revealing 646 and 215 m/z peaks from trout and stickleback, respectively. The measured exometabolomes were principally affected by the type of Empore™ (Hemel Hempstead, UK) disk and also by the sampling time. Many peaks were putatively annotated, including several bile acids (e.g., chenodeoxycholate, taurocholate, glycocholate, glycolithocholate, glycochenodeoxycholate, glycodeoxycholate). Collectively these observations show the ability of Chemcatcher® passive samplers to capture endogenous metabolites excreted from fish.

Dissimilarity based Partial Least Squares (DPLS) for genomic prediction from SNPs

BackgroundGenomic prediction (GP) allows breeders to select plants and animals based on their breeding potential for desirable traits, without lengthy and expensive field trials or progeny testing. We have proposed to use Dissimilarity-based Partial Least Squares (DPLS) for GP. As a case study, we use the DPLS approach to predict Bacterial wilt (BW) in tomatoes using SNPs as predictors. The DPLS approach was compared with the Genomic Best-Linear Unbiased Prediction (GBLUP) and single-SNP regression with SNP as a fixed effect to assess the performance of DPLS.ResultsEight genomic distance measures were used to quantify relationships between the tomato accessions from the SNPs. Subsequently, each of these distance measures was used to predict the BW using the DPLS prediction model. The DPLS model was found to be robust to the choice of distance measures; similar prediction performances were obtained for each distance measure. DPLS greatly outperformed the single-SNP regression approach, showing that BW is a comprehensive trait dependent on several loci. Next, the performance of the DPLS model was compared to that of GBLUP. Although GBLUP and DPLS are conceptually very different, the prediction quality (PQ) measured by DPLS models were similar to the prediction statistics obtained from GBLUP. A considerable advantage of DPLS is that the genotype-phenotype relationship can easily be visualized in a 2-D scatter plot. This so-called score-plot provides breeders an insight to select candidates for their future breeding program.ConclusionsDPLS is a highly appropriate method for GP. The model prediction performance was similar to the GBLUP and far better than the single-SNP approach. The proposed method can be used in combination with a wide range of genomic dissimilarity measures and genotype representations such as allele-count, haplotypes or allele-intensity values. Additionally, the data can be insightfully visualized by the DPLS model, allowing for selection of desirable candidates from the breeding experiments. In this study, we have assessed the DPLS performance on a single trait.