Editorial–data analysis in metabolomics

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Metabolomics variable selection and classification in the presence of observations below the detection limit using an extension of ERp

A compressed folder (XERp Software.zip) containing the Matlab scripts to perform XERp as well as an example application. (ZIP 11 kb

Strategic responses to global challenges: The case of European banking, 1973–2000

In applying a strategy, structure, ownership and performance (SSOP) framework to three major clearing banks (ABN AMRO, UBS, Barclays), this article debates whether the conclusions generated by Whittington and Mayer about European manufacturing industry can be applied to the financial services sector. While European integration plays a key role in determining strategy, it is clear that global factors were far more important in determining management actions, leading to significant differences in structural adaptation. The article also debates whether this has led to improved performance, given the problems experienced with both geographical dispersion and diversification, bringing into question the quality of decision-making over the long term

Vasopressin release is enhanced by the Hemocontrol biofeedback system and could contribute to better haemodynamic stability during haemodialysis

Haemodialysis with the Hemocontrol biofeedback system (HHD) is associated with improved haemodynamic stability compared with standard haemodialysis (HD) (SHD). Although the beneficial effect of HHD on haemodynamic stability is generally explained by its effect on blood volume, we questioned whether additional factors could play a role. Since HHD is associated with higher initial dialysate sodium concentrations and ultrafiltration (UF) rate, we studied whether the beneficial effect of HHD on haemodynamic stability may be explained by an increased release of the vasoconstrictor arginine vasopressin (AVP). Fifteen chronic dialysis patients underwent SHD and HHD in random order. All other treatment factors were identical and patients served as their own control. Plasma levels of AVP were measured pre-dialysis, at 30 and 60 min intra-dialysis and, next, hourly until completion of the dialysis session. Plasma AVP levels did not change significantly during SHD, whereas AVP levels rose significantly within 30 min after the start of HHD (P 0.01). AVP levels were significantly higher at 30 and 60 min of HHD in comparison with SHD (P 0.05). Dialysis hypotension occurred significantly less frequent during HHD than during SHD (P 0.05). HHD is associated with higher initial AVP levels compared with SHD. The enhanced release of the vasoconstrictor AVP with HHD could contribute to the lower frequency of dialysis hypotension by facilitating fluid removal during the first part of the dialysis session, permitting lower UF rates during the second half of the dialysis session

Multivariate paired data analysis: multilevel PLSDA versus OPLSDA

Metabolomics data obtained from (human) nutritional intervention studies can have a rather complex structure that depends on the underlying experimental design. In this paper we discuss the complex structure in data caused by a cross-over designed experiment. In such a design, each subject in the study population acts as his or her own control and makes the data paired. For a single univariate response a paired t-test or repeated measures ANOVA can be used to test the differences between the paired observations. The same principle holds for multivariate data. In the current paper we compare a method that exploits the paired data structure in cross-over multivariate data (multilevel PLSDA) with a method that is often used by default but that ignores the paired structure (OPLSDA). The results from both methods have been evaluated in a small simulated example as well as in a genuine data set from a cross-over designed nutritional metabolomics study. It is shown that exploiting the paired data structure underlying the cross-over design considerably improves the power and the interpretability of the multivariate solution. Furthermore, the multilevel approach provides complementary information about (I) the diversity and abundance of the treatment effects within the different (subsets of) subjects across the study population, and (II) the intrinsic differences between these study subjects

Lithium surveillance by community pharmacists and physicians in ambulatory patients:a retrospective cohort study

Background Shared care agreements between clinical pharmacists and physicians can improve suboptimal lithium monitoring in in- and outpatient settings. However, it is unknown whether incorporating community pharmacists in such agreements can also improve lithium monitoring in an outpatient setting. Aim To assess the necessity for a shared care agreement for lithium monitoring in our region by investigating: intervention rates by community pharmacists and whether those are sufficient; lithium monitoring by physicians in ambulatory patients; the extent of laboratory parameter exchange to community pharmacists. Method Patient files of lithium users were surveyed in a retrospective cohort study among 21 community pharmacies in the Northern Netherlands. Outcome was the intervention rate by community pharmacists and whether those were deemed sufficient by an expert panel. Additionally, we investigated both the percentages of patients monitored according to current guidelines and of laboratory parameters exchanged to community pharmacists. Results 129 patients were included. Interventions were performed in 64.4% (n = 29), 20.8% (n = 5), and 25.0% (n = 1) of initiations, discontinuations, and dosage alterations of drugs interacting with lithium, respectively. The expert panel deemed 40.0% (n = 14) of these interventions as "insufficient". Physicians monitored 40.3% (n = 52) of the patients according to current guidelines for lithium serum levels and kidney functions combined. Approximately half of the requested laboratory parameters were available to the community pharmacist. Conclusion Intervention rates by community pharmacists and lithium monitoring by physicians can be improved. Therefore, a shared care agreement between community pharmacists, clinical pharmacists, and physicians is needed to improve lithium monitoring in ambulatory patients

Centering, scaling, and transformations: improving the biological information content of metabolomics data

BACKGROUND: Extracting relevant biological information from large data sets is a major challenge in functional genomics research. Different aspects of the data hamper their biological interpretation. For instance, 5000-fold differences in concentration for different metabolites are present in a metabolomics data set, while these differences are not proportional to the biological relevance of these metabolites. However, data analysis methods are not able to make this distinction. Data pretreatment methods can correct for aspects that hinder the biological interpretation of metabolomics data sets by emphasizing the biological information in the data set and thus improving their biological interpretability. RESULTS: Different data pretreatment methods, i.e. centering, autoscaling, pareto scaling, range scaling, vast scaling, log transformation, and power transformation, were tested on a real-life metabolomics data set. They were found to greatly affect the outcome of the data analysis and thus the rank of the, from a biological point of view, most important metabolites. Furthermore, the stability of the rank, the influence of technical errors on data analysis, and the preference of data analysis methods for selecting highly abundant metabolites were affected by the data pretreatment method used prior to data analysis. CONCLUSION: Different pretreatment methods emphasize different aspects of the data and each pretreatment method has its own merits and drawbacks. The choice for a pretreatment method depends on the biological question to be answered, the properties of the data set and the data analysis method selected. For the explorative analysis of the validation data set used in this study, autoscaling and range scaling performed better than the other pretreatment methods. That is, range scaling and autoscaling were able to remove the dependence of the rank of the metabolites on the average concentration and the magnitude of the fold changes and showed biologically sensible results after PCA (principal component analysis). In conclusion, selecting a proper data pretreatment method is an essential step in the analysis of metabolomics data and greatly affects the metabolites that are identified to be the most important