On the Use of Correlation and MI as a Measure of Metabolite—Metabolite Association for Network Differential Connectivity Analysis

Metabolite differential connectivity analysis has been successful in investigating potentialmolecular mechanisms underlying different conditions in biological systems. Correlation and MutualInformation (MI) are two of the most common measures to quantify association and for buildingmetabolite—metabolite association networks and to calculate differential connectivity. In this study,we investigated the performance of correlation and MI to identify significantly differentially connected metabolites. These association measures were compared on (i) 23 publicly available metabolomic data sets and 7 data sets from other fields, (ii) simulated data with known correlation structures,and (iii) data generated using a dynamic metabolic model to simulate real-life observed metabolite concentration profiles. In all cases, we found more differentially connected metabolites when using correlation indices as a measure for association than MI.We also observed that different MI estimation algorithms resulted in difference in performance when applied to data generated using a dynamic model. We concluded that there is no significant benefit in using MI as a replacement for standard Pearson’s or Spearman’s correlation when the application is to quantify and detect differentially connected metabolites

Approaches to Sample Size Determination for Multivariate Data:Applications to PCA and PLS-DA of Omics Data

Sample size determination is a fundamental step in the design of experiments. Methods for sample size determination are abundant for univariate analysis methods, but scarce in the multivariate case. Omics data are multivariate in nature and are commonly investigated using multivariate statistical methods, such as principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA). No simple approaches to sample size determination exist for PCA and PLS-DA. In this paper we will introduce important concepts and offer strategies for (minimally) required sample size estimation when planning experiments to be analyzed using PCA and/or PLS-DA.</p

Considering Horn’s parallel analysis from a random matrix theory point of view

Horn’s parallel analysis is a widely used method for assessing the number of principal components and common factors. We discuss the theoretical foundations of parallel analysis for principal components based on a covariance matrix by making use of arguments from random matrix theory. In particular, we show that (i) for the first component, parallel analysis is an inferential method equivalent to the Tracy–Widom test, (ii) its use to test high-order eigenvalues is equivalent to the use of the joint distribution of the eigenvalues, and thus should be discouraged, and (iii) a formal test for higher-order components can be obtained based on a Tracy–Widom approximation. We illustrate the performance of the two testing procedures using simulated data generated under both a principal component model and a common factors model. For the principal component model, the Tracy–Widom test performs consistently in all conditions, while parallel analysis shows unpredictable behavior for higher-order components. For the common factor model, including major and minor factors, both procedures are heuristic approaches, with variable performance. We conclude that the Tracy–Widom procedure is preferred over parallel analysis for statistically testing the number of principal components based on a covariance matrix.<p>Horn’s parallel analysis is a widely used method for assessing the number of principal components and common factors. We discuss the theoretical foundations of parallel analysis for principal components based on a covariance matrix by making use of arguments from random matrix theory. In particular, we show that (i) for the first component, parallel analysis is an inferential method equivalent to the Tracy–Widom test, (ii) its use to test high-order eigenvalues is equivalent to the use of the joint distribution of the eigenvalues, and thus should be discouraged, and (iii) a formal test for higher-order components can be obtained based on a Tracy–Widom approximation. We illustrate the performance of the two testing procedures using simulated data generated under both a principal component model and a common factors model. For the principal component model, the Tracy–Widom test performs consistently in all conditions, while parallel analysis shows unpredictable behavior for higher-order components. For the common factor model, including major and minor factors, both procedures are heuristic approaches, with variable performance. We conclude that the Tracy–Widom procedure is preferred over parallel analysis for statistically testing the number of principal components based on a covariance matrix.</p

A Metabolomic Perspective on Coeliac Disease

Metabolomics is an “omic” science that is now emerging with the purpose of elaborating a comprehensive analysis of the metabolome, which is the complete set of metabolites (i.e., small molecules intermediates) in an organism, tissue, cell, or biofluid. In the past decade, metabolomics has already proved to be useful for the characterization of several pathological conditions and offers promises as a clinical tool. A metabolomics investigation of coeliac disease (CD) revealed that a metabolic fingerprint for CD can be defined, which accounts for three different but complementary components: malabsorption, energy metabolism, and alterations in gut microflora and/or intestinal permeability. In this review, we will discuss the major advancements in metabolomics of CD, in particular with respect to the role of gut microbiome and energy metabolis

SAPP: functional genome annotation and analysis through a semantic framework using FAIR principles

There are currently more than 150.000 sequenced genomes available from which considerable amounts of information can be extracted. However, annotation information is often not interoperable, static, lacks provenance and is quickly outdated. Keeping these datasets up-to-date, and interoperable is a challenging ..

Strategies for individual phenotyping of linoleic and arachidonic Acid metabolism using an oral glucose tolerance test

The ability to restore homeostasis upon environmental challenges has been proposed as a measure for health. Metabolic profiling of plasma samples during the challenge response phase should offer a profound view on the flexibility of a phenotype to cope with daily stressors. Current data modeling approaches, however, struggle to extract biological descriptors from time-resolved metabolite profiles that are able to discriminate between different phenotypes. Thus, for the case of oxylipin responses in plasma upon an oral glucose tolerance test we developed a modeling approach that incorporates a priori biological pathway knowledge. The degradation pathways of arachidonic and linoleic acids were modeled using a regression model based on a pseudo-steady-state approximated model, resulting in a parameter A that summarizes the relative enzymatic activity in these pathways. Analysis of the phenotypic parameters As suggests that different phenotypes can be discriminated according to preferred relative activity of the arachidonic and linoleic pathway. Correlation analysis shows that there is little or no competition between the arachidonic and linoleic acid pathways, although they share the same enzyme

Group‑wise ANOVA simultaneous component analysis for designed omics experiments

Modern omics experiments pertain not only to the measurement of many variables but also follow complex experimental designs where many factors are manipulated at the same time. This data can be conveniently analyzed using multivariate tools like ANOVA-simultaneous component analysis (ASCA) which allows interpretation of the variation induced by the different factors in a principal component analysis fashion. However, while in general only a subset of the measured variables may be related to the problem studied, all variables contribute to the final model and this may hamper interpretatio

Time profiles of energy balance in dairy cows in association with metabolic status, inflammatory status, and disease.

The early lactation period in dairy cows is characterized by complex interactions among energy balance (EB), disease, and alterations in metabolic and inflammatory status. The objective of this study was to cluster cows based on EB time profiles in early lactation and investigate the association between EB clusters and inflammatory status, metabolic status, oxidative stress, and disease. Holstein-Friesian dairy cows (n = 153) were selected and monitored for disease treatments during wk 1 to 6 in lactation. Weekly EB was calculated based on energy intake and energy requirements for maintenance and milk yield in wk 1 to 6 in lactation. Weekly plasma samples were analyzed for metabolic variables in wk 1 to 6, and inflammatory and oxidative stress variables in wk 1, 2, and 4 in lactation. Liver activity index (LAI) was computed from plasma albumin, cholesterol, and retino-binding protein concentration. First, cows were clustered based on time profiles of EB, resulting in 4 clusters (SP: stable positive; MN: mild negative; IN: intermediate negative; SN: severe negative). Cows in the SN cluster had higher plasma nonesterified fatty acids and β-hydroxybutyrate concentrations, compared with cows in the SP cluster, with the MN and IN cluster being intermediate. Cows in the SN cluster had a higher milk yield, lower dry matter intake in wk 1, lower insulin concentration compared with cows in the SP cluster, and lower glucose and IGF-1 concentration compared with cows in the SP and MN clusters. Energy balance clusters were not related with plasma haptoglobin, cholesterol, albumin, paraoxonase, and liver activity index (LAI). Second, cows were grouped based on health status [IHP: cows with treatment for inflammatory health problem (endometritis, fever, clinical mastitis, vaginal discharge or retained placenta); OHP: cows with no IHP but treatment for other health problem (milk fever, cystic ovaries, claw, and leg problems, rumen and intestine problems or other diseases); NHP: cows with no treatments, in the first 6 weeks after calving]. Energy balance was not different among health status groups. The IHP cows had lower nonesterified fatty acids and greater insulin concentration in plasma compared with OHP. The IHP cows had lower plasma albumin concentration, lower LAI and higher haptoglobin concentration compared with OHP and NHP. Overall, EB time profiles were associated with the metabolic status of dairy cows in early lactation, but were only limitedly related with markers of inflammation and oxidative stress status. Inflammatory and metabolic status were related to disease events in early lactation and caused prolonged effects on liver metabolism

Necrotizing soft tissue infections - a multicentre, prospective observational study (INFECT) : Protocol and statistical analysis plan

Background: The INFECT project aims to advance our understanding of the pathophysiological mechanisms in necrotizing soft tissue infections (NSTIs). The INFECT observational study is part of the INFECT project with the aim of studying the clinical profile of patients with NSTIs and correlating these to patient-important outcomes. With this protocol and statistical analysis plan we describe the methods used to obtain data and the details of the planned analyses. Methods: The INFECT study is a multicentre, prospective observational cohort study. Patients with NSTIs are enrolled in five Scandinavian hospitals, which are all referral centres for NSTIs. The primary outcomes are the descriptive variables of the patients. Secondary outcomes include identification of factors associated with 90-day mortality and amputation; associations between affected body part, maximum skin defect and Laboratory Risk Indicator for Necrotizing Fasciitis (LRINEC) score and 90-day mortality; 90-day mortality in patients with and without acute kidney injury (AKI) and LRINEC score of six and above or below six; and association between affected body part at arrival and microbiological findings. Exploratory outcomes include univariate analyses of baseline characteristics associations with 90-day mortality. The statistical analyses will be conducted in accordance with the predefined statistical analysis plan. Conclusion: Necrotizing soft tissue infections result in severe morbidity and mortality. The INFECT study will be the largest prospective study in patients with NSTIs to date and will provide important data for clinicians, researchers and policy makers on the characteristics and outcomes of these patients.</p