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The initialization of concentration vector for iterative target transformation factor analysis (ITTFA) and identification of pure or key variables are the important issue in MCR. In this study, dissimilarity analysis and evolving factor analysis (EFA) are combined to find the selective or key variables and subsequently obtain initial estimates of the concentration vectors for resolution of gas chromatography/mass spectrometry (GC/MS) data by ITTFA. For systems containing components with highly similar mass spectra, a new constraint setting the elements out of elution window to 0 is used to improve convergence rate and accuracy of results. Tested by standard mixture of two wax esters and real GC/MS data of gasoline 97#, dissimilarity based ITTFA could obtain accurate results (average Dot product of concentration vectors, average deviation of peak area ratio and average similarity of mass spectra are 0.9929, 0.0228 and 981.0, respectively)., (Copyright © 2011 Elsevier B.V. All rights reserved.)

We provide an overview of latent variable methods used in pharmaceutics and integrated with advanced characterization techniques such as vibrational spectroscopy. The basics of the most common latent variable methods, principal component analysis (PCA), principal component regression (PCR) and partial least-squares (PLS) regression, are presented. Multiple linear regression (MLR) and methods for improved interpretation, variable selection, classification and validation are also briefly discussed. Extensive use of the methods is demonstrated by compilation of the recent literature., (Copyright © 2011 Elsevier B.V. All rights reserved.)

Mass spectral profiles from cerebrospinal fluid (CSF) are used as input to a novel multivariate approach to select features responsible for the separation of patients with multiple sclerosis (MS) from control groups. Our targeted statistical approach makes it possible to systematically remove features in the spectral fingerprints masking the components expressing the disease pattern. The low molecular weight CSF proteome from 54 patients with MS and a range of other neurological diseases (OND), as well as neurological healthy controls (NHC), is analyzed in replicates using mass spectral profiling. Statistically validated partial least-squares discriminant analysis (PLS-DA) models are created as a first step to separate the groups. Using the group membership as a target, the most discriminatory projection in the multivariate space spanned by the spectral profiles is revealed. From the resulting target-projected component, the spectral regions most significantly contributing to group separation are identified using the nonparametric discriminating variable (DIVA) test together with the so-called selectivity ratio (SR) plot. Our approach is general and can be applied for other diseases and instrumental techniques as well.

ABSTRACT This work investigates the impact of marine omega‐3 and physical activity and their interaction on cardiometabolic health as expressed by the serum lipoprotein profile. Using an experimental design that allows for the possibility of interaction, we performed a 6‐week intervention on 44 middle‐aged women living in Western Norway. The women were randomly divided into four groups: one control group with no intervention, a second group performing sessions of moderate intensity three times per week, a third group taking daily supplements of omega‐3 marine fatty acids, and a fourth group combining the interventions for Groups 2 and 3. The difference in the lipoprotein profiles after the intervention from baseline were assessed for statistical significance by comparing groups 2, 3 and 4 with Group 1 using two‐tailed t‐test corrected for multiple testing and selectivity ratios calculated from the discriminatory component in validated partial least squares discriminant models. The results from the univariate and multivariate analyses were qualitatively equivalent: Only the women combining moderate physical activity and omega‐3 supplementation, revealed statistically significant differences in their lipoprotein profile compared to the nonintervention control group. The pattern of change in the lipoprotein profile is associated with improved cardiometabolic health. Use of the design matrix to predict this pattern revealed that the interaction between omega‐3 supplementation and physical activity played a major role in inducing this change. The recognition of the influence of this interaction may be a step towards resolving the long‐lasting debate of the role played by omega‐3 for preventing cardiovascular unhealth. [ABSTRACT FROM AUTHOR]

Background: Strongly multicollinear covariates, such as those typically represented in metabolomics applications, represent a challenge for multivariate regression analysis. These challenges are commonly circumvented by reducing the number of covariates to a subset of linearly independent variables, but this strategy may lead to loss of resolution and thus produce models with poorer interpretative potential. The aim of this work was to implement and illustrate a method, multivariate pattern analysis (MVPA), which can handle multivariate covariates without compromising resolution or model quality. Results: MVPA has been implemented in an open-source R package of the same name, mvpa. To facilitate the usage and interpretation of complex association patterns, mvpa has also been integrated into an R shiny app, mvpaShiny, which can be accessed on www.mvpashiny.org. MVPA utilizes a general projection algorithm that embraces a diversity of possible models. The method handles multicollinear and even linear dependent covariates. MVPA separates the variance in the data into orthogonal parts within the frame of a single joint model: one part describing the relations between covariates, outcome, and explanatory variables and another part describing the "net" predictive association pattern between outcome and explanatory variables. These patterns are visualized and interpreted in variance plots and plots for pattern analysis and ranking according to variable importance. Adjustment for a linear dependent covariate is performed in three steps. First, partial least squares regression with repeated Monte Carlo resampling is used to determine the number of predictive PLS components for a model relating the covariate to the outcome. Second, postprocessing of this PLS model by target projection provided a single component expressing the predictive association pattern between the outcome and the covariate. Third, the outcome and the explanatory variables were adjusted for the covariate by using the target score in the projection algorithm to obtain "net" data. We illustrate the main features of MVPA by investigating the partial mediation of a linearly dependent metabolomics descriptor on the association pattern between a measure of insulin resistance and lifestyle-related factors. Conclusions: Our method and implementation in R extend the range of possible analyses and visualizations that can be performed for complex multivariate data structures. The R packages are available on github.com/liningtonlab/mvpa and github.com/liningtonlab/mvpaShiny. [ABSTRACT FROM AUTHOR]

Metabolomics-based approaches are becoming increasingly popular to interrogate the chemical basis for phenotypic differences in biological systems. Successful metabolomics studies employ multivariate data analysis to compare large and highly complex datasets. A primary tool for unsupervised statistical analyses, principal component analysis (PCA), relies on the selection of a subsection of a maximum of three components from a larger model to visually represent similarity. The use of only three principal components limits the comprehensiveness of the model and can mask discrimination between samples. We have developed a new statistical metric, the composite score (CS), as a univariate statistic that incorporates multiple principal components to calculate a correlation matrix that enables quantitative comparisons of sample similarity between samples within one dataset based upon measured metabolome profiles. Composite score values were tabulated using profiles of complex extracts of dietary supplements from the plant Hydrastis canadensis (goldenseal) as a case study. Several outliers were unambiguously identified, and a PCA composite score network was developed to provide a graphical representation of the composite score matrix. Comparison with visualization using PCA score plots or dendrograms from hierarchical clustering analysis (HCA) demonstrates the utility of the composite score to as a tool for metabolomics studies that seek to quantify similarity among samples. An R-script for the calculation of composite score has been made available. Image 3 • Combined multiple principal components into a single statistical metric. • Composite score unambiguously described outliers from the dataset. • Network analysis provided clear graphical interpretation of metabolomics data. • Efficient tool for ascribing similarity and differences among samples. [ABSTRACT FROM AUTHOR]

Background Classical scrapie in sheep is a fatal neurodegenerative disease associated with the conversion PrPC to PrPSc. Much is known about genetic susceptibility, uptake and dissemination of PrPSc in the body, but many aspects of prion diseases are still unknown. Different proteomic techniques have been used during the last decade to investigate differences in protein profiles between affected animals and healthy controls. We have investigated the protein profiles in serum of sheep with scrapie and healthy controls by SELDI-TOF-MS and LC-MS/MS. Latent Variable methods such as Principal Component Analysis, Partial Least Squares- Discriminant Analysis and Target Projection methods were used to describe the MS data. Results The serum proteomic profiles showed variable differences between the groups both throughout the incubation period and at the clinical end stage of scrapie. At the end stage, the target projection model separated the two groups with a sensitivity of 97.8%, and serum amyloid A was identified as one of the protein peaks that differed significantly between the groups. Conclusions At the clinical end stage of classical scrapie, ten SELDI peaks significantly discriminated the scrapie group from the healthy controls. During the non-clinical incubation period, individual SELDI peaks were differently expressed between the groups at different time points. Investigations of differences in -omic profiles can contribute to new insights into the underlying disease processes and pathways, and advance our understanding of prion diseases, but comparison and validation across laboratories is difficult and challenging. [ABSTRACT FROM AUTHOR]

Examines normalization procedures of analytical profiles from a theoretical point of view and their effects on simulated and real data. Bias toward negative peak correlations in normalization of data in the presence of homoscedastic noise; Impact of normalization of data in the presence of heteroscedastic noise on the correlation structure.

Chemical ecology has been suggested as a less time-consuming and more cost-efficient monitoring tool of seagrass ecosystems than traditional methods. Phenolic chemistry in Zostera marina samples was analyzed against latitude, sea depth, sample position within a seagrass meadow (periphery or center) and wave exposure. Multivariate data analysis showed that rosmarinic acid correlated moderately positively with depth, while the flavonoids had an overall strong negative correlation with increasing depth—possibly reflecting lack of stress-induced conditions with increasing depth, rather than a different response to light conditions. At a molecular level, the flavonoids were separated into two groups; one group is well described by the variables of depth and wave exposure, and the other group that was not well described by these variables—the latter may reflect biosynthetic dependencies or other unrevealed factors. A higher flavonoid/rosmarinic acid ratio was seen in the periphery of a seagrass meadow, while the contrary ratio was seen in the center. This may reflect higher plant stress in the periphery of a meadow, and the flavonoid/rosmarinic acid ratio may provide a possible molecular index of seagrass ecosystem health. Further studies are needed before the full potential of using variation in phenolic chemistry as a seagrass ecosystem monitoring tool is established. [ABSTRACT FROM AUTHOR]

Abstract: This work presents a new method for variable selection in complex spectral profiles. The method is validated by comparing samples from cerebrospinal fluid (CSF) with the same samples spiked with peptide and protein standards at different concentration levels. Partial least squares discriminant analysis (PLS-DA) attempts to separate two groups of samples by regressing on a y -vector consisting of zeros and ones in the PLS decomposition. In most cases, several PLS components are needed to optimize the discrimination between groups. This creates difficulties for the interpretation of the model. By using the y -vector as a target, it is possible to transform the PLS components to obtain a single predictive target-projected component analogously to the predictive component in orthogonal partial least squares discriminant analysis (OPLS-DA). By calculating the ratio between explained and residual variance of the spectral variables on the target-projected component, a selectivity ratio plot is obtained that can be used for variable selection. Used on whole mass spectral profiles of pure and spiked CSF, we can detect peptide in the low molecular mass range (740–9000 Da) at least down to 400 pM level without severe problems with false biomarker candidates. Similarly, we detect added proteins at least down to 2 nM level in the medium mass range (6000–17,500 Da). Target projection represents the optimal way to fit a latent variable decomposition to a known target, but the selectivity ratio plot can be used for OPLS as well as other methods that produce a single predictive component. Comparison with some commonly used tools for variable selection shows that the selectivity ratio plot has the best performance. This observation is attributed to the fact that target projection utilizes both the predictive ability (regression coefficients) and the explanatory ability (spectral variance/covariance matrix) for the calculation of the selectivity ratio. [Copyright &y& Elsevier]

Abstract: A multivariate index assessing community stress in marine benthic count data is compared to other multivariate methods. Both ordinations and clustering methods are included in the comparison and so is AMBI (AZTI marine biotic index). The community disturbance index (CDI) requires the data to contain undisturbed reference samples. If this requirement is met, the calculations are carried out in two steps. Firstly, the reference samples are separated and used to build a model representing the natural variation. Subsequently, the remaining samples are compared to the model and the respective disturbance indices are calculated. The CDI has the same sensitivity as the traditional multivariate methods. Additionally, one obtains a quantification of the relative level of disturbance. The comparison is performed using data from monitoring surveys at three different oilfields in the North Sea: Troll, Ekofisk and Oseberg-Brage. The samples are collected in 1996, 1990 and 1996, respectively. [Copyright &y& Elsevier]