An approach for the development and selection of chromatographic methods for high-throughput metabolomic screening of urine by ultra pressure LC-ESI-ToF-MS

Since the components of a sample for open metabolomic analysis are unknown a priori a pragmatic approach to method development has been taken in order to develop and select a chromatographic method suitable for high-throughput open metabolomic screening of urine by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS). A total of 848 injections of diluted rat urine were made onto a UPLC-ESI-ToF-MS system using several different gradient profiles and run times to determine a suitable method for analysis of urine from male and female rats. Peak integral and multivariate data analysis were performed to investigate the quality of separation and information obtained from these multiple analyses. A suitable 8 min method was selected and is now used routinely for open profiling metabolomic analyses of urine. The use of a sample-relevant QC mix is also discussed. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Exploratory data analysis of DNA microarrays by multivariate curve resolution

In this work, the application of a multivariate curve resolution procedure based on alternating least squares optimization (MCR-ALS) for the analysis of data from DNA microarrays is proposed. For this purpose, simulated and publicly available experimental data sets have been analyzed. Application of MCR-ALS, a method that operates without the use of any training set, has enabled the resolution of the relevant information about different cancer lines classification using a set of few components; each of these defined by a sample and a pure gene expression profile. From resolved sample profiles, a classification of samples according to their origin is proposed. From the resolved pure gene expression profiles, a set of over- or underexpressed genes that could be related to the development of cancer diseases has been selected. Advantages of the MCR-ALS procedure in relation to other previously proposed procedures such as principal component analysis are discussed.     

Coregionalized single- and multiresolution spatially varying growth curve modeling with application to weed growth

Modeling of longitudinal data from agricultural experiments using growth curves helps understand conditions conducive or unconducive to crop growth. Recent advances in Geographical Information Systems (GIS) now allow geocoding of agricultural data that help understand spatial patterns. A particularly common problem is capturing spatial variation in growth patterns over the entire experimental domain. Statistical modeling in these settings can be challenging because agricultural designs are often spatially replicated, with arrays of subplots, and interest lies in capturing spatial variation at possibly different resolutions. In this article, we develop a framework for modeling spatially varying growth curves as Gaussian processes that capture associations at single and multiple resolutions. We provide Bayesian hierarchical models for this setting, where flexible parameterization enables spatial estimation and prediction of growth curves. We illustrate using data from weed growth experiments conducted in Waseca, Minnesota, that recorded growth of the weed Setaria spp. in a spatially replicated design.     

Distance-based tests for homogeneity of multivariate dispersions

Summary The traditional likelihood‐based test for differences in multivariate dispersions is known to be sensitive to nonnormality. It is also impossible to use when the number of variables exceeds the number of observations. Many biological and ecological data sets have many variables, are highly skewed, and are zero‐inflated. The traditional test and even some more robust alternatives are also unreasonable in many contexts where measures of dispersion based on a non‐Euclidean dissimilarity would be more appropriate. Distance‐based tests of homogeneity of multivariate dispersions, which can be based on any dissimilarity measure of choice, are proposed here. They rely on the rotational invariance of either the multivariate centroid or the spatial median to obtain measures of spread using principal coordinate axes. The tests are straightforward multivariate extensions of Levene's test, with P‐values obtained either using the traditional F‐distribution or using permutation of either least‐squares or LAD residuals. Examples illustrate the utility of the approach, including the analysis of stabilizing selection in sparrows, biodiversity of New Zealand fish assemblages, and the response of Indonesian reef corals to an El Niño. Monte Carlo simulations from the real data sets show that the distance‐based tests are robust and powerful for relevant alternative hypotheses of real differences in spread.     

Bayesian dose-finding in phase I/II clinical trials using toxicity and efficacy odds ratios

A Bayesian adaptive design is proposed for dose-finding in phase I/II clinical trials to incorporate the bivariate outcomes, toxicity and efficacy, of a new treatment. Without specifying any parametric functional form for the drug dose-response curve, we jointly model the bivariate binary data to account for the correlation between toxicity and efficacy. After observing all the responses of each cohort of patients, the dosage for the next cohort is escalated, deescalated, or unchanged according to the proposed odds ratio criteria constructed from the posterior toxicity and efficacy probabilities. A novel class of prior distributions is proposed through logit transformations which implicitly imposes a monotonic constraint on dose toxicity probabilities and correlates the probabilities of the bivariate outcomes. We conduct simulation studies to evaluate the operating characteristics of the proposed method. Under various scenarios, the new Bayesian design based on the toxicity-efficacy odds ratio trade-offs exhibits good properties and treats most patients at the desirable dose levels. The method is illustrated with a real trial design for a breast medical oncology study.     

Congenital defects of the upper lateral incisors: Multivariate analysis of measurements of the other teeth,the superior arch,head and face

The survey of a French male population allowed us to ascertain 75 propositi with one or two missing ULI, 59 propositi with one or two reduced ULI and 99 controls on whom measurements (mesiodistal and buccolingual diameters) of all teeth of the superior arch are available. Principal Component Analysis gave a first estimated principal component highly correlated with each of the dental measurements or arch measurements. This size factor was eliminated by observing the plane of the second and third principal components. Strikingly different clusters of MD diameters or BL diameters were observed for the controls, the propositi missing one or two of the ULI and the propositi with reduced ULI. For the controls, the arch length is correlated with the MD molar diameters and the MD incisor diameter, the arch width being isolated from the other measurements. For the propositi with missing ULI, among the dental measurements the MD and BL diameters cluster, the arch length is isolated as are the arch widths. For the propositi with reduced ULI, the arch length is closer to the dental measurements while the widths, especially the first one, are isolated. The best discriminant measurements are the diameters of the first premolars and the canine, the first arch width and the arch length. Among controls, the arch is narrowed and shorter for the propositi with absence and wider for the propositi with reduction. Teeth measurements are always smaller in propositi.     

A phylogenetic study of titi monkeys,genusCallicebus, based on cranial measurements: I. Phyletic groups ofCallicebus

Examinations of 23 forms ofCallicebus were made to elucidate their phylogenetic relationships based on cranial measurements. Multivariate statistical methods of distance analysis utilizing Q-mode correlation coefficients and principal component analysis were employed. As a result, the following five distinct groups were recognized morphometrically among the 23 species and subspecies: theDonacophilus, Cupreus, Moloch, Personatus, andTorquatus groups. Moreover, the phenetic characters of cranial size, karyotype, pelage coloration, and geographic range were consistent with the results of this group classification. It could be safely concluded therefore that these groups represented phylogenetically independent groups, in view of the fact that the rate of character differentiation was not appreciably different among closely related groups. Of the five groups, thePersonatus andTorquatus groups were respectively considered to maintain a higher peculiarity of character differentiation within theCallicebus phylogeny, since these two groups individually displayed the higher magnitudes of differentiation in both their craniometric shape and some phenetic features. Conversely, the other three groups were considered to be closer to each other in theCallicebus phylogeny.