Multivariate profiling

Loo, L.-H., Wu, L.-F. and Altschuler, S.J. (2007) Image-based multivariate profiling of drug responses from single cells. Nature Methods, 4, 445-453. 

For several reasons, notably the lack of randomization, some statisticians question the use of a split-plot analysis with repeated measurements over time. Although this type of analysis is probably satisfactory for most applications, one could turn to multivariate profile analysis (see Timm, 1975) to obtain more theoretical rigor. 

Laboratory and/or field data were analyzed using SAS systems (SAS Institute Inc. 1999) utilizing analysis of variance, regression analysis, response surface analysis, univariate analysis, repeated measures analysis (multivariate profile analysis), covariance analysis and/or principle components analysis. Good statistical practices were used to verify that the data satisfied the assumptions underlying the various analyses. Significant differences between means were determined by Tukey s Studentized Range Test, the Tukey-Kramer HSD test, or the Bonferroni t test. Alpha was set at 0.05. 

The target projection plots are easy to interpret and can be used to name factors influencing a system. Furthermore, for systems in which variation in the multivariate profile with a given dependent variable is small, target projection can amplify the changes by means of the target projection plots. The NIR spectral profiles of food... 

Bayesian networks for multivariate reasoning about cause and effect within R D with a flow bottleneck model (Fig. 11.6) to help combine scientific and economic aspects of decision making. This model can, where research process decisions affect potential candidate value, further incorporate simple estimation of how the candidate value varies based on the target product profile. Factors such as ease of dosing in this profile can then be causally linked to the relevant predictors within the research process (e.g., bioavailability), to model the value of the predictive methods that might be used and to perform sensitivity analysis of how R D process choices affect the expected added... 

To detect adulteration of wine. Bums et al. (2002) found that the ratios of acetylated to p-coumaroylated conjugates of nine characteristic anthocyanins served as useful parameters to determine grape cultivars for a type of wine. Our laboratory utilized mid-infrared spectroscopy combined with multivariate analysis to provide spectral signature profiles that allowed the chemically based classification of antho-cyanin-containing fruits juices and produced distinctive and reproducible chemical fingerprints, making it possible to discriminate different juices. " This new application of ATR-FTIR to detect adulteration in anthocyanin-containing juices and foods may be an effective and efficient method for manufacturers to assure product quality and authenticity. 

D. Coomans, I. Broeckaert, M.P. Derde, A. Tassin, D.L. Massart and S. Wold, Use of a microcomputer for the definition of multivariate confidence regions in medical diagnosis based on clinical laboratory profiles. Comp. Biomed. Res., 17 (1984) 1-14. 

The aim of all the foregoing methods of factor analysis is to decompose a data-set into physically meaningful factors, for instance pure spectra from a HPLC-DAD data-set. After those factors have been obtained, quantitation should be possible by calculating the contribution of each factor in the rows of the data matrix. By ITTFA (see Section 34.2.6) for example, one estimates the elution profiles of each individual compound. However, for quantitation the peak areas have to be correlated to the concentration by a calibration step. This is particularly important when using a diode array detector because the response factors (absorptivity) may considerably vary with the compound considered. Some methods of factor analysis require the presence of a pure variable for each factor. In that case quantitation becomes straightforward and does not need a multivariate approach because full selectivity is available. 

There are four main types of data that frequently occur in sensory analysis pair-wise differences, attribute profiling, time-intensity recordings and preference data. We will discuss in what situations such data arise and how they can be analyzed. Especially the analysis of profiling data and the comparison of such data with chemical information calls for a multivariate approach. Here, we can apply some of the techniques treated before, particularly those of Chapters 35 and 36. 

The multivariate statistical data analysis, using principal component analysis (PCA), of this historical data revealed three main contamination profiles. A first contamination profile was identified as mostly loaded with PAHs. A samples group which includes sampling sites R1 (Ebro river in Miranda de Ebro, La Rioja), T3 (Zadorra river in Villodas, Alava) and T9 (Arga river in Puente la Reina, Navarra), all located in the upper Ebro river basin and close to Pamplona and Vitoria cities,... 

Only multivariate (e.g. multi-wavelength) data are amenable to model-free analyses. While this is a restriction, it is not a serious one. The goal of the analysis is to decompose the matrix of data into a product of two physically meaningful matrices, usually into a matrix containing the concentration profiles of the components taking part in the chemical process, and a matrix that contains their absorption spectra (Beer-Lambert s law). If there are no model-based equations that quantitatively describe the data, model-free analyses are the only method of analysis. Otherwise, the results of model-... 

C calc=Y/A % cone, profiles via multivariate linear regression... 

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