Intrinsic vector analysis

On the other hand, factor analysis involves other manipulations of the eigen vectors and aims to gain insight into the structure of a multidimensional data set. The use of this technique was first proposed in biological structure-activity relationship (i. e., SAR) and illustrated with an analysis of the activities of 21 di-phenylaminopropanol derivatives in 11 biological tests [116-119, 289]. This method has been more commonly used to determine the intrinsic dimensionality of certain experimentally determined chemical properties which are the number of fundamental factors required to account for the variance. One of the best FA techniques is the Q-mode, which is based on grouping a multivariate data set based on the data structure defined by the similarity between samples [1, 313-316]. It is devoted exclusively to the interpretation of the inter-object relationships in a data set, rather than to the inter-variable (or covariance) relationships explored with R-mode factor analysis. The measure of similarity used is the cosine theta matrix, i. e., the matrix whose elements are the cosine of the angles between all sample pairs [1,313-316]. 

Recently, the detechon of vancomycin resistant enterococci (VRE) using MALDI-TOF MS profiles and a support vector machine has been described [74]. Internal cross-vahdation of the optimal statishcal model resulted in a sensitivity of 92.4% and a specificity of 85.2%. A subsequent external validation study after incorporation of the algorithm into the rouhne laboratory workflow surprisingly showed an even higher sensihvity and specificity of 96.7% and 98.1%, respectively. A further advantage was the rehable differentiation from other, intrinsically vancomycin resistant species. These excellent results did lead to incorporation of the analysis into the authors rouhne laboratory workflow. The broad applicability of the method shU has to be shown. 

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