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    ABSTRACT: SOMs (Self Organising Maps) are derived from the machine learning literature and serve as a valuable method for representing data. In this paper, the use of SOMs as a technique for determining the most significant variables (or markers) in a dataset is described. The method is applied to the NMR spectra of 96 human saliva samples, half of which have been treated with an oral rinse formulation and half of which are controls, and 49 variables consisting of bucketed intensities. In addition, three simulations, two of which consist of the same number of samples and variables as the experimental dataset and a third that contains a much larger number of variables, are described. Two of the simulations contain known discriminatory variables, and the remaining is treated as a null dataset without any specific discriminatory variables added. The described SOM method is contrasted to Partial Least Squares Discriminant Analysis, and a list of the markers determined to be most significant using both approaches was obtained and the differences arising are discussed. A SOM Discrimination Index (SOMDI) is defined, whose magnitude relates to how strongly a variable is considered to be a discriminator. In order to ensure that the model is stable and not dependent on the random starting point of the SOM, one hundred iterations were performed and variables that were consistently of high rank were selected. A variety of approaches for data representation are illustrated, and the main theoretical principles of employing SOMs for determining which variables are most significant are outlined. Software used in this paper was written in-house, allowing greater flexibility over existing packages, and tailored for the specific application in hand.
    Chemometrics and Intelligent Laboratory Systems. 10/2009;
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    ABSTRACT: Three methods for variable selection are described, namely the t-statistic, Partial Least Squares Discriminant Analysis (PLS-DA) weights and regression coefficients, with the aim of determining which variables are the most significant markers for discriminating between two groups: a variable’s level of significance is related to its magnitude. Monte-Carlo methods are employed to determine empirical significance of variables, by permuting randomly the class membership 5000times to obtain null distributions, and comparing the observed statistic for each variable with the null distribution. Seven simulations consisting of 200 samples, divided equally between two classes, and 300 variables, are constructed; in one dataset there are no induced correlations between variables, in two datasets correlations are induced but there is no induced separation between the classes, and in four datasets, separation is induced by selecting 20 of the variables to be discriminators. In addition two metabolomic datasets were analysed consisting of the GCMS of urinary extracts from mice both to determine the effect of stress and to determine the effect of diet on the urinary chemosignal. It is shown that the t-statistic combined with Monte-Carlo permutations provides similar results to PLS weights. PLS regression coefficients find the least number of markers but, for the simulations, the lowest False Positives rates.
    Metabolomics 01/2009; 5(4):387-406. · 4.43 Impact Factor
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    ABSTRACT: Odors emitted by human skin are of great interest to biologists in many fields; applications range from forensic studies to diagnostic tools, the design of perfumes and deodorants, and the ecology of blood-sucking insect vectors of human disease. Numerous studies have investigated the chemical composition of skin odors, and various sampling methods have been used for this purpose. The literature shows that the chemical profile of skin volatiles varies greatly among studies, and the use of different sampling procedures is probably responsible for some of these variations. To our knowledge, this is the first review focused on human skin volatile compounds. We detail the different sampling techniques, each with its own set of advantages and disadvantages, which have been used for the collection of skin odors from different parts of the human body. We present the main skin volatile compounds found in these studies, with particular emphasis on the most frequently studied body regions, axillae, hands, and feet. We propose future directions for promising experimental studies on odors from human skin, particularly in relation to the chemical ecology of blood-sucking insects.
    Journal of Chemical Ecology 04/2013; · 2.46 Impact Factor

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