Unsupervised multivariate statistical methods

Unsupervised multivariate statistical methods [CA, principal components analysis, Kohonen s self-organizing maps (SOMs), nonlinear mapping, etc.], which perform spontaneous data analysis without the need for special training (learning), levels of knowledge, or preliminary conditions. 

The choice of properties has a major influence on pattern recognition methods (unsupervised multivariate statistical or neural network methods) in particular and different property sets can resnlt in qnite different patterns of similarity between compounds. Several methods are available to make selections of subsets of uncorrelated properties which can be used... 

The types of statistieal analytical methods required in this application are often multivariate methods. These statistical procedures are called multivariate when the property being measured, for example, the location of the food, is being related to several variables (such as the signal levels in different miz channels) in the analysis. Multivariate statistical methods can be broadly divided into two types (1) unsupervised, which means that no a priori knowledge of the samples to be classified is required and (2) supervised, which requires a priori knowledge about the samples [18]. A good example of an unsupervised method is principal component analysis (PCA) [19-27], which looks for patterns in a block of data that depend on different variables. PCA provides a useful tool to explore and visualize information, and in particular to identify patterns in complex data, and it is therefore widely used. Applications of PCA in food science will be presented later in this chapter. 

An in-depth review of statistical methods for metabonomic data analysis is beyond the scope of this chapter. Briefly, there are a few main approaches to data analysis. Examples of multivariate data analyses include the so-called unsupervised analyses such as PCA, independent component analysis (ICA), and hierarchical clustering analysis (HCA), while partial least square differential analysis (PLS-DA) is... 

Statistical Analysis and Reporting Methods for statistical analysis of metabonomics data sets include a variety of supervised and unsupervised multivariate techniques (Holmes et al., 2000) as well as univariate analysis strategies. These chemometric approaches have been recently reviewed (Holmes and Antti, 2002 Robertson et al., 2007), and a thorough discussion of these is outside the scope of this chapter. Perhaps the best known of the unsupervised multivariate techniques is principle component analysis (PCA) and is widely... 

In this chapter, we will show altered composition of metabolites in the cancerous tissue revealed by IMS, with both manual data processing and statistic data management. In particular, as a statistical strategy, an unsupervised multivariate data analysis technique that enables us to sort the data sets without any reference information is described. A major method that is related to IMS, namely principal component analysis (PCA), will be described in detail. 

Because all metabolites cannot routinely be identified and quantified in a complex metabolome it is often satisfactory to investigate patterns of the metabolome to determine changes due to external stress on the biosystem. Data from metabolome analysis are complex and large. Thus multivariant analyses are often used to provide meaningful data. There are two types of multivariant analysis approaches used to statistically analyze metabolic data supervised and unsupervised methods. As shown above in the volatile breath analysis by IMS, discriminant analysis was used to determine healthy patients from patients suffering from lung cancer. Discriminant analysis is a supervised method, meaning the classification of the sample must be... 

---