Genetic algorithm based two-mode clustering of metabolomics data

Metabolomics and other omics tools are generally characterized by large data sets with many variables obtained under different environmental conditions. Clustering methods and more specifically two-mode clustering methods are excellent tools for analyzing this type of data. Two-mode clustering methods allow for analysis of the behavior of subsets of metabolites under different experimental conditions. In addition, the results are easily visualized. In this paper we introduce a two-mode clustering method based on a genetic algorithm that uses a criterion that searches for homogeneous clusters. Furthermore we introduce a cluster stability criterion to validate the clusters and we provide an extended knee plot to select the optimal number of clusters in both experimental and metabolite modes. The genetic algorithm-based two-mode clustering gave biological relevant results when it was applied to two real life metabolomics data sets. It was, for instance, able to identify a catabolic pathway for growth on several of the carbon sources

Experiences in Pattern Recognition for Machine Olfaction

Pattern recognition is essential for translating complex olfactory sensor responses into simple outputs that are relevant to users. Many approaches to pattern recognition have been applied in this field, including multivariate statistics (e.g. discriminant analysis), artificial neural networks (ANNs) and support vector machines (SVMs). Reviewing our experience of using these techniques with many different sensor systems reveals some useful insights. Most importantly, it is clear beyond any doubt that the quantity and selection of samples used to train and test a pattern recognition system are by far the most important factors in ensuring it performs as accurately and reliably as possible. Here we present evidence for this assertion and make suggestions for best practice based on these findings

Dynamic metabolomic data analysis: a tutorial review

In metabolomics, time-resolved, dynamic or temporal data is more and more collected. The number of methods to analyze such data, however, is very limited and in most cases the dynamic nature of the data is not even taken into account. This paper reviews current methods in use for analyzing dynamic metabolomic data. Moreover, some methods from other fields of science that may be of use to analyze such dynamic metabolomics data are described in some detail. The methods are put in a general framework after providing a formal definition on what constitutes a ‘dynamic’ method. Some of the methods are illustrated with real-life metabolomics examples

Miller–Urey spark-discharge experiments in the deuterium world

We designed and conducted a series of primordial-soup Miller-Urey style experiments with deuterated gases and reagents to compare the spark-discharge products of a “deuterated world” with the standard reaction in the “hydrogenated world”. While the deuteration of the system has little effect on the distribution of amino acid products, significant differences are seen in other regions of the product-space. Not only do we observe about 120 new species, we also see significant differences in their distribution if the two hydrogen isotope worlds are compared. Several isotopologue matches can be identified in both, but a large proportion of products have no equivalent in the corresponding isotope world with ca. 43 new species in the D world and ca. 39 new species in the H world. This shows that isotopic exchange (the addition of only one neutron) may lead to significant additional complexity in chemical space under otherwise identical reaction conditions