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

Two-mode clustering of genotype by trait and genotype by environment data

In this paper, we demonstrate the use of two-mode clustering for genotype by trait and genotype by environment data. In contrast to two separate (one mode) clusterings on genotypes or traits/environments, two-mode clustering simultaneously produces homogeneous groups of genotypes and traits/environments. For two-mode clustering, we first scan all two-mode cluster solutions with all possible numbers of clusters using k-means. After deciding on the final numbers of clusters, we continue with a two-mode clustering algorithm based on a genetic algorithm. This ensures optimal solutions even for large data sets. We discuss the application of two-mode clustering to multiple trait data stemming from genomic research on tomatoes as well as an application to multi-environment data on barle

Optimal Optimisation in Chemometrics

Contains fulltext : 19462.pdf (publisher's version ) (Open Access)The use of global optimisation methods is not straightforward, especially for the more difficult optimisation problems. Solutions have to be found for items such as the evaluation function, representation, step function and meta-parameters, before any useful results can be obtained. This thesis aims at investigating and improving the use of global optimisation algorithms. In particular, this thesis will focus at three specific problems which are associated with global optimisation. (1) It aims at finding a similarity criterion which deals with the problem of correctly comparing spectra when many shifted peaks are present and that can be used as an evaluation function for optimisation purposes. (2) TS is a relatively new optimisation technique with different characteristics compared to SA and GA's. By implementing TS to solve several chemical optimisation problems, this thesis investigates the properties and the possibilities of TS. (3) Unfortunately, for all three methods, there exists no standard recipe on how or when to use SA, GA's or TS. By studying and solving several chemical optimisation problems, a third goal is to detect guidelines on how and when to use global optimisation algorithmsRU Radboud Universiteit Nijmegen, 24 juni 2004Promotor : Buydens, L.M.C. Co-promotor : Wehrens, H.R.M.J.164 p

Temperature robust multivariate calibration: methods for dealing with temperature influences on NIR-spectra.

Multivariate calibration is a powerful tool for establishing a relationship between spectral variables and properties of interest. Usually, changes in spectral variables are ascribed to changes in the chemical composition of the sample. However, spectral intensities that are measured at varying temperatures do not only change because of changes in sample composition but also respond to the change in temperature. In these cases, multivariate calibration can be (severely) hindered, resulting in a loss of prediction capabilities. This paper provides an overview of the characteristics and possibilities of (most) methods for temperature robust multivariate calibration. The methods are discussed by using two data sets. © NIR Publication