Subspace K-means clustering

Timmerman, Marieke E.; Ceulemans, Eva; Roover, Kim De; Leeuwen, Karla Van

doi:10.3758/s13428-013-0329-y

Cited by 57 publications

(47 citation statements)

References 52 publications

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“…when performances was stable) and used a k -mean clustering separation with Statistica ® software (version 12) (Timmerman et al 2013), so that animal belonged to a set that had the closest mean to its own performance value. Three groups were defined: animals which chose mostly advantageous options at the end of the experiment, thereafter called “safe” group, animals which explored the different options at the end of the experiment, thereafter called “risky”, and animals which exhibited an intermediate behavior and distributed their choices between sporadic risky choices and high proportion of advantageous choices, thereafter called “average”.…”

Section: Methodsmentioning

confidence: 99%

Individual behavioral and neurochemical markers of unadapted decision-making processes in healthy inbred mice

et al. 2016

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One of the hallmarks of decision-making processes is the inter-individual variability between healthy subjects. These behavioral patterns could constitute risk factors for the development of psychiatric disorders. Therefore, finding predictive markers of safe or risky decision-making is an important challenge for psychiatry research. We set up a mouse gambling task (MGT)—adapted from the human Iowa gambling task with uncertain contingencies between response and outcome that furthermore enables the emergence of inter-individual differences. Mice (n = 54) were further individually characterized for locomotive, emotional and cognitive behavior. Individual basal rates of monoamines and brain activation after the MGT were assessed in brain regions related to reward, emotion or cognition. In a large healthy mice population, 44 % showed a balanced strategy with limited risk-taking and flexible choices, 29 % showed a safe but rigid strategy, while 27 % adopted risky behavior. Risky mice took also more risks in other apparatus behavioral devices and were less sensitive to reward. No difference existed between groups regarding anxiety, working memory, locomotion and impulsivity. Safe/rigid mice exhibited a hypoactivation of prefrontal subareas, a high level of serotonin in the orbitofrontal cortex combined with a low level of dopamine in the putamen that predicted the emergence of rigid behavior. By contrast, high levels of dopamine, serotonin and noradrenalin in the hippocampus predicted the emergence of more exploratory and risky behaviors. The coping of C57bl/6J mice in MGT enables the determination of extreme patterns of choices either safe/rigid or risky/flexible, related to specific neurochemical and behavioral markers.Electronic supplementary materialThe online version of this article (doi:10.1007/s00429-016-1192-2) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Individual behavioral and neurochemical markers of unadapted decision-making processes in healthy inbred mice

et al. 2016

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“…1 Using PCA to discover natural products unique to group 7 (Hou et al 2012) Computational and statistical analysis of metabolomics data Soete and Carroll 1994). Many methods have been proposed to improve the original reduced K-means (De Soete and Carroll 1994), including factorial K-means (Vichi and Kiers 2001) and subspace K-means (Timmerman et al 2013). An alternative solution is using variable selection (Steinley and Brusco 2008) or variable weighting (Huang et al 2005).…”

Section: Clusteringmentioning

confidence: 99%

Computational and statistical analysis of metabolomics data

et al. 2015

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Metabolomics is the comprehensive study of small molecule metabolites in biological systems. By assaying and analyzing thousands of metabolites in biological samples, it provides a whole picture of metabolic status and biochemical events happening within an organism and has become an increasingly powerful tool in the disease research. In metabolomics, it is common to deal with large amounts of data generated by nuclear magnetic resonance (NMR) and/or mass spectrometry (MS). Moreover, based on different goals and designs of studies, it may be necessary to use a variety of data analysis methods or a combination of them in order to obtain an accurate and comprehensive result. In this review, we intend to provide an overview of computational and statistical methods that are commonly applied to analyze metabolomics data. The review is divided into five sections. The first two sections will introduce the background and the databases and resources available for metabolomics research. The third section will briefly describe the principles of the two main experimental methods that produce metabolomics data: MS and NMR, followed by the fourth section that describes the preprocessing of the data from these two approaches. In the fifth and the most important section, we will review four main types of analysis that can be performed on metabolomics data with examples in metabolomics. These are unsupervised learning methods, supervised learning methods, pathway analysis methods and analysis of time course metabolomics data. We conclude by providing a table summarizing the principles and tools that we discussed in this review.

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“…As examples, one may think of the loading matrices that result from fitting mixtures of factor analyzers (McLachlan & Peel, 2000;Yung, 1997), a subspace k-means analysis (Timmerman, Ceulemans, De Roover & Van Leeuwen, 2013), or a switching principal component analysis (De Roover, Timmerman, Van Diest, Onghena & Ceulemans, 2014b).…”

Section: Discussionmentioning

confidence: 99%

How to detect which variables are causing differences in component structure among different groups

Roover¹,

Timmerman

Ceulemans³

2015

Behav Res

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When comparing the component structures of a multitude of variables across different groups, the conclusion often is that the component structures are very similar in general and differ in a few variables only. Detecting such Boutlying variables^is substantively interesting. Conversely, it can help to determine what is common across the groups. This article proposes and evaluates two formal detection heuristics to determine which variables are outlying, in a systematic and objective way. The heuristics are based on clusterwise simultaneous component analysis, which was recently presented as a useful tool for capturing the similarities and differences in component structures across groups. The heuristics are evaluated in a simulation study and illustrated using crosscultural data on values.

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Subspace K-means clustering

Cited by 57 publications

References 52 publications

Individual behavioral and neurochemical markers of unadapted decision-making processes in healthy inbred mice

Individual behavioral and neurochemical markers of unadapted decision-making processes in healthy inbred mice

Computational and statistical analysis of metabolomics data

How to detect which variables are causing differences in component structure among different groups

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