Desire thinking as a confounder in the relationship between mindfulness and craving: Evidence from a cross-cultural validation of the Desire Thinking Questionnaire.

Desire thinking and mindfulness have been associated with craving. The aim of the present study was to validate the French version of the Desire Thinking Questionnaire (DTQ) and to investigate the relationship between mindfulness, desire thinking and craving among a sample of university students. Four hundred and ninety six university students completed the DTQ and measures of mindfulness, craving and alcohol use. Results from confirmatory factor analyses showed that the two-factor structure proposed in the original DTQ exhibited suitable goodness-of-fit statistics. The DTQ also demonstrated good internal reliability, temporal stability and predictive validity. A set of linear regressions revealed that desire thinking had a confounding effect in the relationship between mindfulness and craving. The confounding role of desire thinking in the relationship between mindfulness and craving suggests that interrupting desire thinking may be a viable clinical option aimed at reducing craving

Clustering transformed compositional data using K-means, with applications in gene expression and bicycle sharing system data

International audienceAlthough there is no shortage of clustering algorithms proposed in the literature, the question of the most relevant strategy for clustering composi-tional data (i.e., data made up of profiles, whose rows belong to the simplex) remains largely unexplored in cases where the observed value of an observation is equal or close to zero for one or more samples. This work is motivated by the analysis of two sets of compositional data, both focused on the categorization of profiles but arising from considerably different applications: (1) identifying groups of co-expressed genes from high-throughput RNA sequencing data, in which a given gene may be completely silent in one or more experimental conditions ; and (2) finding patterns in the usage of stations over the course of one week in the Velib' bicycle sharing system in Paris, France. For both of these applications , we focus on the use of appropriately chosen data transformations, including the Centered Log Ratio and a novel extension we propose called the Log Centered Log Ratio, in conjunction with the K-means algorithm. We use a nonasymptotic penalized criterion, whose penalty is calibrated with the slope heuristics, to select the number of clusters present in the data. Finally, we illustrate the performance of this clustering strategy, which is implemented in the Bioconductor package coseq, on both the gene expression and bicycle sharing system data