Clustering transformed compositional data using K-means, with applications in gene expression and bicycle sharing system data. Issue 1 (2nd January 2019)

• Record Type: Journal Article
• Authors: Godichon-Baggioni, Antoine; Maugis-Rabusseau, Cathy; Rau, Andrea
• Is Part Of: Journal of applied statistics. Volume 46:Issue 1(2019)
• Page Start: 47
• Page End: 65
• Publication Date: 2019-01-02
• Subjects: Clustering -- compositional data -- data transformations -- K-means; Statistics -- Periodicals; 519.5
• DOI: 10.1080/02664763.2018.1454894
• Languages: English
• ISSNs: 0266-4763

Abstract

ABSTRACT: Although there is no shortage of clustering algorithms proposed in the literature, the question of the most relevant strategy for clustering compositional data (i.e. data whose rows belong to the simplex) remains largely unexplored in cases where the observed value is equal or close to zero for one or more samples. This work is motivated by the analysis of two applications, both focused on the categorization of compositional profiles: (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 make use of appropriately chosen data transformations, including the Centered Log Ratio and a novel extension called the Log Centered Log Ratio, in conjunction with the K -means algorithm. We use a non-asymptotic penalized criterion, whose penalty is calibrated with the slope heuristics, to select the number of clusters. Finally, we illustrate the performance of this clustering strategy, which is implemented in the Bioconductor packagecoseq, on both the gene expression and bicycle sharing system data.