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Adjacency-constrained hierarchical clustering of a band similarity matrix with application to Genomics

Abstract : Motivation: Genomic data analyses such as Genome-Wide Association Studies (GWAS) or Hi-C studies are often faced with the problem of partitioning chromosomes into successive regions based on a similarity matrix of high-resolution, locus-level measurements. An intuitive way of doing this is to perform a modified Hierarchical Agglomerative Clustering (HAC), where only adjacent clusters (according to the ordering of positions within a chromosome) are allowed to be merged. A major practical drawback of this method is its quadratic time and space complexity in the number of loci, which is typically of the order of 10^4 to 10^5 for each chromosome. Results: By assuming that the similarity between physically distant objects is negligible, we propose an implementation of this adjacency-constrained HAC with quasi-linear complexity. Our illustrations on GWAS and Hi-C datasets demonstrate the relevance of this assumption, and show that this method highlights biologically meaningful signals. Thanks to its small time and memory footprint, the method can be run on a standard laptop in minutes or even seconds. Availability and Implementation: Software and sample data are available as an R package, adjclust, that can be downloaded from the Comprehensive R Archive Network (CRAN).
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Contributor : Pierre Neuvial <>
Submitted on : Sunday, November 24, 2019 - 9:57:59 PM
Last modification on : Thursday, July 16, 2020 - 3:06:11 PM
Document(s) archivé(s) le : Tuesday, February 25, 2020 - 1:53:17 PM



Christophe Ambroise, Alia Dehman, Pierre Neuvial, Guillem Rigaill, Nathalie Vialaneix. Adjacency-constrained hierarchical clustering of a band similarity matrix with application to Genomics. Algorithms for Molecular Biology, BioMed Central, 2019, 14 (22), pp.22. ⟨10.1186/s13015-019-0157-4⟩. ⟨hal-02006331v2⟩



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