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 : Monday, February 4, 2019 - 3:26:54 PM
Last modification on : Monday, April 29, 2019 - 4:49:52 PM
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  • HAL Id : hal-02006331, version 1
  • ARXIV : 1902.01596


Christophe Ambroise, Alia Dehman, Pierre Neuvial, Guillem Rigaill, Nathalie Vialaneix. Adjacency-constrained hierarchical clustering of a band similarity matrix with application to Genomics. 2019. ⟨hal-02006331⟩



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