ArticleAlgorithms for molecular biology : AMB2019
Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomics.
Article in Algorithms for molecular biology : AMB, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 0 citations in OpenAlex.
- TADShop: systematic benchmarking and identification of topologically associating domains.Nature methods · 2026Article
- A comparative analysis of topological domain callers over RNA-associated interactome.BMC bioinformatics · 2026Article
- Sparse multitask group Lasso for genome-wide association studies.PLoS computational biology · 2025Article
- Genome structural dynamics: insights from Gaussian network analysis of Hi-C data.Briefings in functional genomics · 2024Article
- Neuron-level explainable AI for Alzheimer's Disease assessment from fundus images.Scientific reports · 2024Article
- The shape of chromatin: insights from computational recognition of geometric patterns in Hi-C data.Briefings in bioinformatics · 2023Review
- Genetic effects on molecular network states explain complex traits.Molecular systems biology · 2023Article
- Association between Days Open and Parity, Calving Season or Milk Spectral Data.Animals : an open access journal from MDPI · 2023Article
- The three-dimensional landscape of cortical chromatin accessibility in Alzheimer's disease.Nature neuroscience · 2022Article
- A comparison of topologically associating domain callers over mammals at high resolution.BMC bioinformatics · 2022Article
- Chromatin domain alterations linked to 3D genome organization in a large cohort of schizophrenia and bipolar disorder brains.Nature neuroscience · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenomic 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. But 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
resultsBy assuming that the similarity between physically distant objects is negligible, we are able to propose an implementation of adjacency-constrained HAC with quasi-linear complexity. This is achieved by pre-calculating specific sums of similarities, and storing candidate fusions in a min-heap. 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,
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