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adjclust: Adjacency-Constrained Clustering of a Block-Diagonal Similarity Matrix
Implements a constrained version of hierarchical agglomerative clustering, in which each observation is associated to a position, and only adjacent clusters can be merged. Typical application fields in bioinformatics include Genome-Wide Association Studies or Hi-C data analysis, where the similarity between items is a decreasing function of their genomic distance. Taking advantage of this feature, the implemented algorithm is time and memory efficient. This algorithm is described in Ambroise et al (2019) <doi:10.1186/s13015-019-0157-4>.
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Package details |
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| Author | Christophe Ambroise [aut], Shubham Chaturvedi [aut], Alia Dehman [aut], Pierre Neuvial [aut, cre] (<https://orcid.org/0000-0003-3584-9998>), Guillem Rigaill [aut], Nathalie Vialaneix [aut] (<https://orcid.org/0000-0003-1156-0639>), Gabriel Hoffman [aut] |
| Bioconductor views | Clustering FeatureExtraction |
| Maintainer | Pierre Neuvial <pierre.neuvial@math.univ-toulouse.fr> |
| License | GPL-3 |
| Version | 0.6.10 |
| URL | https://pneuvial.github.io/adjclust/ |
| Package repository | View on CRAN |
| Installation |
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