c(person("Christophe", "Ambroise", role="aut"),
person("Shubham", "Chaturvedi", role="aut"),
person("Alia", "Dehman", role="aut"),
person("Michel", "Koskas", role="aut"),
person("Pierre", "Neuvial", role=c("aut", "cre"),
email="pierre.neuvial@math.univ-toulouse.fr"),
person("Guillem", "Rigaill", role="aut"),
person("Nathalie", "Villa-Vialaneix", role="aut"))
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
Chapter 4 of Alia Dehman (2015)
<https://hal.archives-ouvertes.fr/tel-01288568v1>.
Matrix, matrixStats, snpStats, methods, utils, HiTC,
BiocStyle, knitr, testthat, rmarkdown, rioja
Christophe Ambroise [aut],
Shubham Chaturvedi [aut],
Alia Dehman [aut],
Michel Koskas [aut],
Pierre Neuvial [aut, cre],
Guillem Rigaill [aut],
Nathalie Villa-Vialaneix [aut]

knitr, testthat, rmarkdown, rioja, HiTC, snpStats

c(person("Christophe", "Ambroise", role="aut"),
person("Shubham", "Chaturvedi", role="aut"),
person("Alia", "Dehman", role="aut"),
person("Michel", "Koskas", role="aut"),
person("Pierre", "Neuvial", role=c("aut", "cre"),
email="pierre.neuvial@math.univ-toulouse.fr"),
person("Guillem", "Rigaill", role="aut"),
person("Nathalie", "Vialaneix", role="aut"))
stats, graphics, grDevices, Matrix, matrixStats, methods,
utils, capushe
knitr, testthat, rmarkdown, rioja, HiTC, snpStats,
BiocGenerics
Christophe Ambroise [aut],
Shubham Chaturvedi [aut],
Alia Dehman [aut],
Michel Koskas [aut],
Pierre Neuvial [aut, cre],
Guillem Rigaill [aut],
Nathalie Vialaneix [aut]

c(person("Christophe", "Ambroise", role="aut"),
person("Shubham", "Chaturvedi", role="aut"),
person("Alia", "Dehman", role="aut"),
person("Pierre", "Neuvial", role=c("aut", "cre"),
email="pierre.neuvial@math.univ-toulouse.fr"),
person("Guillem", "Rigaill", role="aut"),
person("Nathalie", "Vialaneix", role="aut"))
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) <https://almob.biomedcentral.com/articles/10.1186/s13015-019-0157-4>.
Christophe Ambroise [aut],
Shubham Chaturvedi [aut],
Alia Dehman [aut],
Pierre Neuvial [aut, cre],
Guillem Rigaill [aut],
Nathalie Vialaneix [aut]