hicClust {adjclust} | R Documentation |
Adjacency-constrained hierarchical agglomerative clustering of Hi-C contact maps
hicClust(x, h = NULL, log = FALSE, ...)
x |
either: 1. A pxp contact sparse or dense matrix (classes matrix, Matrix, dscMatrix, dgTMatrix, dgCMatrix, dgeMatrix). Its entries are the number of counts of physical interactions observed between all pairs of loci. 2. An object of class HiTC::HTCexp. The corresponding Hi-C data is stored as a Matrix::dsCMatrix object in the intdata slot. 3. A text file path with one line per pair of loci for which an interaction has been observed (in the format: locus1<tab>locus2<tab>signal) or a matrix or data frame with similar data (3 columns). |
h |
band width. If not provided, |
log |
logical. Whether to log-transform the count data. Default to
|
... |
further arguments to be passed to |
Adjacency-constrained hierarchical agglomerative clustering (HAC) is HAC in which each observation is associated to a position, and the clustering is constrained so as only adjacent clusters are merged. Genomic regions (loci) are clustered according to information provided by high-throughput conformation capture data (Hi-C).
An object of class chac
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Dehman A. (2015) Spatial Clustering of Linkage Disequilibrium Blocks for Genome-Wide Association Studies, PhD thesis, Universite Paris Saclay.
Servant N. et al (2012). HiTC : Exploration of High-Throughput 'C' experiments. Bioinformatics.
# input as HiTC::HTCexp object ## Not run: if (require("HiTC", quietly = TRUE)) { load(system.file("extdata", "hic_imr90_40_XX.rda", package = "adjclust")) res1 <- hicClust(hic_imr90_40_XX) } ## End(Not run) # input as Matrix::dsCMatrix contact map ## Not run: mat <- HiTC::intdata(hic_imr90_40_XX) res2 <- hicClust(mat) ## End(Not run) # input as text file res3 <- hicClust(system.file("extdata", "sample.txt", package = "adjclust"))