hicClust: Adjacency-constrained Clustering of Hi-C contact maps

In adjclust: Adjacency-Constrained Clustering of a Block-Diagonal Similarity Matrix

Adjacency-constrained Clustering of Hi-C contact maps

Description

Adjacency-constrained hierarchical agglomerative clustering of Hi-C contact maps

Usage

hicClust(x, h = NULL, log = FALSE, ...)

Arguments

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, h is set to default value 'p-1'.
log	logical. Whether to log-transform the count data. Default to FALSE.
...	further arguments to be passed to read.table function when x is a text file name. If not provided, the text file is supposed to be separated by tabulations, with no header.

Details

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).

Value

An object of class chac.

References

Ambroise C., Dehman A., Neuvial P., Rigaill G., and Vialaneix N (2019). Adjacency-constrained hierarchical clustering of a band similarity matrix with application to genomics, Algorithms for Molecular Biology 14(22)"

Servant N. et al (2012). HiTC : Exploration of High-Throughput 'C' experiments. Bioinformatics.

See Also

adjClust

Examples

# 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"))

adjclust documentation built on April 28, 2023, 1:10 a.m.