knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.path = "man/figures/README-" )
adjclust
is a package that provides methods to perform adjacency-constrained hierarchical agglomerative clustering. Adjacency-constrained hierarchical agglomerative clustering is hierarchical agglomerative clustering (HAC) in which each observation is associated to a position, and the clustering is constrained so as only adjacent clusters are merged. It is useful in bioinformatics (e.g. Genome Wide Association Studies or Hi-C data analysis).
adjclust
provides three user level functions: adjClust
, snpClust
and hicClust
, which are briefly explained below.
You can install adjclust from github with:
# install.packages("devtools") devtools::install_github("pneuvial/adjclust")
adjClust
adjClust
performs adjacency-constrained HAC for standard and sparse, similarity and dissimilarity matrices and dist
objects. Matrix::dgCMatrix
and Matrix::dsCMatrix
are the supported sparse matrix classes. Let's look at a basic example
library("adjclust") sim <- matrix(c(1.0, 0.5, 0.2, 0.1, 0.5, 1.0, 0.1, 0.2, 0.2, 0.1, 1.0, 0.6, 0.1, 0.2 ,0.6 ,1.0), nrow=4) h <- 3 fit <- adjClust(sim, "similarity", h) plot(fit)
The result is of class chac
. It can be plotted as a dendrogram (as shown above). Successive merge and heights of clustering can be obtained by fit$merge
and fit$height
respectively.
snpClust
snpClust
performs adjacency-constrained HAC for specific application of Genome Wide Association Studies (GWAS). A minimal example is given below. See GWAS Vignette for details.
library("snpStats") data("ld.example", package = "snpStats") geno <- ceph.1mb[, -316] ## drop one SNP leading to one missing LD value h <- 100 ld.ceph <- ld(geno, stats = "R.squared", depth = h) image(ld.ceph, lwd = 0) fit <- snpClust(geno, stats = "R.squared", h = h) plot(fit) sel_clust <- select(fit, "bs") plotSim(as.matrix(ld.ceph), clustering = sel_clust, dendro = fit)
hicClust
hicClust
performs adjacency-constrained HAC for specific application of Hi-C data analysis. A minimal example is given below. See Hi-C Vignette for details.
library("HiTC")
load(system.file("extdata", "hic_imr90_40_XX.rda", package = "adjclust")) binned <- binningC(hic_imr90_40_XX, binsize = 5e5) mapC(binned) fitB <- hicClust(binned) plot(fitB) plotSim(intdata(binned), dendro = fitB) # default: log scale for colors
Version 0.4.0 of this package was completed by Shubham Chaturvedi as a part of the Google Summer of Code 2017 program.
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