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:
hicClust, which are briefly explained below.
You can install adjclust from github with:
# install.packages("devtools") devtools::install_github("pneuvial/adjclust")
adjClust performs adjacency-constrained HAC for standard and sparse, similarity and dissimilarity matrices 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 dendogram (as shown above). Successive merge and heights of clustering can be obtained by
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") #> Loading required package: survival #> Loading required package: Matrix 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) #> Note: 125 merges with non increasing heights. plot(fit)
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.
data("hic_imr90_40_XX", package = "adjclust") binned <- binningC(hic_imr90_40_XX, binsize = 5e5) #> Bin size 'xgi' =501579 [1x501579] #> Bin size 'ygi' =501579 [1x501579] mapC(binned) #> minrange= 12 - maxrange= 1022
fitB <- hicClust(binned) #> Note: 32 merges with non increasing heights. plot(fitB)
Version 0.4.0 of this package was completed by Shubham Chaturvedi as a part of the Google Summer of Code 2017 program.