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>.
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:
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 <- 3fit <- 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: Matrixdata("ld.example", package = "snpStats")geno <- ceph.1mb[, -316] ## drop one SNP leading to one missing LD valueh <- 100ld.ceph <- ld(geno, stats = "R.squared", depth = h)image(ld.ceph, lwd = 0)
fit <- snpClust(geno, stats = "R.squared", h = h)#> Note: 132 merges with non increasing heights.plot(fit)#> Warning in plot.chac(fit):#> Detected reversals in dendrogram: mode = 'corrected', 'within-disp' or 'total-disp' might be more relevant.
sel_clust <- select(fit, "bs")plotSim(as.matrix(ld.ceph), clustering = sel_clust, dendro = 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.
library("HiTC")#> Warning: multiple methods tables found for 'acbind'#> Warning: multiple methods tables found for 'arbind'#> Warning: multiple methods tables found for 'rglist'
load(system.file("extdata", "hic_imr90_40_XX.rda", package = "adjclust"))binned <- binningC(hic_imr90_40_XX, binsize = 5e5)#> Bin size 'xgi' =500488 [1x500488]#> Bin size 'ygi' =500488 [1x500488]mapC(binned)#> minrange= 104 - maxrange= 36776.8
fitB <- hicClust(binned)#> Note: 5 merges with non increasing heights.plot(fitB)#> Warning in plot.chac(fitB):#> Detected reversals in dendrogram: mode = 'corrected', 'within-disp' or 'total-disp' might be more relevant.
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.