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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")adjClustadjClust 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.
snpClustsnpClust 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)
#> Warning in run.snpClust(x, h = h, stats = stats): Forcing the LD similarity to
#> be smaller than or equal to 1
#> Note: 133 merges with non increasing heights.
plot(fit)
#> Warning:
#> 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)
#> Warning:
#> Detected reversals in dendrogram: mode = 'corrected', 'within-disp' or 'total-disp' might be more relevant.
#> Warning: Removed 602 rows containing missing values (`geom_text()`).
hicClusthicClust 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)
#> 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:
#> 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
#> Warning:
#> Detected reversals in dendrogram: mode = 'corrected', 'within-disp' or 'total-disp' might be more relevant.
#> Warning: Removed 41 rows containing missing values (`geom_text()`).
Version 0.4.0 of this package was completed by Shubham Chaturvedi as a part of the Google Summer of Code 2017 program.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.