`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")
::install_github("pneuvial/adjclust") devtools
```

`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")
<- matrix(c(1.0, 0.5, 0.2, 0.1,
sim 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)
<- 3
h <- adjClust(sim, "similarity", h)
fit 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")
#> Loading required package: survival
#> Loading required package: Matrix
data("ld.example", package = "snpStats")
<- ceph.1mb[, -316] ## drop one SNP leading to one missing LD value
geno <- 100
h <- ld(geno, stats = "R.squared", depth = h)
ld.ceph image(ld.ceph, lwd = 0)
```

```
<- snpClust(geno, stats = "R.squared", h = h)
fit #> Warning in run.snpClust(x, h = h, stats = stats): Forcing the LD similarity to
#> be smaller than or equal to 1
#> as(<dsTMatrix>, "dgTMatrix") is deprecated since Matrix 1.4-2; do as(., "generalMatrix") instead
#> Note: 133 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.
```

```
<- select(fit, "bs")
sel_clust 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"))
<- binningC(hic_imr90_40_XX, binsize = 5e5)
binned #> Bin size 'xgi' =500488 [1x500488]
#> Bin size 'ygi' =500488 [1x500488]
mapC(binned)
#> minrange= 104 - maxrange= 36776.8
```

```
<- hicClust(binned)
fitB #> 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.