Perform Hierarchical Clustering on Methylation Data

Produces a hierarchical clustering tree based on the input matrices of counts.

Usage

fuse.cluster(x, ...)

# Default S3 method
fuse.cluster(x, K1, chr = NULL, pos = NULL, ...)

# S3 method for class 'BSseq'
fuse.cluster(x, ...)

# S3 method for class 'methrix'
fuse.cluster(x, ...)

Arguments

x

Input object. One of:

matrix: Unmethylated count matrix (K0).
BSseq: A BSseq object.
methrix: A methrix object.

...

Additional arguments if K0 is a matrix.

K1

Methylated count matrix (if x is matrix).

chr

Chromosome labels (if x is matrix).

pos

Genomic positions (if x is matrix.

Value

A clustering tree of class hclust.

Examples

# Example: Clustering generated data
set.seed(1234)
K0 <- matrix(
  rep(c(sample(0:20, 200, replace = TRUE), sample(20:40, 200, replace = TRUE)), 2),
  nrow = 100, byrow = TRUE
)
K1 <- matrix(
  rep(c(sample(20:40, 200, replace = TRUE), sample(0:20, 200, replace = TRUE)), 2),
  nrow = 100, byrow = TRUE
)
tree <- fuse.cluster(K0, K1)
tree
#> 
#> Call:
#> "fuse.cluster(k0, k1, pos, chr)"
#> 
#> Cluster method   : fuse 
#> Distance         : fuse 
#> Number of objects: 100 
#>