Package 'methFuse'

Perform Hierarchical Clustering on Methylation Data

Description

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

---

Cut Hierarchical Clustering Tree into Clusters

Description

Divides the clustering tree into a specified number of clusters.

Usage

fuse.cut.tree(tree, k)

Arguments

tree	Clustering tree of class hclust.
k	Number of clusters

Value

A vector indicating which cluster each element in the original data frame belonged to

Examples

# Example: Cutting small tree in 2 segments
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)

segments <- fuse.cut.tree(tree, 2)
segments

---

Full FUSE segmentation pipeline

Description

Performs the full FUSE segmentation workflow: hierarchical clustering, model selection, tree cutting, and genomic segment summarization.

Usage

fuse.segment(x, ...)

## Default S3 method:
fuse.segment(x, K1, chr, pos, method = c("BIC", "AIC"), ...)

## S3 method for class 'BSseq'
fuse.segment(x, method = c("BIC", "AIC"), ...)

## S3 method for class 'methrix'
fuse.segment(x, method = c("BIC", "AIC"), ...)

Arguments

x	Input object. One of: matrix Unmethylated count matrix (K0). BSseq A BSseq object. methrix A methrix object.
...	Additional arguments (matrix input only)
K1	Methylated count matrix (if x is matrix).
chr	Chromosome labels (if x is matrix).
pos	Genomic positions (if x is matrix.
method	Information criterion for model selection: "BIC" (default) or "AIC". For internal use, x corresponds to the unmethylated count matrix (K0).

Details

fuse.segment() is an S3 generic with methods for:

matrix

Raw count matrices (K0, K1) with genomic annotation.

BSseq

Bioconductor BSseq objects.

methrix

Bioconductor methrix objects (supports DelayedMatrix).

Value

An object of class fuse_summary, containing:

summary

Data frame with one row per genomic segment.

betas_per_segment

Matrix of per-sample methylation estimates.

raw_beta

Per-CpG methylation estimates.

raw_pos

Genomic positions of CpGs.

Automatic data extraction

For BSseq objects:

• Methylated counts are obtained via getCoverage(x, "M")

• Unmethylated counts via getCoverage(x, "Cov") - M

• Chromosome and position from rowRanges(x)

For methrix objects:

• Methylated counts via get_matrix(x, "M")

• Total coverage via get_matrix(x, "C")

• Unmethylated counts computed as C - M

• Genomic coordinates extracted from locus metadata

---

Summarize FUSE Segmentation Results

Description

Summarizes FUSE segmentation results into one row per segment, including genomic coordinates, CpG count, segment length, average methylation (beta), and stability flag based on likelihood testing. Also returns per-sample methylation estimates for each segment. Result can be visualized using plot(result).

Usage

fuse.summary(K0, K1, chr, pos, segments)

Arguments

K0	Integer or numeric matrix of unmethylated counts.
K1	Integer or numeric matrix of methylated counts.
chr	Character vector giving the chromosome for each site.
pos	Numeric vector giving genomic coordinates for each site.
segments	Integer vector giving segment IDs for each site in K0 and K1.

Value

A list with four elements:

summary

A data frame with one row per segment and the following columns:

• Segment: Segment ID

• Chr: Chromosome

• Start: Start genomic coordinate

• End: End genomic coordinate

• CpGs: Number of CpGs in the segment

• Length: Genomic length (End - Start + 1)

• Beta: Average methylation across samples and CpGs

• Coherent: Logical indicator (TRUE if segment is coherently methylated, else FALSE)

betas_per_segment

Matrix of per-sample methylation estimates for each segment (rows = segments, columns = samples).

raw_beta

Average beta per CpG site, used for plotting.

raw_pos

Genomic position for every given CpG site, used for plotting.

Examples

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)
segments <- fuse.cut.tree(tree, 4)
res <- fuse.summary(K0, K1, rep("chr1", nrow(K0)), 1:nrow(K0), segments)
head(res$summary)
head(res$betas_per_segment)

---

Find Optimal Number of Clusters

Description

Determines the optimal number of clusters to cut a hierarchical clustering tree, based on the selected information criterion (e.g., BIC or AIC).

Usage

number.of.clusters(tree, n, method = c("BIC", "AIC"))

Arguments

tree	Clustering tree of class hclust.
n	Number of samples in the original data.
method	Information criterion method. One of "BIC" or "AIC".

Value

An integer representing the optimal number of clusters.

Examples

# Example: Determine number of clusters in dummy data set
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)
k <- number.of.clusters(tree, ncol(K0), 'BIC')
k

---

Plot method for FUSE segmentation results

Description

Plotting method for fuse_summary.

Usage

## S3 method for class 'fuse_summary'
plot(x, ..., segments_to_plot = 1:50)

Arguments

x	A fuse_summary object
...	Additional arguments
segments_to_plot	Integer vector of segment indices

Details

Raw CpG-level methylation values are shown as grey points. Segment-level methylation is shown as horizontal bars (red = hypermethylated, blue = hypomethylated).

Value

No return value, called for side effects.

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