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methFuse implements FUSE: FUnctional SEgmentation of DNA methylation data through hierarchical clustering.
Either using remotes: (recommended)
# Install remotes if needed
install.packages("remotes")
# Install fuseR from GitHub
remotes::install_github("holmsusa/methFuse")or using devtools:
# Install devtools if needed
install.packages("devtools")
# Install fuseR from GitHub
devtools::install_github("holmsusa/methFuse")You may need platform-specific tools:
fuse.segment() supports the following input formats:
K0)K1)chr) and position
(pos) vectorsDelayedMatrixInstall needed packages with
BiocManager::install(c("bsseq", "methrix", "DelayedArray"))library(fuseR)
set.seed(1234)
# Generate sample data
# Unmethylated counts, T's
K0 <- matrix(
rep(c(sample(0:20, 200, replace = TRUE), sample(20:40, 200, replace = TRUE)), 2),
nrow = 100, byrow = TRUE
)
# Methylated counts, C's
K1 <- matrix(
rep(c(sample(20:40, 200, replace = TRUE), sample(0:20, 200, replace = TRUE)), 2),
nrow = 100, byrow = TRUE
)
# Perform segmentation
segment_result <- fuse.segment(
K0, K1,
chr = sub("\\..*$", "", rownames(K0)),
pos = as.numeric(sub("^.*\\.", "", rownames(K0)))
)
# Access summary and per-segment betas
head(segment_result$summary)
head(segment_result$betas_per_segment)Check out a full example workflow in the vignette.
This package is licensed under the MIT License. See LICENSE for details.
Susanna Holmstrom
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.
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