View source: R/segmentDensity.R
segmentDensity | R Documentation |
This function allows for various methods (see type
)
of segmenting based on the density of features x
.
segmentDensity(x, n, L_s = 1e+06, exclude = NULL, type = c("cbs", "hmm"))
x |
the input GRanges, e.g. genes |
n |
the number of states |
L_s |
segment length |
exclude |
GRanges of excluded region |
type |
the type of segmentation, either
Circular Binary Segmentation |
a GRanges with metadata columns containing:
state segmentation state
counts average number of genes
Circular binary segmentation (CBS):
Olshen, A. B., E. S. Venkatraman, R. Lucito, and M. Wigler. 2004. "Circular binary segmentation for the analysis of array-based DNA copy number data." Biostatistics 5 (4): 557–72.
Hidden Markov Model from RcppHMM:
Roberto A. Cardenas-Ovando, Julieta Noguez, and Claudia Rangel-Escareno. "Rcpp Hidden Markov Model." CRAN R package.
n <- 10000
library(GenomicRanges)
gr <- GRanges("chr1", IRanges(round(
c(runif(n/4,1,991), runif(n/4,1001,3991),
runif(n/4,4001,4991), runif(n/4,7001,9991))),
width=10), seqlengths=c(chr1=10000))
gr <- sort(gr)
exclude <- GRanges("chr1", IRanges(5001,6000), seqlengths=c(chr1=10000))
seg <- segmentDensity(gr, n=3, L_s=100, exclude=exclude, type="cbs")
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.