cherByThreshold {Ringo} | R Documentation |
Given a vector of probe positions on the chromosome, a vector of smoothed intensities on these positions, and a threshold for intensities to indicated enrichment, this function identifies Chers (ChIP-enriched regions) on this chromosome.
This function is called by the function findChersOnSmoothed
.
cherByThreshold(positions, scores, threshold, distCutOff, minProbesInRow = 3)
positions |
numeric vector of genomic positions of probes |
scores |
scores (intensities) of probes on those positions |
threshold |
threshold for scores to be called a cher |
distCutOff |
maximal positional distance between two probes to be part of the same cher |
minProbesInRow |
integer; minimum number of enriched
probes required for a cher; see details for further
explanation. |
Specifying a minimum number of probes for a cher (argument
minProbesInRow
) guarantees that a cher is supported by a
reasonable number of measurements in probe-sparse regions.
For example, if there's only one enriched probe within a certain
genomic 1kb region and no other probes can been mapped to that
region, this single probe does arguably not provide enough evidence
for calling this genomic region enriched.
A LIST with n components, where the first n components are the cher clusters, each one holding the scores and, as their names, the genomic positions of probes in that cluster.
Joern Toedling toedling@ebi.ac.uk
## example with random generated data: rpos <- cumsum(round(runif(200)*5)) rsco <- rnorm(200)+0.2 plot(rpos, rsco, type="l", col="seagreen3", lwd=2) rug(rpos, side=1, lwd=2); abline(h=0, lty=2) rchers <- cherByThreshold(rpos, rsco, threshold=0, distCutOff=2) sapply(rchers[-length(rchers)], function(thisClust){ points(x=as.numeric(names(thisClust)), y=thisClust, type="h", lwd=2, col="gold")})