changeMaxPostCutoff {chromstaR} | R Documentation |
Adjusts the peak calls of a uniHMM
, multiHMM
or combinedMultiHMM
object with a cutoff on the maximum-posterior within each peak. Higher values of maxPost.cutoff
mean less sensitive and more precise peak calls. Remaining peaks are kept intact, as opposed to function changePostCutoff
, where broad peaks are fragmented. This function was formerly called 'changeFDR' and is still available for backwards compatibiltiy.
changeMaxPostCutoff(model, maxPost.cutoff = 0.99, invert = FALSE) changeFDR(model, fdr = 0.01, invert = FALSE)
model |
|
maxPost.cutoff |
A vector of values between 0 and 1 for each column in |
invert |
Select peaks below ( |
fdr |
Same as |
Each peak has a maximum-posterior (maxPostInPeak, between 0 and 1) associated. The sensitivity is adjusted with a simple cutoff on maxPostInPeak, e.g. for maxPost.cutoff = 0.99
only peaks with maxPostInPeak >= 0.99
will be selected.
The input object is returned with adjusted peak calls.
changeFDR
: This function was renamed to 'changeMaxPostCutoff' in chromstaR 1.5.1 but it still available for backwards compatibility.
Aaron Taudt
## Get an example uniHMM ## file <- system.file("data","H3K27me3-BN-rep1.RData", package="chromstaR") model <- get(load(file)) ## Compare fits with different fdrs plotHistogram(model) + ylim(0,0.25) + ylim(0,0.3) plotHistogram(changeMaxPostCutoff(model, maxPost.cutoff=0.99)) + ylim(0,0.3) plotHistogram(changeMaxPostCutoff(model, maxPost.cutoff=1-1e-12)) + ylim(0,0.3) ## Get an example multiHMM ## file <- system.file("data","multivariate_mode-combinatorial_condition-SHR.RData", package="chromstaR") model <- get(load(file)) genomicFrequencies(model) model.new <- changeMaxPostCutoff(model, maxPost.cutoff=0.9999, invert=FALSE) genomicFrequencies(model.new) ## Get an example combinedMultiHMM ## file <- system.file("data","combined_mode-differential.RData", package="chromstaR") model <- get(load(file)) genomicFrequencies(model) model.new <- changeMaxPostCutoff(model, maxPost.cutoff=0.9999, invert=FALSE) genomicFrequencies(model.new)