MEDME (version 1.32.0)

MEDME.predict: Applying the logistic model on MeDIP enrichment data

Description

This allows the probe-level determination of MeDIP smoothed data, as well as absolute and relative methylation levels (AMS and RMS respectively)

Usage

MEDME.predict(data, MEDMEfit, MEDMEextremes = c(1,32), wsize = 1000, wFunction='linear')

Arguments

data

An object of class MEDMEset

MEDMEfit

the model obtained from the MEDME.model function

MEDMEextremes

vector; the background and saturation values as determined by the fitting of the model on the calibration data

wsize

number; the size of the smoothing window, in bp

wFunction

string; the type of weighting function, to choose among linear, exp, log or none

Value

An object of class MEDMEset. The resulting smoothed data, the absolute and relative methylation score (AMS and RMS) are saved in the smoothed, AMS and RMS slots, respectively.

References

http://genome.cshlp.org/cgi/content/abstract/gr.080721.108v1

See Also

smooth, CGcount, MEDME

Examples

data(testMEDMEset)
## just an example with the first 1000 probes
testMEDMEset = smooth(data = testMEDMEset[1:1000, ])
library(BSgenome.Hsapiens.UCSC.hg18)
testMEDMEset = CGcount(data = testMEDMEset)
MEDMEmodel = MEDME(data = testMEDMEset, sample = 1, CGcountThr = 1, figName = NULL)
testMEDMEset = MEDME.predict(data = testMEDMEset, MEDMEfit = MEDMEmodel, MEDMEextremes = c(1,32), wsize = 1000, wFunction='linear')