MEDME.predict: Applying the logistic model on MeDIP enrichment data
In MEDME: Modelling Experimental Data from MeDIP Enrichment

Description Usage Arguments Value References See Also Examples

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

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

`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

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.

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

smooth, CGcount, MEDME

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')