predict {MEDME} | R Documentation |
This allows the probe-level determination of MeDIP smoothed data, as well as absolute and relative methylation levels (AMS and RMS respectively)
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
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 = predict(data = testMEDMEset, MEDMEfit = MEDMEmodel, MEDMEextremes = c(1,32), wsize = 1000, wFunction='linear')