predict {MEDME}R Documentation

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

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

[Package MEDME version 1.2.1 Index]