MEDME: Determining the logistic model of MeDIP enrichment in respect...
In MEDME: Modelling Experimental Data from MeDIP Enrichment

Description Usage Arguments Details Value References See Also Examples

Probe-level MeDIP weighted enrichment is compared to the expected DNA methytlation level. The former is determined applying MeDIP protocol to a fully methylated DNA. The latter is determined as the count of CpGs for each probe. This is assumed to be the methylation level of each probe in a fully methylated sample.

1	MEDME(data, sample, CGcountThr = 1, figName = NULL)

`data`	An object of class MEDMEset
`sample`	Integer; the number of the sample to be used to fit the model, based on the order of samples in the smoothed slot
`CGcountThr`	number; the threshold to avoid modelling probes with really low methylation level, i.e. CpG count
`figName`	string; the name of the file reporting the model fitting

The model should be applied on calibration data containing MeDIP enrichment of fully methylated DNA, most likely artificially generated (see references). Nevertheless, in case chromosome or genome-wide human tiling arrays are used a regular sample could be used too. In fact, human genomic DNA is known to be hyper-methylated but in the promoter regions. Of course the performance of the method is expected to be somehow affected by this approximation.

The logistic model as returned from the multdrc function from the drc R library

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

smooth, CGcount

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)