MEDME                 package:MEDME                 R Documentation

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_D_e_s_c_r_i_p_t_i_o_n:

     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.

_U_s_a_g_e:

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

_A_r_g_u_m_e_n_t_s:

    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 

_D_e_t_a_i_l_s:

     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.

_V_a_l_u_e:

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

_R_e_f_e_r_e_n_c_e_s:

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

_S_e_e _A_l_s_o:

     'smooth', 'CGcount'

_E_x_a_m_p_l_e_s:

     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)

