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

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

Arguments

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

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(MEDME)

Attaching package: 'MEDME'

The following object is masked from 'package:stats':

    smooth

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/MEDME/MEDME.predict.Rd_%03d_medium.png", width=480, height=480)
> ### Name: MEDME.predict
> ### Title: Applying the logistic model on MeDIP enrichment data
> ### Aliases: MEDME.predict
> 
> ### ** Examples
> 
> data(testMEDMEset)
> ## just an example with the first 1000 probes
> testMEDMEset = smooth(data = testMEDMEset[1:1000, ])
chrX  
> library(BSgenome.Hsapiens.UCSC.hg18)
Loading required package: BSgenome
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: GenomicRanges

Attaching package: 'GenomicRanges'

The following object is masked from 'package:MEDME':

    pos

Loading required package: Biostrings
Loading required package: XVector
Loading required package: rtracklayer
> testMEDMEset = CGcount(data = testMEDMEset)
chrX  
> 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')
[1] "chrX"
done
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>