Color bias adjustment of Illumina Infinium methylaton microarrays using smooth quantile normalization

Description

Color bias adjustment of Illumina Infinium methylaton microarrays using smooth quantile normalization smoothQuantileNormalization

Usage

adjColorBias.quantile(methyLumiM, refChannel = c("green", "red"), logMode = TRUE, verbose = TRUE,...)

Arguments

Details

Perform color bias adjustment of Illumina Infinium methylaton microarrays. It requires the input methyLumiM object includes the color channel information in the featureData. Basically, there should be a "COLOR_CHANNEL" column in the data.frame returned by pData(featureData(methyLumiM)).

The basic idea of color bias adjustment is to treat it as the normalization between two color channels. It uses smooth quantile normalization smoothQuantileNormalization to normalize two color channels.

Value

Return an object (same class as input methyLumiM) with updated "methylated" and "unmethylated" data matrix after color bias adjustment.

Author(s)

Pan DU

See Also

See Also lumiMethyC, smoothQuantileNormalization and adjColorBias.ssn

Examples

data(example.lumiMethy)
# before adjustment
plotColorBias1D(example.lumiMethy)
lumiMethy.adj = adjColorBias.quantile(example.lumiMethy)
# after adjustment
plotColorBias1D(lumiMethy.adj)

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(lumi)
Loading required package: Biobase
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

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Setting options('download.file.method.GEOquery'='auto')
Setting options('GEOquery.inmemory.gpl'=FALSE)
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/lumi/adjColorBias.quantile.Rd_%03d_medium.png", width=480, height=480)
> ### Name: adjColorBias.quantile
> ### Title: Color bias adjustment of Illumina Infinium methylaton
> ###   microarrays using smooth quantile normalization
> ### Aliases: adjColorBias.quantile
> ### Keywords: methods
> 
> ### ** Examples
> 
> data(example.lumiMethy)
> # before adjustment
> plotColorBias1D(example.lumiMethy)
> lumiMethy.adj = adjColorBias.quantile(example.lumiMethy)
Processing sample Treat1 ...
Processing sample Treat2 ...
Processing sample Treat3 ...
Processing sample Treat4 ...
Processing sample Ctrl1 ...
Processing sample Ctrl2 ...
Processing sample Ctrl3 ...
Processing sample Ctrl4 ...
Processing sample Treat1.rep ...
Processing sample Treat2.rep ...
Processing sample Treat3.rep ...
Processing sample Treat4.rep ...
Processing sample Ctrl1.rep ...
Processing sample Ctrl2.rep ...
Processing sample Ctrl3.rep ...
Processing sample Ctrl4.rep ...
> # after adjustment
> plotColorBias1D(lumiMethy.adj)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>