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Last data update: 2014.03.03

R: Spread vs location of probe intensities

MADvsMedianPlot

R Documentation

Spread vs location of probe intensities

Description

Plots of the spread (median absolute deviation) versus the location (median) of probe intensity levels.

Usage


MADvsMedianPlot(x, ...)

## S4 method for signature 'list'
MADvsMedianPlot(
    x,
    channel=c("G", "R"),
    group=NULL,
    subset=NULL, 
    ...)

Arguments

`x`	A list containing `MAList` and/or `NChannelSet` objects
`channel`	The channel to use for calculating distances, one of either "G" (green or control channel) or "R" (red or experimental channel)
`group`	An optional character string specifying the name of a factor to create separate panel displays, which must be in `x$genes` (for `RGList` objects)
`subset`	An optional character vector specifying the which levels of `group` to use in creating separate panel displays
`...`	arguments to pass to `densityplot`

Methods

signature(x = "list"): The method for list objects is intended to work with lists of normalized data sets, as either MAList or NChannelSet objects. This method will produce separate panel displays for each normalized data set, additionally color-coded by the group argument if supplied.

References

D. Sarkar, R. Parkin, S. Wyman, A. Bendoraite, C. Sather, J. Delrow, A. K. Godwin, C. Drescher, W. Huber, R. Gentleman, and M. Tewari. Quality assessment and data analysis for microRNA expression arrays. Nucleic Acids Res, 37(2):e17, 2009.

Examples

data(PalateData)
reducedSet <- filterArray(PalateData, keep=c("MIR", "LET", "POSCON", "CALIB"),
                          frac=1.1, number=3, reps=4)
ndata.none <- normalizeWithinArrays(reducedSet, method="none")
ndata.median <- normalizeWithinArrays(reducedSet, method="median")
ndata.loess <- normalizeWithinArrays(reducedSet, method="loess")
ndata.quantile <- normalizeBetweenArrays(reducedSet, method="quantile")
ndata.all <- list(ndata.none, ndata.median, ndata.loess, 
                  ndata.quantile)
res <- MADvsMedianPlot(ndata.all, channel="R", group="probe.type",                  
                 subset=c("MMU miRNAs", "Other miRNAs", "Control"))
print(res)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(MmPalateMiRNA)
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")'.

Loading required package: xtable
Loading required package: limma

Attaching package: 'limma'

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

    plotMA

Loading required package: statmod
Loading required package: lattice
Loading required package: vsn
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/MmPalateMiRNA/MADvsMedianPlot.Rd_%03d_medium.png", width=480, height=480)
> ### Name: MADvsMedianPlot
> ### Title: Spread vs location of probe intensities
> ### Aliases: MADvsMedianPlot MADvsMedianPlot-methods
> ###   MADvsMedianPlot,list-method
> ### Keywords: methods hplot
> 
> ### ** Examples
> 
> data(PalateData)
> reducedSet <- filterArray(PalateData, keep=c("MIR", "LET", "POSCON", "CALIB"),
+                           frac=1.1, number=3, reps=4)
> ndata.none <- normalizeWithinArrays(reducedSet, method="none")
> ndata.median <- normalizeWithinArrays(reducedSet, method="median")
> ndata.loess <- normalizeWithinArrays(reducedSet, method="loess")
> ndata.quantile <- normalizeBetweenArrays(reducedSet, method="quantile")
> ndata.all <- list(ndata.none, ndata.median, ndata.loess, 
+                   ndata.quantile)
> res <- MADvsMedianPlot(ndata.all, channel="R", group="probe.type",                  
+                  subset=c("MMU miRNAs", "Other miRNAs", "Control"))
> print(res)
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>