RGData: The RGData class
In HenrikBengtsson/aroma: An R Object-oriented Microarray Analysis package

Description Usage Arguments Details Fields and Methods Note Author(s) Examples

Package: aroma
Class RGData

Object
~~|
~~+--MicroarrayData
~~~~~~~|
~~~~~~~+--RGData

Directly known subclasses:

public static class RGData
extends MicroarrayData

Creates a new RGData object. The philosophy behind this data structure is to think about the data in the form of the signals in one channel (R) versus the signals in the other channel (G). This is in contrast to the idea of the MAData structure, which thinks about the data as the log ratios (M) and log intensites (A) for the spot signals.

1	RGData(R=NULL, G=NULL, layout=NULL, extras=list())

`R,G`	A NxM `matrix` containing (non-logged) signals of the red (green) channel, where N is the number of spots on each slide and M is the number of slides in this data set.
`layout`	A `Layout` object specifying the spot layout of the slides in this data set.
`extras`	Private argument. Do not use.

The mapping between M and A, and R and G is a one-to-one function. Given the signal R and G for the R and the G channels you get the M and the A values by:

M = log2(R/G), A = log2(sqrt(R*G)) = 1/2*log2(R*G),

which in [R] can be done by ma <- as.MAData(rg). The reverse function, i.e. going back to the R and the G is:

R = sqrt(2^(2A+M)), G = sqrt(2^(2A-M))

which in [R] can be done by rg <- as.RGData(rg).

Note that if the signal in one or both channels is non-positive, the log-transform will make these values undefined, that is, set them to NA. When going back to (G,R) from (A,M) these values will remain NA.

Fields

	`R`	The signal for channel R (non-logged).
	`G`	The signal for channel G (non-logged).

Methods:

	`as.character`	-
	`as.MAData`	Transform from the red and green intensities into log ratios between them and the log product of them.
	`as.RawData`	-
	`as`	-
	`as.RGData`	-
	`boxplot`	-
	`calibrateMultiscan`	Calibrates multiple re-scanned images based on an affine model.
	`getCalibratedMultiscan`	-
	`getChannelNames`	-
	`getColors`	Generates red to green colors for each of the specified spots.
	`getLogIntensities`	Calculates the log-intensitites (A values).
	`getLogRatios`	Calculates the log-ratios (M values).
	`getWithinChannelPairs`	-
	`mean`	Genewise Average Mean for channel R and G.
	`normalizeAffine`	Weighted affine normalization between channels and arrays.
	`normalizeCurveFit`	Within-slide intensity-dependent normalization in (A,M).
	`normalizeGenewise`	-
	`normalizeLoess`	-
	`normalizeLowess`	-
	`normalizeQuantile`	-
	`normalizeRobustSpline`	-
	`normalizeSpline`	-
	`plot`	-
	`plotSpatial`	-
	`plotXY`	-
	`range`	-
	`shift`	Shift the log-ratios, log-intensities or the raw signal.
	`swapDyes`	Swap dyes of one or many slides.
	`var`	Genewise Variance for channel R and G.

Methods inherited from MicroarrayData:
addFlag, append, applyGenewise, applyGroupwise, applyPlatewise, applyPrintdipwise, applyPrinttipwise, as.character, as.data.frame, boxplot, clearCache, clearFlag, createColors, dataFrameToList, equals, extract, getBlank, getCache, getChannelNames, getColors, getExcludedSpots, getExtra, getExtreme, getFieldNames, getFlag, getInclude, getLabel, getLayout, getProbeWeights, getSignalWeights, getSlideNames, getSlidePairs, getSpotPosition, getSpotValue, getTreatments, getView, getWeights, getWeightsAsString, hasExcludedSpots, hasLayout, hasProbeWeights, hasSignalWeights, hasWeights, highlight, hist, isFieldColorable, keepSlides, keepSpots, listFlags, lowessCurve, nbrOfDataPoints, nbrOfFields, nbrOfSlides, nbrOfSpots, nbrOfTreatments, normalizePlatewise, normalizePrintorder, normalizeQuantile, plot, plotDensity, plotGene, plotPrintorder, plotReplicates, plotSpatial, plotSpatial3d, plotXY, points, putGene, putSlide, qqnorm, quantile, range, range2, read, readHeader, readToList, removeSlides, removeSpots, resetProbeWeights, resetSignalWeights, select, seq, setCache, setExcludedSpots, setExtra, setFlag, setLabel, setLayout, setProbeWeights, setSignalWeights, setSlideNames, setTreatments, setView, setWeights, size, str, subplots, summary, text, updateHeader, validateArgumentChannel, validateArgumentChannels, validateArgumentGroupBy, validateArgumentSlide, validateArgumentSlides, validateArgumentSpotIndex, validateArgumentWeights, write, writeHeader

Methods inherited from Object:
$, $<-, [[, [[<-, as.character, attach, attachLocally, clearCache, clone, detach, equals, extend, finalize, gc, getEnvironment, getFields, getInstanciationTime, getStaticInstance, hasField, hashCode, ll, load, objectSize, print, save

There are several functions that returns an object of this class, and it is only in very special cases that you actually have to create one yourself.

Henrik Bengtsson (http://www.braju.com/R/)

  # Create a raw data object from the preexisting example data in
  # the sma package.
  SMA$loadData("mouse.data")
  layout <- Layout$read("MouseArray.Layout.dat", path=system.file("data-ex", package="aroma"))
  raw <- RawData(mouse.data, layout=layout)

  # Get the signal (here by default non-background corrected)
  ma <- getSignal(raw)

  # Transform (M,A) into (R,G)
  rg <- as.RGData(ma)