| preprocess {Ringo} | R Documentation |
Calls one of various limma functions to transform raw probe
intensities into (background-corrected) normalized log ratios
(M-values).
preprocess(myRG, method = "vsn", returnMAList=FALSE,
idColumn="PROBE_ID", verbose=TRUE, ...)
myRG |
object of class RGList |
method |
string; denoting which normalization method to choose, see below for details |
returnMAList |
logical; should an MAList object be returned? Default is to return an ExpressionSet object. |
idColumn |
string; indicating which column of the genes
data.frame of the RGList holds the identifier for reporters on the
microarray. This column, after calling
make.names on it, will make up the unique
featureNames of the resulting ExpressionSet.
If argument returnMAList is TRUE, this argument is
ignored. |
verbose |
logical; progress output to STDOUT? |
... |
further arguments to be passed on
normalizeWithinArrays and normalizeBetweenArrays |
The procedure and called limma functions depend on the choice of
method.
limma's function backgroundCorrect with
method="normexp" and offset=50. Then calls
normalizeWithinArrays.normalizeBetweenArrays with method="vsn".normalizeBetweenArrays with method="Gquantile".normalizeBetweenArrays with method="Rquantile".limma's function backgroundCorrect with
method="normexp" and offset=50. Then calls
normalizeWithinArrays with method="median".log2(R)-log2(G) as component M
and (log2(R)+log2(G))/2 as component A;
uses normalizeWithinArrays with method="none".
Returns normalized, transformed values as an object of class
ExpressionList or MAList.
Joern Toedling toedling@ebi.ac.uk
backgroundCorrect,
normalizeWithinArrays,
normalizeBetweenArrays,
malist,ExpressionSet,
tukey.biweight
exDir <- system.file("exData",package="Ringo")
exRG <- readNimblegen("example_targets.txt","spottypes.txt",path=exDir)
exampleX <- preprocess(exRG)
sampleNames(exampleX) <-
make.names(paste(exRG$targets$Cy5,"vs",exRG$targets$Cy3,sep="_"))
print(exampleX)
### compare VSN to NimbleGen's tukey-biweight scaling
exampleX.NG <- preprocess(exRG, method="nimblegen")
sampleNames(exampleX.NG) <- sampleNames(exampleX)
if (interactive())
corPlot(cbind(exprs(exampleX),exprs(exampleX.NG)),
grouping=c("VSN normalized","Tukey-biweight scaled"))