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"))