normalize.exprSet {affyPLM} | R Documentation |
Allows the user to apply normalization routines to exprSets.
normalize.exprSet.quantiles(eset,transfn=c("none","log","antilog")) normalize.exprSet.loess(eset,transfn=c("none","log","antilog"),...) normalize.exprSet.contrasts(eset, span = 2/3, choose.subset = TRUE, subset.size = 5000, verbose = TRUE, family = "symmetric",transfn=c("none","log","antilog")) normalize.exprSet.qspline(eset,transfn=c("none","log","antilog"),...) normalize.exprSet.invariantset(eset,prd.td = c(0.003, 0.007),verbose=FALSE,transfn=c("none","log","antilog"),baseline.type=c("mean","median","pseudo-mean","pseudo-median")) normalize.exprSet.scaling(eset,trim=0.02,baseline=-1,transfn=c("none","log","antilog"))
eset |
An exprSet |
span |
parameter to be passed to the function loess . |
choose.subset |
|
subset.size |
|
verbose |
verbosity flag |
family |
parameter to be passed to the function
loess . |
prd.td |
cutoff parameter (details in the bibliographic reference) |
trim |
How much to trim from the top and bottom before computing the mean when using the scaling normalization |
baseline |
Index of array to use as baseline, negative values (-1,-2,-3,-4) control different baseline selection methods |
transfn |
Transform the exprSet before normalizing. Useful when dealing with expression values that are log-scale |
baseline.type |
A method of selecting the baseline array |
... |
Additional parameters that may be passed to the normalization routine |
This function carries out normalization of expression values. In general you should either normalize at the probe level or at the expression value level, not both.
Typing normalize.exprSet.methods
should give you a list of
methods that you may use. note that you can also use the
normalize
function on exprSets. Use method
to select the
normalization method.
A normalized exprSet
.
Ben Bolstad, bolstad@stat.berkeley.edu
data(affybatch.example) eset <- rma(affybatch.example,normalize=FALSE,background=FALSE) normalize(eset)