gcrma {gcrma} | R Documentation |
This function converts an AffyBatch
into an exprSet
using the robust multi-array average (RMA) expression measure with help of probe sequence.
gcrma(object,affinity.info=NULL, affinity.source=c("reference","local"),NCprobe=NULL, type=c("fullmodel","affinities","mm","constant"), k=6*fast+0.5*(1-fast),stretch=1.15*fast+1*(1-fast),correction=1, GSB.adjust=TRUE, rho=.7,optical.correct=TRUE,verbose=TRUE,fast=TRUE, subset=NULL,normalize=TRUE,...)
object |
an AffyBatch |
affinity.info |
NULL or an AffyBatch containing the
affinities in the exprs slot. This object can be created
using the function compute.affinities . |
affinity.source |
reference : use the package internal
Non-specific binding data or local : use the experimental
data in object . If local is chosen, either MM probes or a user-defined
list of probes (see NCprobes ) are used to estimatee
affinities. |
NCprobe |
Index of negative control probes. When set as
NULL ,the MM probes will be used. These probes
are used to estimate parameters of non-specific binding on each
array. These will be also used to estimate probe affinity profiles when
affinity.info is not provided. |
type |
"fullmodel" for sequence and MM model. "affinities" for sequence information only. "mm" for using MM without sequence information. |
k |
A tuning factor. |
stretch |
|
correction |
. |
GSB.adjust |
Logical value. If TRUE , probe effects in specific binding will
be adjusted. |
rho |
correlation coefficient of log background intensity in a pair of pm/mm probes. Default=.7 |
optical.correct |
Logical value. If TRUE , optical
background correction is performed. |
verbose |
Logical value. If TRUE messages about the progress of
the function is printed. |
fast |
Logical value. If TRUE a faster ad hoc algorithm is
used. |
subset |
a character vector with the the names of the probesets to be used in expression calculation. |
normalize |
logical value. If 'TRUE' normalize data using quantile normalization. |
... |
further arguments to be passed (not currently implemented - stub for future use) |
Note that this expression measure is given to you in log base 2 scale. This differs from most of the other expression measure methods.
The tunning factor k
will have different meainngs if one uses
the fast (add-hoc) algorithm or the empirical bayes approach. See Wu
et al. (2003)
An exprSet
.
Rafeal Irizarry
if(require(affydata) & require(hgu95av2probe) & require(hgu95av2cdf)){ data(Dilution) ai <- compute.affinities(cdfName(Dilution)) Dil.expr<-gcrma(Dilution,affinity.info=ai,type="affinities") }