gcrma: Robust Multi-Array expression measure using sequence...
In gcrma: Background Adjustment Using Sequence Information

Description Usage Arguments Details Value Author(s) Examples

This function converts an AffyBatch into an ExpressionSet 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 estimate 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 tuning factor k will have different meanings if one uses the fast (add-hoc) algorithm or the empirical Bayes approach. See Wu et al. (2003)

An ExpressionSet.

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")
}