compute.RA: LVS function for the residual standard errors and the array...
In Senbee/FLUSH_LVS_bundle: FLUSH.LVS.bundle

Description Usage Value Author(s) References See Also Examples

Given an AffyBatch object fits a robust linear model at probe level and return the residual standard errors and the array effects.

1	compute.RA(abatch,bg.RA=c("nome","imm","rma"))

an object of class RA with a 'data' slot containing a two columns data.frame with IDs as rows and RSE and Array effects as columns.

Stefano Calza <calza@med.unibs.it>, Davide Valentini and Yudi Pawitan.

S. Calza et al. 'Normalization of oligonucleotide arrays based on the least variant set of genes' (2008, BMCBioinformatics).

lvs, normalize.lvs, lvs.fit, normalize.AffyBatch.lvs fitRA

## Not run: 
# Starting from an Affibatch object called aBatch 
data.RA <- compute.RA(aBatch)
lvs.id <- lvs.fit(data.RA, proportion=0.6)
lvs.prep <- expresso(aBatch, normalize=FALSE, bgcorrect.method="mas",pmcorrect.method="mas",summary.method="mas")
normalize.AffyBatch.lvs(lvs.prep,lvs.id=lvs.id)
 
## End(Not run)