rmaPLM {affyPLM} | R Documentation |
This function converts an AffyBatch
into an
PLMset
by fitting a multichip model. In particular we
concentrate on the RMA model.
rmaPLM(object,subset=NULL,normalize=TRUE,background=TRUE,background.method="RMA.2",normalize.method="quantile",background.param = list(),normalize.param=list(),output.param=list(),model.param=list())
object |
an AffyBatch |
subset |
a vector with the names of probesets to be used. If NULL then all probesets are used. |
normalize |
logical value. If TRUE normalize data using
quantile normalization |
background |
logical value. If TRUE background correct
using RMA background correction |
background.method |
name of background method to use. |
normalize.method |
name of normalization method to use. |
background.param |
A list of parameters for background routines |
normalize.param |
A list of parameters for normalization routines |
output.param |
A list of parameters controlling optional output from the routine. |
model.param |
A list of parameters controlling model procedure |
This function fits the RMA as a Probe Level Linear models to all the probesets in
an AffyBatch
.
An PLMset
Ben Bolstad bolstad@stat.berkeley.edu
Bolstad, BM (2004) Low Level Analysis of High-density
Oligonucleotide Array Data: Background, Normalization and
Summarization. PhD Dissertation. University of California,
Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B and Speed
TP (2003) Summaries of Affymetrix GeneChip probe level data
Nucleic Acids Research 31(4):e15
Bolstad, BM, Irizarry RA, Astrand, M, and Speed, TP (2003)
A Comparison of Normalization Methods for High Density
Oligonucleotide Array Data Based on Bias and Variance.
Bioinformatics 19(2):185-193
expresso
,
rma
, threestep
,fitPLM
, threestepPLM
# A larger example testing weight image function data(Dilution) ## Not run: Pset <- rmaPLM(Dilution,output.param=list(weights=TRUE)) ## Not run: image(Pset)