fitPLM {affyPLM}R Documentation

Fit a Probe Level Model to Affymetrix Genechip Data.

Description

This function converts an AffyBatch into an PLMset by fitting a specified robust linear model to the probe level data

Usage

fitPLM(object,model=PM ~ -1 + probes +samples,
variable.type=c(default="factor"),
constraint.type=c(default="contr.treatment"),
subset=NULL,
background=TRUE, normalize=TRUE, background.method = "RMA.2",normalize.method = "quantile",background.param=list(),normalize.param=list(),output.param =
                 verify.output.param(), model.param =
                 verify.model.param(object, model))

Arguments

object an AffyBatch
model A formula describing the model to fit. This is slightly different from the standard method of specifying formulae in R. Read the description below
variable.type a way to specify whether variables in the model are factors or standard variables
constraint.type should factor variables sum to zero or have first variable set to zero (endpoint constraint)
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

Details

This function fits robust Probe Level linear Models to all the probesets in an AffyBatch. This is carried out on a probeset by probeset basis. The user has quite a lot of control over which model is used and what outputs are stored. For more details please read the vignette.

Value

An PLMset

Author(s)

Ben Bolstad bolstad@stat.berkeley.edu

References

Bolstad, BM (2004) Low Level Analysis of High-density Oligonucleotide Array Data: Background, Normalization and Summarization. PhD Dissertation. University of California, Berkeley.

See Also

expresso, rma, threestep

Examples

data(affybatch.example)
Pset <- fitPLM(affybatch.example,model=PM ~ -1 + probes + samples)
se(Pset)[1:5,]

# A larger example testing weight image function
data(Dilution)
## Not run: Pset <- fitPLM(Dilution,model=PM ~ -1 + probes + samples)
## Not run: image(Pset)
## Not run: NUSE(Pset) # NUSE

#now lets try a wider class of models
## Not run: Pset <- fitPLM(Dilution,model=PM ~ -1 + probes +liver,normalize=FALSE,background=FALSE)
## Not run: coefs(Pset)[1:10,]

## Not run: Pset <- fitPLM(Dilution,model=PM ~ -1 + probes + liver + scanner,normalize=FALSE,background=FALSE)
coefs(Pset)[1:10,]

#try liver as a covariate
logliver <- log2(c(20,20,10,10))
## Not run: Pset <- fitPLM(Dilution,model=PM~-1+probes+logliver+scanner,normalize=FALSE,background=FALSE,variable.type=c(logliver="covariate"))
coefs(Pset)[1:10,]

#try a different se.type
## Not run: Pset <- fitPLM(Dilution,model=PM~-1+probes+scanner,normalize=FALSE,background=FALSE,model.param=list(se.type=2))
se(Pset)[1:10,]


[Package affyPLM version 1.6.0 Index]