Relative M vs. A plots

Description

Create boxplots of M or M vs A plots. Where M is determined relative to a specified chip or to a pseudo-median reference chip.

Arguments

Seealso

mva.pairs

Convert a PLMset to an ExpressionSet

Description

This function converts a PLMset to an ExpressionSet. This is often useful since many Bioconductor functions operate on ExpressionSet objects.

Usage

PLMset2exprSet(pset)
pset2eset(pset)

Arguments

Details

These functions convert PLMset objects to ExpressionSet objects. This is often useful since many Bioconductor functions operate on ExpressionSet objects. Note that the function pset2eset is a wrapper for PLMset2exprSet .

Value

returns a ExpressionSet

Seealso

ExpressionSet

Author

Ben Bolstad bmb@bmbolstad.com

Examples

if (require(affydata)) {
data(Dilution)
Pset <- fitPLM(Dilution)
eset <- pset2eset(Pset)
}

Class PLMset

Description

This is a class representation for Probe level Linear Models fitted to Affymetrix GeneChip probe level data.

Note

This class is better described in the vignette.

Author

B. M. Bolstad bmb@bmbolstad.com

References

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

Read RMAExpress computed expression values

Description

Read RMAExpress computed binary output files into a matrix or ExpressionSet

Usage

ReadRMAExpress(filename, return.value=c("ExpressionSet","matrix"))

Arguments

Value

returns an ExpressionSet

Author

Ben Bolstad bmb@bmbolstad.com

References

http://rmaexpress.bmbolstad.com

Internal affyPLM functions

Description

Internal affyPLM functions

Details

These are not to be called by the user and/or are undergoing testing

LESN - Low End Signal is Noise Background corrections

Description

This function background corrects PM probe data using LESN - Low End Signal is Noise concepts.

Usage

bg.correct.LESN(object, method=2, baseline=0.25, theta=4)

Arguments

Details

This method will be more formally documented at a later date.

The basic concept is to consider that the lowest end of intensites is most likely just noise (and should be heavily corrected) and the highest end signals are most likely signal and should have little adjustment. Low end signals are made much smaller while high end signals get less adjustment relative adjustment.

Value

An AffyBatch

Author

Ben Bolstad bmb@bmbolstad.com

References

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

Examples

if (require(affydata)) {
data(Dilution)
Dilution.example.bgcorrect <- bg.correct.LESN(Dilution)
}

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),
       verbosity.level=0)

Arguments

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

Seealso

expresso , rma , threestep

Author

Ben Bolstad bmb@bmbolstad.com

References

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

Examples

if (require(affydata)) {
data(Dilution)
Pset <- fitPLM(Dilution, model=PM ~ -1 + probes + samples)
se(Pset)[1:5,]

image(Pset)
NUSE(Pset)

#now lets try a wider class of models
Pset <- fitPLM(Dilution,model=PM ~ -1 + probes +liver,
normalize=FALSE,background=FALSE)
coefs(Pset)[1:10,]

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))
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
Pset <- fitPLM(Dilution, model=PM~-1+probes+scanner,
normalize=FALSE,background=FALSE,m odel.param=list(se.type=2))
se(Pset)[1:10,]
}

Normalization applied to ExpressionSets

Description

Allows the user to apply normalization routines to ExpressionSets.

Usage

normalize.ExpressionSet.quantiles(eset, transfn=c("none","log","antilog"))
  normalize.ExpressionSet.loess(eset, transfn=c("none","log","antilog"),...)
  normalize.ExpressionSet.contrasts(eset, span = 2/3,
      choose.subset=TRUE, subset.size=5000, verbose=TRUE, family="symmetric",
      transfn=c("none","log","antilog")) 
  normalize.ExpressionSet.qspline(eset, transfn=c("none","log","antilog"),...)
  normalize.ExpressionSet.invariantset(eset,prd.td=c(0.003, 0.007),
      verbose=FALSE, transfn=c("none","log","antilog"),
      baseline.type=c("mean","median","pseudo-mean","pseudo-median")) 
  normalize.ExpressionSet.scaling(eset, trim=0.02, baseline=-1,
      transfn=c("none","log","antilog"))

Arguments

Details

This function carries out normalization of expression values. In general you should either normalize at the probe level or at the expression value level, not both.

Typing normalize.ExpressionSet.methods should give you a list of methods that you may use. note that you can also use the normalize function on ExpressionSets. Use method to select the normalization method.

Value

A normalized ExpressionSet .

Author

Ben Bolstad, bmb@bmbolstad.com

References

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

Examples

if (require(affydata)) {
data(Dilution)
eset <- rma(Dilution, normalize=FALSE, background=FALSE)
normalize(eset)
}

Quantile Normalization applied to probesets

Description

Using a normalization based upon quantiles, this function normalizes a matrix of probe level intensities.

Usage

normalize.AffyBatch.quantiles.probeset(abatch,type=c("separate","pmonly","mmonly","together"),use.median=FALSE,use.log=TRUE)

Arguments

Details

This function applies the quantile method in a probeset specific manner.

In particular a probeset summary is normalized using the quantile method and then the probes adjusted accordingly.

Value

A normalized AffyBatch .

Seealso

normalize.quantiles

Author

Ben Bolstad, bmb@bmbolstad.com

References

Bolstad, B (2001) list("Probe Level Quantile Normalization of High Density ", " Oligonucleotide Array Data") . Unpublished manuscript http://oz.berkeley.edu/~bolstad/stuff/qnorm.pdf

Bolstad, B. M., Irizarry R. A., Astrand, M, and Speed, T. P. (2003) list("A Comparison of Normalization Methods for High Density ", " Oligonucleotide Array Data Based on Bias and Variance.") Bioinformatics 19(2) ,pp 185-193. http://www.stat.berkeley.edu/~bolstad/normalize/normalize.html

Scaling normalization

Description

Allows the user to apply scaling normalization.

Usage

normalize.scaling(X,trim=0.02, baseline=-1, log.scalefactors=FALSE)
normalize.AffyBatch.scaling(abatch,
    type=c("together","pmonly","mmonly","separate"),
    trim=0.02, baseline=-1, log.scalefactors=FALSE)

Arguments

Details

These function carries out scaling normalization of expression values.

Value

A normalized ExpressionSet .

Author

Ben Bolstad, bmb@bmbolstad.com

Examples

if (require(affydata)) {
data(Dilution)
normalize.AffyBatch.scaling(Dilution)
}

Background correct and Normalize

Description

This function pre-processes an AffyBatch .

Usage

preprocess(object, subset=NULL, normalize=TRUE, background=TRUE,
           background.method="RMA.2", normalize.method="quantile",
           background.param=list(), normalize.param=list(),
           verbosity.level=0)

Arguments

Details

This function carries out background correction and normalization pre-processing steps. It does not summarize to produce gene expression measures. All the same pre-processing methods supplied by threestep are supported by this function.

Value

An AffyBatch

Seealso

expresso , rma

Author

Ben Bolstad bmb@bmbolstad.com

References

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

Examples

if (require(affydata)) {
data(Dilution)

# should be equivalent to the bg and norm of rma()
abatch.preprocessed <- preprocess(Dilution)
}

Coloring pseudo chip images

Description

These are routines used for coloring pseudo chip images.

Usage

pseudoPalette(low = "white", high = c("green", "red"), mid = NULL,k =50) 
  pseudoColorBar(x, horizontal = TRUE, col = heat.colors(50), scale = 1:length(x),k = 11, log.ticks=FALSE,...)

Arguments

Details

Adapted from similar tools in maPlots pacakge.

Seealso

AffyBatch , read.affybatch

Author

Ben Bolstad bmb@bmbolstad.com

Fit a RMA to Affymetrix Genechip Data as a PLMset

Description

This function converts an AffyBatch into an PLMset by fitting a multichip model. In particular we concentrate on the RMA model.

Usage

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(), verbosity.level=0)

Arguments

Details

This function fits the RMA as a Probe Level Linear models to all the probesets in an AffyBatch .

Value

An PLMset

Seealso

expresso , rma , threestep , fitPLM , threestepPLM

Author

Ben Bolstad bmb@bmbolstad.com

References

Bolstad, BM (2004) list("Low Level Analysis of High-density ", " Oligonucleotide Array Data: Background, Normalization and ", " Summarization") . PhD Dissertation. University of California, list() list() Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B and Speed TP (2003) list("Summaries of Affymetrix GeneChip probe level data") Nucleic Acids Research 31(4):e15 list() list() Bolstad, BM, Irizarry RA, Astrand, M, and Speed, TP (2003) list("A Comparison of Normalization Methods for High Density ", " Oligonucleotide Array Data Based on Bias and Variance.") Bioinformatics 19(2):185-193

Examples

if (require(affydata)) {
# A larger example testing weight image function
data(Dilution)
Pset <- rmaPLM(Dilution,output.param=list(weights=TRUE))
image(Pset)
}

Three Step expression measures

Description

This function converts an AffyBatch into an ExpressionSet using a three step expression measure.

Usage

threestep(object, subset=NULL, normalize=TRUE, background=TRUE,
          background.method="RMA.2", normalize.method="quantile",
          summary.method="median.polish", background.param=list(),
          normalize.param=list(), summary.param=list(), verbosity.level=0)

Arguments

Details

This function computes the expression measure using threestep methods. Greater details can be found in a vignette.

Value

An ExpressionSet

Seealso

expresso , rma

Author

Ben Bolstad bmb@bmbolstad.com

References

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

Examples

if (require(affydata)) {
data(Dilution)

# should be equivalent to rma()
eset <- threestep(Dilution)

# Using Tukey Biweight summarization
eset <- threestep(Dilution, summary.method="tukey.biweight")

# Using Average Log2 summarization
eset <- threestep(Dilution, summary.method="average.log")

# Using IdealMismatch background and Tukey Biweight and no normalization.
eset <- threestep(Dilution, normalize=FALSE,background.method="IdealMM",
summary.method="tukey.biweight")

# Using average.log summarization and no background or normalization.
eset <- threestep(Dilution, background=FALSE, normalize=FALSE,
background.method="IdealMM",summary.method="tukey.biweight")

# Use threestep methodology with the rlm model fit
eset <- threestep(Dilution, summary.method="rlm")

# Use threestep methodology with the log of the average
eset <- threestep(Dilution, summary.method="log.average")

# Use threestep methodology with log 2nd largest method
eset <- threestep(Dilution, summary.method="log.2nd.largest")

eset <- threestep(Dilution, background.method="LESN2")
}

Three Step expression measures returned as a PLMset

Description

This function converts an AffyBatch into an PLMset using a three step expression measure.

Usage

threestepPLM(object,subset=NULL, normalize=TRUE, background=TRUE,
             background.method="RMA.2", normalize.method="quantile",
             summary.method="median.polish", background.param = list(),
             normalize.param=list(), output.param=list(),
             model.param=list(), verbosity.level=0)

Arguments

Details

This function computes the expression measure using threestep methods. It returns a PLMset . The most important difference is that the PLMset allows you to access the residuals which the threestep function does not do.

Value

An PLMset

Seealso

expresso , rma , threestep , rmaPLM , fitPLM

Author

Ben Bolstad bmb@bmbolstad.com

References

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

Examples

if (require(affydata)) {
data(Dilution)

# should be equivalent to rma()
eset <- threestepPLM(Dilution)
}