Class AffyBatch

Description

This is a class representation for Affymetrix GeneChip probe level data. The main component are the intensities from multiple arrays of the same CDF type. It extends eSet .

Seealso

related methods merge.AffyBatch , pairs.AffyBatch , and eSet

Note

This class is better described in the vignette.

Examples

if (require(affydata)) {
## load example
data(Dilution)

## nice print
print(Dilution)

pm(Dilution)[1:5,]
mm(Dilution)[1:5,]

## get indexes for the PM probes for the affyID "1900_at"
mypmindex <- pmindex(Dilution,"1900_at")
## same operation using the primitive
mypmindex <- indexProbes(Dilution, which="pm", genenames="1900_at")[[1]]
## get the probe intensities from the index
intensity(Dilution)[mypmindex, ]

description(Dilution) ##we can also use the methods of eSet
sampleNames(Dilution)
abstract(Dilution)
}

Function to assess RNA degradation in Affymetrix GeneChip data.

Description

Uses ordered probes in probeset to detect possible RNA degradation. Plots and statistics used for evaluation.

Usage

AffyRNAdeg(abatch,log.it=TRUE)
summaryAffyRNAdeg(rna.deg.obj,signif.digits=3)
plotAffyRNAdeg(rna.deg.obj, transform = "shift.scale", cols = NULL, ...)

Arguments

Details

Within each probeset, probes are numbered directionally from the 5' end to the 3' end. Probe intensities are averaged by probe number, across all genes. If log.it= FALSE and transform="Neither", then plotAffyRNAdeg simply shows these means for each chip. Shifted and scaled versions of the plot can make it easier to see.

Value

AffyRNAdeg returns a list with the following components:

*

Author

Leslie Cope

Examples

if (require(affydata)) {
data(Dilution)
RNAdeg<-AffyRNAdeg(Dilution)
plotAffyRNAdeg(RNAdeg)
}

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.

Usage

MAplot(object,...)
Mbox(object,...)
ma.plot(A, M, subset = sample(1:length(M), min(c(10000, length(M)))),
        show.statistics = TRUE, span = 2/3, family.loess = "gaussian",
        cex = 2, plot.method = c("normal","smoothScatter","add"),
        add.loess = TRUE, lwd = 1, lty = 1, loess.col = "red", ...)

Arguments

Seealso

mva.pairs

Examples

if (require(affydata)) {
data(Dilution)
MAplot(Dilution)
Mbox(Dilution)
}

Class ProbeSet

Description

A simple class that contains the PM and MM data for a probe set from one or more samples.

Seealso

probeset , AffyBatch-class

Note

More details are contained in the vignette.

Examples

if (require(affydata)) {
data(Dilution)
ps <- probeset(Dilution, geneNames(Dilution)[1:2])
names(ps)
print(ps[[1]])
}

Class "ProgressBarText"

Description

A class to handle progress bars in text mode.

Author

Laurent

Examples

f <- function(x, header = TRUE) {
pbt <- new("ProgressBarText", length(x), barsteps = as.integer(20))

open(pbt, header = header)

for (i in x) {
Sys.sleep(i)
updateMe(pbt)
}
close(pbt)
}

## if too fast on your machine, change the number
x <- runif(15)

f(x)
f(x, header = FALSE)

## 'cost' of the progress bar:
g <- function(x) {
z <- 1
for (i in 1:x) {
z <- z + 1
}
}
h <- function(x) {
pbt <- new("ProgressBarText", as.integer(x), barsteps = as.integer(20))
open(pbt)
for (i in 1:x) {
updateMe(pbt)
}
close(pbt)
}

system.time(g(10000))
system.time(h(10000))

SpikeIn Experiment Data: ProbeSet Example

Description

This ProbeSet represents part of SpikeIn experiment data set.

Format

SpikeIn is ProbeSet containing the $PM$ and $MM$ intensities for a gene spiked in at different concentrations (given in the vector colnames(pm(SpikeIn)) ) in 12 different arrays.

Usage

data(SpikeIn)

Deprecated functions in package affy

Description

These functions are provided for compatibility with older versions of affy only, and will be defunct at the next release.

Details

The following functions are deprecated and will be made defunct; use the replacement indicated below:

• list("loess.normalize: ", list(list("normalize.loess")))

• list("maffy.normalize")

• list("multiloess")

• list("simplemultiLoess")

Options for the affy package

Description

Description of the options for the affy package.

Note

The affy package options are contained in the Bioconductor options. The options are:

• use.widgets : a logical used to decide on the default of widget use.

• compress.cel : a logical

• compress.cdf : a logical

• probes.loc : a list. Each element of the list is it self a list with two elements what and where . When looking for the informations about the locations of the probes on the array, the elements in the list will be looked at one after the other. The first one for which what and where lead to the matching locations information is used. The element what can be one of package , environment or file . The element where depends on the corresponding element what .

• if package : location for the package (like it would be for the argument lib.loc for the function library .)

• if environment : an environment to look for the information (like the argument env for the function get ).

• if file : a character with the path in which a CDF file can be found.

Examples

## get the options
opt <- getOption("BioC")
affy.opt <- opt$affy

## list their names
names(affy.opt)

## set the option compress.cel
affy.opt$compress.cel <- TRUE
options(BioC=opt)

Scale normalization for expreSets

Description

Normalizes expression values using the method described in the Affymetrix user manual.

Usage

affy.scalevalue.exprSet(eset, sc = 500, analysis="absolute")

Arguments

Details

This is function was implemented from the Affymetrix technical documentation for MAS 5.0. It can be downloaded from the website of the company. Please refer to this document for details.

Value

A normalized ExpressionSet .

Author

Laurent

show a ProbeSet as barplots

Description

Displays the probe intensities in a ProbeSet as a barplots

Usage

list(list("barplot"), list("ProbeSet"))(height, xlab = "Probe pair", ylab = "Intensity",
    main = NA, col.pm = "red", col.mm = "blue", beside = TRUE, names.arg = "pp",
    ask = TRUE, scale, ...)

Arguments

Examples

if (require(affydata)) {
data(Dilution)
gn <- geneNames(Dilution)
pps <- probeset(Dilution, gn[1])[[1]]

barplot.ProbeSet(pps)
}

Background adjustment (internal function)

Description

An internal function to be used by bg.correct.rma .

Usage

bg.adjust(pm, n.pts = 2^14, ...)
bg.parameters(pm, n.pts = 2^14)

Arguments

Details

Assumes PMs are a convolution of normal and exponential. So we observe X+Y where X is background and Y is signal. bg.adjust | returns E[Y|X+Y, Y>0] as our background corrected PM.| bg.parameters provides ad hoc estimates of the parameters of the normal and exponential distributions.

Value

a matrix

Seealso

bg.correct.rma

Background Correction

Description

Background corrects probe intensities in an object of class AffyBatch .

Usage

bg.correct(object, method, ...)
bg.correct.rma(object,...)
bg.correct.mas(object, griddim)
bg.correct.none(object, ...)

Arguments

Details

The name of the method to apply must be double-quoted. Methods provided with the package are currently:

• bg.correct.none: returns object unchanged.

• bg.correct.chipwide: noise correction as described in a white paper' from Affymetrix. * bg.correct.rma: the model based correction used by the RMA expression measure. They are listed in the variablebg.correct.methods. The user must supply the word after "bg.correct", i.e none, subtractmm, rma, etc... More details are available in the vignette. R implementations similar in function to the internal implementation used bybg.correct.rmaare in [bg.adjust](#bg.adjust) . ## Value An [AffyBatch`](#affybatch) for which the intensities have been background adjusted. For some methods (RMA), only PMs are corrected and the MMs remain the same. ## Examples r if (require(affydata)) { data(Dilution) ##bgc will be the bg corrected version of Dilution bgc <- bg.correct(Dilution, method="rma") ##This plot shows the tranformation plot(pm(Dilution)[,1],pm(bgc)[,1],log="xy", main="PMs before and after background correction") }

Functions to obtain CDF files

Description

A set of functions to obtain CDF files from various locations.

Usage

cdfFromBioC(cdfname, lib = .libPaths()[1], verbose = TRUE)
cdfFromLibPath(cdfname, lib = NULL, verbose=TRUE)
cdfFromEnvironment(cdfname, where, verbose=TRUE)

Arguments

Details

These functions all take a requested CDF environment name and will attempt to locate that environment in the appropriate location (a package's data directory, as a CDF package in the .libPaths(), from a loaded environment or on the Bioconductor website. If the environment can not be found, it will return a list of the methods tried that failed.

Value

The CDF environment or a list detailing the failed locations.

Author

Jeff Gentry

Example cdfenv

Description

Example cdfenv (environment containing the probe locations).

Format

An environment cdfenv.example containing the probe locations

Usage

data(cdfenv.example)

Clean Affymetrix's CDF name

Description

This function converts Affymetrix's names for CDF files to the names used in the annotation package and in all Bioconductor.

Usage

cleancdfname(cdfname, addcdf = TRUE)

Arguments

Details

This function takes a CDF filename obtained from an Affymetrix file (from a CEL file for example) and convert it to a convention of ours: all small caps and only alphanumeric characters. The details of the rule can be seen in the code. We observed exceptions that made us create a set of special cases for mapping CEL to CDF. The object mapCdfName holds information about these cases. It is a data.frame of three elements: the first is the name as found in the CDF file, the second the name in the CEL file and the third the name in Bioconductor. mapCdfName can be loaded using data(mapCdfName) .

Value

A character

Examples

cdf.tags <- c("HG_U95Av2", "HG-133A")
for (i in cdf.tags)
cat(i, "becomes", cleancdfname(i), "
")

Debugging Flag

Description

For developmental use only

From raw probe intensities to expression values

Description

Goes from raw probe intensities to expression values

Usage

expresso(
        afbatch,
    # background correction
        bg.correct = TRUE,
        bgcorrect.method = NULL,
        bgcorrect.param = list(),
    # normalize
        normalize = TRUE,
        normalize.method = NULL,
        normalize.param = list(),
    # pm correction
        pmcorrect.method = NULL,
        pmcorrect.param = list(),
    # expression values
        summary.method = NULL,
        summary.param = list(),
        summary.subset = NULL,
    # misc.
        verbose = TRUE,
%        warnings = TRUE,        widget = FALSE)

Arguments

Details

Some arguments can be left to NULL if the widget=TRUE . In this case, a widget pops up and let the user choose with the mouse. The arguments are: AffyBatch , bgcorrect.method , normalize.method , pmcorrect.method and summary.method .

For the mas 5.0 and 4.0 methods ones need to normalize after obtaining expression. The function affy.scalevalue.exprSet does this.

For the Li and Wong summary method notice you will not get the same results as you would get with dChip. dChip is not open source so it is not easy to reproduce. Notice also that this iterative algorithm will not always converge. If you run the algorithm on thousands of probes expect some non-convergence warnings. These are more likely when few arrays are used. We recommend using this method only if you have 10 or more arrays. Please refer to the fit.li.wong help page for more details.

Value

An object of class ExpressionSet , with an attribute pps.warnings as returned by the method computeExprSet .

Seealso

AffyBatch

Examples

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

eset <- expresso(Dilution, bgcorrect.method="rma",
normalize.method="constant",pmcorrect.method="pmonly",
summary.method="avgdiff")

##to see options available for bg correction type:
bgcorrect.methods()
}

A widget for users to pick correction methods

Description

This widget is called by expresso to allow users to select correction methods that will be used to process affy data.

Usage

expressoWidget(BGMethods, normMethods, PMMethods, expMethods, BGDefault,
normDefault, PMDefault, expDefault)

Arguments

Details

The widget will be invoked when expresso is called with argument "widget" set to TRUE. Default values can be changed using the drop down list boxes. Double clicking on an option from the drop-down list makes an selection. The first element of the list for available methods will be the default method if no default is provided.

Value

The widget returns a list of selected correction methods.

*

Seealso

expresso

Author

Jianhua Zhang

References

Documentations of affy package

Examples

if(interactive()){
require(widgetTools)
expressoWidget(c("mas", "none", "rma"), c("constant", "quantiles"),
c("mas", "pmonly"), c("liwong", "playerout"))
}

Fit Li and Wong Model to a Probe Set

Description

Fits the model described in Li and Wong (2001) to a probe set with I chips and J probes.

Usage

fit.li.wong(data.matrix, remove.outliers=TRUE, normal.array.quantile=0.5,
            normal.resid.quantile=0.9, large.threshold=3, large.variation=0.8,
            outlier.fraction=0.14, delta=1e-06, maxit=50,
            outer.maxit=50,verbose=FALSE, ...)
li.wong(data.matrix,remove.outliers=TRUE, normal.array.quantile=0.5,
            normal.resid.quantile=0.9, large.threshold=3, large.variation=0.8,
            outlier.fraction=0.14, delta=1e-06, maxit=50,
            outer.maxit=50,verbose=FALSE)

Arguments

Details

This is Bioconductor's implementation of the Li and Wong algorithm. The Li and Wong PNAS 2001 paper was followed. However, you will not get the same results as you would get with dChip. dChip is not open source so it is not easy to reproduce.

Notice that this iterative algorithm will not always converge. If you run the algorithm on thousands of probes expect some non-convergence warnings. These are more likely when few arrays are used. We recommend using this method only if you have 10 or more arrays.

Please refer to references for more details.

Value

li.wong returns a vector of expression measures (or column effects) followed by their respective standard error estimates. It was designed to work with express which is no longer part of the package.

fit.li.wong returns much more. Namely, a list containing the fitted parameters and relevant information.

*

Seealso

li.wong , expresso

Author

Rafael A. Irizarry, Cheng Li, Fred A. Wright, Ben Bolstad

References

Li, C. and Wong, W.H. (2001) list("Genome Biology") list("2") , 1--11. list()

Li, C. and Wong, W.H. (2001) list("Proc. Natl. Acad. Sci USA") list("98") , 31--36.

Examples

x <- sweep(matrix(2^rnorm(600),30,20),1,seq(1,2,len=30),FUN="+")
fit1 <- fit.li.wong(x)
plot(x[1,])
lines(fit1$theta)

generate a set of expression values

Description

Generate a set of expression values from the probe pair information. The set of expression is returned as an ExpressionSet object.

Usage

computeExprSet(x, pmcorrect.method, summary.method, ...)
  generateExprSet.methods()
  upDate.generateExprSet.methods(x)

Arguments

Details

An extra argument ids= can be passed. It must be a vector of affids. The expression values will only be computed and returned for these affyids.

The different methods available through this mechanism can be accessed by calling the method generateExprSet.methods with an object of call Cel.container as an argument.

In the Affymetrix design, MM probes were included to measure the noise (or background signal). The original algorithm for background correction was to subtract the MM signal to the PM signal. The methods currently included in the package are "bg.correct.subtractmm", "bg.correct.pmonly" and "bg.correct.adjust".

To alter the available methods for generating ExprSets use upDate.generateExprSet.methods.

Seealso

method generateExprSet of the class AffyBatch list() expresso

Examples

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

ids <- c( "1000_at","1001_at")

eset <- computeExprSet(Dilution, pmcorrect.method="pmonly",
summary.method="avgdiff", ids=ids)
}

Compute a summary expression value from the probes intensities

Description

Compute a summary expression value from the probes intensities

Usage

express.summary.stat(x, pmcorrect, summary, ...)
express.summary.stat.methods() # vector of names of methods
upDate.express.summary.stat.methods(x)

Arguments

Value

Returns a vector of expression values.

Examples

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

p <- probeset(Dilution, "1001_at")[[1]]

par(mfcol=c(5,2))
mymethods <- express.summary.stat.methods()
nmet <- length(mymethods)
nc <- ncol(pm(p))

layout(matrix(c(1:nc, rep(nc+1, nc)), nc, 2), width = c(1, 1))

barplot(p)

results <- matrix(0, nc, nmet)
rownames(results) <- paste("sample", 1:nc)
colnames(results) <- mymethods

for (i in 1:nmet) {
ev <- express.summary.stat(p, summary=mymethods[i], pmcorrect="pmonly")
if (mymethods[[i]] != "medianpolish")
results[, i] <- 2^(ev$exprs)
else
results[, i] <- ev$exprs
}

dotchart(results, labels=paste("sample", 1:nc))
}

Generate an expression value from the probes informations

Description

Generate an expression from the probes

Usage

generateExprVal.method.avgdiff(probes, ...)
generateExprVal.method.medianpolish(probes, ...)
generateExprVal.method.liwong(probes, ...)
generateExprVal.method.mas(probes, ...)

Arguments

Value

A list containing entries:

*

Seealso

generateExprSet-methods , generateExprVal.method.playerout , fit.li.wong

Examples

data(SpikeIn) ##SpikeIn is a ProbeSets
probes <- pm(SpikeIn)
avgdiff <- generateExprVal.method.avgdiff(probes)
medianpolish <- generateExprVal.method.medianpolish(probes)
liwong <- generateExprVal.method.liwong(probes)
playerout <- generateExprVal.method.playerout(probes)
mas <- generateExprVal.method.mas(probes)

concentrations <- as.numeric(sampleNames(SpikeIn))
plot(concentrations,avgdiff$exprs,log="xy",ylim=c(50,10000),pch="a",type="b")
points(concentrations,2^medianpolish$exprs,pch="m",col=2,type="b",lty=2)
points(concentrations,liwong$exprs,pch="l",col=3,type="b",lty=3)
points(concentrations,playerout$exprs,pch="p",col=4,type="b",lty=4)
points(concentrations,mas$exprs,pch="p",col=4,type="b",lty=4)

Generate an expression value from the probes informations

Description

Generate an expression from the probes

Usage

generateExprVal.method.playerout(probes, weights=FALSE, optim.method="L-BFGS-B")

Arguments

Details

A non-parametric method to weight each perfect match probe in the set and to compute a weighted mean of the perfect match values. One will notice this method only makes use of the perfect matches. (see function playerout.costfunction for the cost function).

Value

A vector of expression values.

Author

Laurent laurent@cbs.dtu.dk list() (Thanks to E. Lazaridris for the original playerout code and the discussions about it)

References

Emmanuel N. Lazaridis, Dominic Sinibaldi, Gregory Bloom, Shrikant Mane and Richard Jove A simple method to improve probe set estimates from oligonucleotide arrays, Mathematical Biosciences, Volume 176, Issue 1, March 2002, Pages 53-58

Hybrid Log

Description

Given a constant c this function returns x if x is less than c and sign(x)*(c*log(abs(x)/c) if its not. Notice this is a continuous odd ( f(-x)=-f(x) ) function with continuous first derivative. The main purpose is to perform log transformation when one has negative numbers, for example for PM-MM.

Usage

hlog(x, constant=1)

Arguments

Details

If constant is less than or equal to 0 log(x) is returned for all x . If constant is infinity x is returned for all x .

Author

Rafael A. Irizarry

Read CEL files into an ExpressionSet

Description

Read CEL files and compute an expression measure without using an AffyBatch.

Usage

just.rma(list(), filenames = character(0),
               phenoData = new("AnnotatedDataFrame"),
               description = NULL,
               notes = "",
               compress = getOption("BioC")$affy$compress.cel,
               rm.mask = FALSE, rm.outliers = FALSE, rm.extra = FALSE,
               verbose=FALSE, background=TRUE, normalize=TRUE,
               bgversion=2, destructive=FALSE, cdfname = NULL)
justRMA(list(), filenames=character(0),
              widget=getOption("BioC")$affy$use.widgets,
              compress=getOption("BioC")$affy$compress.cel,
              celfile.path=getwd(),
              sampleNames=NULL,
              phenoData=NULL,
              description=NULL,
              notes="",
              rm.mask=FALSE, rm.outliers=FALSE, rm.extra=FALSE,
              hdf5=FALSE, hdf5FilePath=NULL,verbose=FALSE,
              normalize=TRUE, background=TRUE, 
              bgversion=2, destructive=FALSE, cdfname = NULL)

Arguments

Details

justRMA is a wrapper for just.rma that permits the user to read in phenoData, MIAME information, and CEL files using widgets. One can also define files where to read phenoData and MIAME information.

If the function is called with no arguments justRMA() , then all the CEL files in the working directory are read, converted to an expression measure using RMA and put into an ExpressionSet . However, the arguments give the user great flexibility.

phenoData is read using read.AnnotatedDataFrame . If a character is given, it tries to read the file with that name to obtain the AnnotatedDataFrame object as described in read.AnnotatedDataFrame . If left NULL and widget=FALSE ( widget=TRUE is not currently supported), then a default object is created. It will be an object of class AnnotatedDataFrame with its pData being a data.frame with column x indexing the CEL files.

description is read using read.MIAME . If a character is given, it tries to read the file with that name to obtain a MIAME instance. If left NULL but widget=TRUE , then widgets are used. If left NULL and widget=FALSE , then an empty instance of MIAME is created.

The arguments rm.masks , rm.outliers , rm.extra are passed along to the function read.celfile .

Value

An ExpressionSet object, containing expression values identical to what one would get from running rma on an AffyBatch .

Seealso

rma , read.affybatch

Author

In the beginning: James MacDonald jmacdon@med.umich.edu Supporting routines, maintenance and just.rma: Ben Bolstad bmb@bmbolstad.com

List the Cel Files in a Directory/Folder

Description

This function produces a vector containing the names of files in the named directory/folder ending in .cel or .CEL.

Usage

list.celfiles(...)

Arguments

Value

A character vector of file names.

Seealso

list.files

Examples

list.celfiles()

Select Subset

Description

Select a subset of rows with small rank-range over columns.

Usage

maffy.subset(data,subset.size=5000,maxit=100,
             subset.delta=max(round(subset.size/100),25),verbose=FALSE)

Arguments

Details

Please refer to references.

Value

A list with component subset , the indexes for subset.

Seealso

maffy.normalize

Author

Magnus Astrand

References

Astrand, M. (2001) http://www.math.chalmers.se/~magnusaa/maffy/

Examples

if (require(affydata)) {
#data(Dilution)
#x <- log2(pm(Dilution)[,1:3])
#Index <- maffy.subset(x,subset.size=100)$subset
#mva.pairs(x[Index,])
}

MAS 5.0 expression measure

Description

This function converts an instance of AffyBatch into an instance of ExpressionSet using our implementation of Affymetrix's MAS 5.0 expression measure.

Usage

mas5(object, normalize = TRUE, sc = 500, analysis = "absolute", ...)

Arguments

Details

This function is a wrapper for expresso and affy.scalevalue.exprSet .

Value

ExpressionSet

The methods used by this function were implemented based upon available documentation. In particular a useful reference is Statistical Algorithms Description Document by Affymetrix. Our implementation is based on what is written in the documentation and, as you might appreciate, there are places where the documentation is less than clear. This function does not give exactly the same results. All source code of our implementation is available. You are free to read it and suggest fixes.

For more information visit this URL: http://stat-www.berkeley.edu/users/bolstad/

Seealso

expresso , affy.scalevalue.exprSet

Examples

if (require(affydata)) {
data(Dilution)
eset <- mas5(Dilution)
}

MAS 5.0 Absolute Detection

Description

Performs the Wilcoxon signed rank-based gene expression presence/absence detection algorithm first implemented in the Affymetrix Microarray Suite version 5.

Usage

mas5calls(object,...)
mas5calls.AffyBatch(object, ids = NULL, verbose = TRUE, tau = 0.015,
                    alpha1 = 0.04, alpha2 = 0.06,
                    ignore.saturated=TRUE) 
mas5calls.ProbeSet(object, tau = 0.015, alpha1 = 0.04, alpha2 = 0.06,
                   ignore.saturated=TRUE) 
mas5.detection(mat, tau = 0.015, alpha1 = 0.04, alpha2 = 0.06,
               exact.pvals = FALSE, cont.correct = FALSE)

Arguments

Details

This function performs the hypothesis test:

H0: median(Ri) = tau, corresponding to absence of transcript H1: median(Ri) > tau, corresponding to presence of transcript

where Ri = (PMi - MMi) / (PMi + MMi) for each i a probe-pair in the probe-set represented by data.

Currently exact.pvals=TRUE is not supported, and cont.correct=TRUE works but does not give great results (so both should be left as FALSE). The defaults for tau, alpha1 and alpha2 correspond to those in MAS5.0.

The p-value that is returned estimates the usual quantity:

| Pr(observing a more "present looking" probe-set than data | data is absent)|

So that small p-values imply presence while large ones imply absence of transcript. The detection call is computed by thresholding the p-value as in:

call "P" if p-value < alpha1 call "M" if alpha1 <= p-value < alpha2 call "A" if alpha2 <= p-value

This implementation has been validated against the original MAS5.0 implementation with the following results (for exact.pvals and cont.correct set to F):

Average Relative Change from MAS5.0 p-values:38% Proportion of calls different to MAS5.0 calls:1.0%

where "average/proportion" means over all probe-sets and arrays, where the data came from 11 bacterial control probe-sets spiked-in over a range of concentrations (from 0 to 150 pico-mols) over 26 arrays. These are the spike-in data from the GeneLogic Concentration Series Spikein Dataset.

Clearly the p-values computed here differ from those computed by MAS5.0 -- this will be improved in subsequent releases of the affy package. However the p-value discrepancies are small enough to result in the call being very closely aligned with those of MAS5.0 (99 percent were identical on the validation set) -- so this implementation will still be of use.

The function mas5.detect is no longer the engine function for the others. C code is no available that computes the Wilcox test faster. The function is kept so that people can look at the R code (instead of C).

Value

mas5.detect returns a list containing the following components:

The mas5calls method for AffyBatch returns an ExpressionSet with calls accessible with exprs(obj) and p-values available with assayData(obj)[["se.exprs"]] . The code mas5calls for ProbeSet returns a list with vectors of calls and p-values.

Author

Crispin Miller, Benjamin I. P. Rubinstein, Rafael A. Irizarry

References

Liu, W. M. and Mei, R. and Di, X. and Ryder, T. B. and Hubbell, E. and Dee, S. and Webster, T. A. and Harrington, C. A. and Ho, M. H. and Baid, J. and Smeekens, S. P. (2002) Analysis of high density expression microarrays with signed-rank call algorithms, Bioinformatics, 18(12), pp. 1593--1599.

Liu, W. and Mei, R. and Bartell, D. M. and Di, X. and Webster, T. A. and Ryder, T. (2001) Rank-based algorithms for analysis of microarrays, Proceedings of SPIE, Microarrays: Optical Technologies and Informatics, 4266.

Affymetrix (2002) Statistical Algorithms Description Document, Affymetrix Inc., Santa Clara, CA, whitepaper. http://www.affymetrix.com/support/technical/whitepapers/sadd_whitepaper.pdf , http://www.affymetrix.com/support/technical/whitepapers/sadd_whitepaper.pdf

Examples

if (require(affydata)) {
data(Dilution)
PACalls <- mas5calls(Dilution)
}

merge two AffyBatch objects

Description

merge two AffyBatch objects into one.

Usage

list(list("merge"), list("AffyBatch"))(x, y, annotation = paste(annotation(x),
                 annotation(y)), description = NULL, notes =
                 character(0), ...)

Arguments

Details

To be done.

Value

A object if class AffyBatch .

Seealso

AffyBatch-class

M vs. A Matrix

Description

A matrix of M vs. A plots is produced. Plots are made on the upper triangle and the IQR of the Ms are displayed in the lower triangle

Usage

mva.pairs(x, labels=colnames(x), log.it=TRUE,span=2/3,family.loess="gaussian",
          digits=3,line.col=2,main="MVA plot",cex=2,...)

Arguments

Seealso

pairs

Examples

x <- matrix(rnorm(4000),1000,4)
x[,1] <- x[,1]^2
dimnames(x) <- list(NULL,c("chip 1","chip 2","chip 3","chip 4"))
mva.pairs(x,log=FALSE,main="example")

Normalize Affymetrix Probe Level Data - methods

Description

Method for normalizing Affymetrix Probe Level Data

Usage

normalize.methods(object)
bgcorrect.methods()
upDate.bgcorrect.methods(x)
pmcorrect.methods()
upDate.pmcorrect.methods(x)

Arguments

Details

If object is an AffyBatch object, then normalize(object) returns an AffyBatch object with the intensities normalized using the methodology specified by getOption("BioC")$affy$normalize.method . The affy package default is quantiles .

Other methodologies can be used by specifying them with the method argument. For example to use the invariant set methodology described by Li and Wong (2001) one would type: normalize(object, method="invariantset") .

Further arguments passed by ... , apart from method , are passed along to the function responsible for the methodology defined by the method argument.

A character vector of nicknames for the methodologies available is returned by normalize.methods(object)) , where object is an AffyBatch , or simply by typing normalize.AffyBatch.methods . If the nickname of a method is called "loess", the help page for that specific methodology can be accessed by typing ?normalize.loess .

For more on the normalization methodologies currently implemented please refer to the vignette Custom Processing Methods'. To add your own normalization procedures please refer to the customMethods vignette. The functions:bgcorrect.methods,pmcorrect.methods, provide access to internal vectors listing the corresponding capabilities. ## Seealso [AffyBatch-class](#affybatch-class) , [normalize`](#normalize) . ## Examples r if (require(affydata)) { data(Dilution) normalize.methods(Dilution) generateExprSet.methods() bgcorrect.methods() pmcorrect.methods() }

Scale probe intensities

Description

Scale array intensities in a AffyBatch .

Usage

normalize.AffyBatch.constant(abatch, refindex=1, FUN=mean, na.rm=TRUE)
normalize.constant(x, refconstant, FUN=mean, na.rm=TRUE)

Arguments

Value

%A code{link[Biobase]{container-class}} of normalized objects. An AffyBatch with an attribute "constant" holding the value of the factor used for scaling.

Seealso

AffyBatch

Author

L. Gautier laurent@cbs.dtu.dk

Normalize intensities using the contrasts method

Description

Scale chip objects in an AffyBatch-class .

Usage

%normalize.contrast() ## currently maffy.normalizenormalize.AffyBatch.contrasts(abatch,span=2/3, choose.subset=TRUE,
                              subset.size=5000, verbose=TRUE,
                              family="symmetric",
                              type=c("together","pmonly","mmonly","separate"))

Arguments

Value

An object of the same class as the one passed.

Seealso

maffy.normalize

Invariant Set normalization

Description

Normalize arrays in an AffyBatch using an invariant set.

Usage

normalize.AffyBatch.invariantset(abatch, prd.td = c(0.003, 0.007),
                                 verbose = FALSE,
                                 baseline.type = c("mean","median","pseudo-mean","pseudo-median"),
                                 type = c("separate","pmonly","mmonly","together"))
normalize.invariantset(data, ref, prd.td=c(0.003,0.007))

Arguments

Details

The set of invariant intensities between data and ref is found through an iterative process (based on the respective ranks the intensities). This set of intensities is used to generate a normalization curve by smoothing.

The type argument should be one of "separate","pmonly","mmonly","together" which indicates whether to normalize only one probe type (PM,MM) or both together or separately.

Value

Respectively a AffyBatch of normalized objects, or a vector of normalized intensities, with an attribute "invariant.set" holding the indexes of the 'invariant' intensities.

Seealso

normalize to normalize AffyBatch objects.

Author

L. Gautier laurent@cbs.dtu.dk (Thanks to Cheng Li for the discussions about the algorithm.)

References

Cheng Li and Wing Hung Wong, Model-based analysis of oligonucleotides arrays: model validation, design issues and standard error application. Genome Biology 2001, 2(8):research0032.1-0032.11

Scale microarray data

Description

Normalizes arrays using loess.

Usage

normalize.loess(mat, subset = sample(1:(dim(mat)[1]), min(c(5000,
                 nrow(mat)))), epsilon = 10^-2, maxit = 1, log.it =
                 TRUE, verbose = TRUE, span = 2/3, family.loess =
                 "symmetric")
normalize.AffyBatch.loess(abatch,type=c("together","pmonly","mmonly","separate"), ...)

Arguments

Details

The type argument should be one of "separate","pmonly","mmonly","together" which indicates whether to normalize only one probe type (PM,MM) or both together or separately.

Seealso

normalize

Examples

if (require(affydata)) {
#data(Dilution)
#x <- pm(Dilution[,1:3])
#mva.pairs(x)
#x <- normalize.loess(x,subset=1:nrow(x))
#mva.pairs(x)
}

Normalize arrays

Description

normalizes arrays in an AffyBatch each other or to a set of target intensities

Usage

normalize.AffyBatch.qspline(abatch,type=c("together", "pmonly", "mmonly",
                 "separate"), ...)
normalize.qspline(x, target = NULL, samples = NULL, 
                  fit.iters = 5, min.offset = 5, 
                  spline.method = "natural", smooth = TRUE,
                  spar = 0, p.min = 0, p.max = 1.0, 
                  incl.ends = TRUE, converge = FALSE, 
                  verbose = TRUE, na.rm = FALSE)

Arguments

Details

This normalization method uses the quantiles from each array and the target to fit a system of cubic splines to normalize the data. The target should be the mean (geometric) or median of each probe but could also be the name of a particular chip in the abatch object.

Parameters setting can be of much importance when using this method. The parameter fit.iter is used as a starting point to find a more appropriate value. Unfortunately the algorithm used do not converge in some cases. If this happens, the fit.iter value is used and a warning is thrown. Use of different settings for the parameter samples was reported to give good results. More specifically, for about 200 data points use samples = 0.33 , for about 2000 data points use samples = 0.05 , for about 10000 data points use samples = 0.02 (thanks to Paul Boutros).

The type argument should be one of "separate","pmonly","mmonly","together" which indicates whether to normalize only one probe type (PM,MM) or both together or separately.

Value

a normalized AffyBatch .

Author

Laurent and Workman C.

References

Christopher Workman, Lars Juhl Jensen, Hanne Jarmer, Randy Berka, Laurent Gautier, Henrik Bjorn Nielsen, Hans-Henrik Saxild, Claus Nielsen, Soren Brunak, and Steen Knudsen. A new non-linear normal- ization method for reducing variability in dna microarray experiments. Genome Biology, accepted, 2002

Quantile Normalization

Description

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

Usage

normalize.AffyBatch.quantiles(abatch, type=c("separate","pmonly","mmonly","together"))

Arguments

Details

This method is based upon the concept of a quantile-quantile plot extended to n dimensions. No special allowances are made for outliers. If you make use of quantile normalization either through rma or expresso please cite Bolstad et al, Bioinformatics (2003).

The type argument should be one of "separate","pmonly","mmonly","together" which indicates whether to normalize only one probe type (PM,MM) or both together or separately.

Value

A normalized AffyBatch .

Seealso

normalize

Author

Ben Bolstad, bmbolstad.com

References

Bolstad, B (2001) list("Probe Level Quantile Normalization of High Density ", " Oligonucleotide Array Data") . Unpublished manuscript http://bmbolstad.com/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://bmbolstad.com/misc/normalize/normalize.html

Robust Quantile Normalization

Description

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

Usage

normalize.AffyBatch.quantiles.robust(abatch,
                                         type = c("separate","pmonly","mmonly","together"),
                                         weights = NULL,
                                         remove.extreme = c("variance","mean","both","none"),
                                         n.remove = 1, use.median = FALSE,
                                         use.log2 = FALSE)

Arguments

Details

This method is based upon the concept of a quantile-quantile plot extended to n dimensions. Note that the matrix is of intensities not log intensities. The function performs better with raw intensities.

Choosing variance will remove chips with variances much higher or lower than the other chips, mean removes chips with the mean most different from all the other means, both removes first extreme variance and then an extreme mean. The option none does not remove any chips, but will assign equal weights to all chips.

The type argument should be one of "separate","pmonly","mmonly","together" which indicates whether to normalize only one probe type (PM,MM) or both together or separately.

Value

a matrix of normalized intensities

Seealso

normalize , normalize.quantiles

Note

This function is still experimental.

Author

Ben Bolstad, bmb@bmbolstad.com

plot intensities using 'pairs'

Description

Plot intensities using the function 'pairs'

Usage

list(list("pairs"), list("AffyBatch"))(x, panel=points, ..., transfo=I, main=NULL, oma=NULL,
                  font.main = par("font.main"),
                  cex.main = par("cex.main"), cex.labels = NULL, 
                  lower.panel=panel, upper.panel=NULL, diag.panel=NULL,
                  font.labels = 1, row1attop = TRUE, gap = 1)

Arguments

Details

Plots with several chips can represent zillions of points. They require a lot of memory and can be very slow to be displayed. You may want to try to split of the plots, or to plot them in a device like 'png' or 'jpeg'.

Plot a location on a cel image

Description

Plots a location on a previously plotted cel image. This can be used to locate the physical location of probes on the array.

Usage

plotLocation(x, col="green", pch=22, ...)

Arguments

Seealso

AffyBatch

Author

Laurent

Examples

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

## image of the celfile
image(Dilution[, 1])

## genenames, arbitrarily pick the 101th
n <- geneNames(Dilution)[101]

## get the location for the gene n
l <- indexProbes(Dilution, "both", n)[[1]]
## convert the index to X/Y coordinates
xy <- indices2xy(l, abatch=Dilution)

## plot
plotLocation(xy)
}

plot a probe set

Description

Plot intensities by probe set.

Usage

list(list("plot"), list("ProbeSet"))(x, which=c("pm", "mm"), xlab = "probes", type = "l", ylim = NULL, ...)

Arguments

Value

This function is only used for its (graphical) side-effect.

Seealso

ProbeSet

Examples

data(SpikeIn)
plot(SpikeIn)

Plot Densities

Description

Plots the non-parametric density estimates using values contained in the columns of a matrix.

Usage

%matdensity(mat, ylab = "density", xlab="x", type="l", ...)plotDensity(mat, ylab = "density", xlab="x", type="l", col=1:6,
            na.rm = TRUE, ...)
plotDensity.AffyBatch(x, col = 1:6, log = TRUE,
                      which=c("pm","mm","both"),
                      ylab = "density",
                      xlab = NULL, ...)

Arguments

Details

The list returned can be convenient for plotting large input matrices with different colors/line types schemes (the computation of the densities can take some time).

To match other functions in base R, this function should probably be called matdensity , as it is sharing similarities with matplot and matlines .

Value

It returns invisibly a list of two matrices x' andy'.

Author

Ben Bolstad and Laurent Gautier

Examples

if (require(affydata)) {
data(Dilution)
plotDensity(exprs(Dilution), log="x")
}

PM Correction

Description

Corrects the PM intensities in a ProbeSet for non-specific binding.

Usage

pmcorrect.pmonly(object)
pmcorrect.subtractmm(object)
pmcorrect.mas(object, contrast.tau=0.03, scale.tau=10, delta=2^(-20))

Arguments

Details

These are the pm correction methods perfromed by Affymetrix MAS 4.0 (subtractmm) and MAS 5.0 (mas). See the Affymetrix Manual for details. pmonly does what you think: does not change the PM values.

Value

A ProbeSet for which the pm slot contains the corrected PM values.

References

Affymetrix MAS 4.0 and 5.0 manual

Examples

if (require(affydata)) {
data(Dilution)
gn <- geneNames(Dilution)
pps <- probeset(Dilution, gn[1])[[1]]

pps.pmonly <- pmcorrect.pmonly(pps)
pps.subtractmm <- pmcorrect.subtractmm(pps)
pps.mas5 <- pmcorrect.mas(pps)
}

Apply a function over the ProbeSets in an AffyBatch

Description

Apply a function over the ProbeSets in an AffyBatch

Usage

ppsetApply(abatch, FUN, genenames = NULL, ...)
ppset.ttest(ppset, covariate, pmcorrect.fun = pmcorrect.pmonly, ...)

Arguments

Value

Returns a list of objects, or values, as returned by the function FUN for each ProbeSet it processes.

Seealso

ProbeSet-class

Author

Laurent Gautier laurent@cbs.dtu.dk

Examples

ppset.ttest <- function(ppset, covariate, pmcorrect.fun = pmcorrect.pmonly, ...) {
probes <- do.call("pmcorrect.fun", list(ppset))
my.ttest <- function(x) {
y <- split(x, get(covariate))
t.test(y[[1]], y[[2]])$p.value
}
r <- apply(probes, 1, my.ttest)
return(r)
}
##this takes a long time - and rowttests is a good alternative
## eg: rt = rowttests(exprs(Dilution), Dilution$liver)
data(Dilution)
all.ttest <- ppsetApply(Dilution, ppset.ttest, covariate="liver")

Methods for accessing perfect matches and mismatches

Description

Methods for perfect matches and mismatches probes

Methods for accessing the Probe Names

Description

Methods for accessing Probe Names

Read CEL files into an AffyBatch

Description

Read CEL files into an Affybatch.

Usage

read.affybatch(list(), filenames = character(0),
               phenoData = new("AnnotatedDataFrame"),
               description = NULL,
               notes = "",
               compress = getOption("BioC")$affy$compress.cel,
               rm.mask = FALSE, rm.outliers = FALSE, rm.extra = FALSE,
               verbose = FALSE,sd=FALSE, cdfname = NULL)
ReadAffy(list(), filenames=character(0),
              widget=getOption("BioC")$affy$use.widgets,
              compress=getOption("BioC")$affy$compress.cel,
              celfile.path=NULL,
              sampleNames=NULL,
              phenoData=NULL,
              description=NULL,
              notes="",
              rm.mask=FALSE, rm.outliers=FALSE, rm.extra=FALSE,
              verbose=FALSE,sd=FALSE, cdfname = NULL)

Arguments

Details

ReadAffy is a wrapper for read.affybatch that permits the user to read in phenoData, MIAME information, and CEL files using widgets. One can also define files where to read phenoData and MIAME information.

If the function is called with no arguments ReadAffy() all the CEL files in the working directory are read and put into an AffyBatch . However, the arguments give the user great flexibility.

If phenoData is a character vector of length 1, the function read.AnnotatedDataFrame is called to read a file of that name and produce the AnnotationDataFrame object with the sample metadata. If phenoData is a data.frame , it is converted to an AnnotatedDataFrame . If it is NULL and widget=FALSE ( widget=TRUE is not currently supported), then a default object of class AnnotatedDataFrame is created, whose pData is a data.frame with rownames being the names of the CEL files, and with one column sample with an integer index.

AllButCelsForReadAffy is an internal function that gets called by ReadAffy . It gets all the information except the cel intensities.

description is read using read.MIAME . If a character is given, then it tries to read the file with that name to obtain a MIAME instance. If left NULL but widget=TRUE , then widgets are used. If left NULL and widget=FALSE , then an empty instance of MIAME is created.

Value

An AffyBatch object.

Seealso

AffyBatch

Author

Ben Bolstad bmb@bmbolstad.com (read.affybatch), Laurent Gautier, and Rafael A. Irizarry (ReadAffy)

Examples

if(require(affydata)){
celpath <- system.file("celfiles", package="affydata")
fns <- list.celfiles(path=celpath,full.names=TRUE)

cat("Reading files:
",paste(fns,collapse="
"),"
")
##read a binary celfile
abatch <- ReadAffy(filenames=fns[1])
##read a text celfile
abatch <- ReadAffy(filenames=fns[2])
##read all files in that dir
abatch <- ReadAffy(celfile.path=celpath)
}

Read CEL file data into PM or MM matrices

Description

Read CEL data into matrices.

Usage

read.probematrix(..., filenames = character(0),
                 phenoData = new("AnnotatedDataFrame"),
                 description = NULL,
                 notes = "",
                 compress = getOption("BioC")$affy$compress.cel,
                 rm.mask = FALSE, rm.outliers = FALSE, rm.extra = FALSE,
                 verbose = FALSE, which = "pm", cdfname = NULL)

Arguments

Value

A list of one or two matrices. Each matrix is either PM or MM data. No AffyBatch is created.

Seealso

AffyBatch , read.affybatch

Author

Ben Bolstad bmb@bmbolstad.com

Robust Multi-Array Average expression measure

Description

This function converts an AffyBatch object into an ExpressionSet object using the robust multi-array average (RMA) expression measure.

Usage

rma(object, subset=NULL, verbose=TRUE, destructive=TRUE, normalize=TRUE,
    background=TRUE, bgversion=2, ...)

Arguments

Details

This function computes the RMA (Robust Multichip Average) expression measure described in Irizarry et al Biostatistics (2003).

Note that this expression measure is given to you in log base 2 scale. This differs from most of the other expression measure methods.

Please note that the default background adjustment method was changed during the lead up to the Bioconductor 1.2 release. This means that this function and expresso should give results that directly agree.

Value

An ExpressionSet

Seealso

expresso

Author

Ben Bolstad bmb@bmbolstad.com

References

Rafael. A. Irizarry, Benjamin M. Bolstad, Francois Collin, Leslie M. Cope, Bridget Hobbs and Terence P. Speed (2003), Summaries of Affymetrix GeneChip probe level data Nucleic Acids Research 31(4):e15

Bolstad, B.M., Irizarry R. A., Astrand M., and Speed, T.P. (2003), A Comparison of Normalization Methods for High Density Oligonucleotide Array Data Based on Bias and Variance. Bioinformatics 19(2):185-193

Irizarry, RA, Hobbs, B, Collin, F, Beazer-Barclay, YD, Antonellis, KJ, Scherf, U, Speed, TP (2003) Exploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data. Biostatistics .Vol. 4, Number 2: 249-264

Examples

if (require(affydata)) {
data(Dilution)
eset <- rma(Dilution)
}

~~function to set options ~~

Description

~~ Set the options for the package

Usage

.setAffyOptions(affy.opt = NA)

Arguments

Details

See the vignettes to know more. This function could disappear in favor of a more general one the package Biobase.

Value

The function is used for its side effect. Nothing is returned.

Author

Laurent

Examples

affy.opt <- getOption("BioC")$affy

.setAffyOptions(affy.opt)

Probe Set Summarizing Functions

Description

These were used with the function express , which is no longer part of the package. Some are still used by the generateExprVal functions, but you should avoid using them directly.

Seealso

expresso

One-step Tukey's biweight

Description

One-step Tukey's biweight on a matrix.

Usage

tukey.biweight(x, c = 5, epsilon = 1e-04)

Arguments

Details

The details can be found in the given reference.

Value

a vector of values (one value per column in the input matrix).

Seealso

pmcorrect.mas and generateExprVal.method.mas

References

Statistical Algorithms Description Document, 2002, Affymetrix.

Find which CDF corresponds

Description

Find which kind of CDF corresponds to a CEL file.

Usage

whatcdf(filename, compress = getOption("BioC")$affy$compress.cel)

Arguments

Details

Information concerning the corresponding CDF file seems to be found in CEL files. This allows us to try to link CDF information automatically.

Value

a character with the name of the CDF.

Seealso

getInfoInAffyFile , read.celfile

Functions to convert indices to x/y (and reverse)

Description

Functions to convert indices to x/y (and reverse)

Usage

xy2indices(x, y, nc = NULL, cel = NULL, abatch = NULL, cdf = NULL, xy.offset = NULL)
indices2xy(i, nc = NULL, cel = NULL, abatch = NULL, cdf = NULL, xy.offset = NULL)

Arguments

Details

The Affymetrix scanner reads data from a GeneChip by row, and exports those data to a CEL file. When we read in the CEL file data to an AffyBatch object, we store data for each GeneChip as a single column in a matrix of probe-wise intensity values.

The CDF files that Affymetrix make available for various GeneChips map individual probes to probesets based on their (x,y) coordinates on the GeneChip. Note that these coordinates are zero-based, and (x,y) is the same as (column, row). In other words, the x coordinate indicates the horizontal location of the probe, and the y coordinate indicates the vertical location of the probe. By convention, (0,0) is the coordinate location for the top left position, and (ncol-1, nrow-1) is the coordinate location of the lower right position.

For most users, the mapping of probes to probeset is handled internally by various functions ( rma , espresso , etc), and in general usage it is never necessary for a user to convert probe index position in an AffyBatch to the corresponding (x,y) coordinates on the GeneChip. These functions are only useful for those who wish to know more about the internal workings of the Affymetrix GeneChip.

The parameter xy.offset is there for compatibility. For historical reasons, the xy-coordinates for the features on Affymetrix GeneChips were decided to start at 1 (one) rather than 0 (zero). One can set the offset to 1 or to 0. Unless the you really know what you are doing, it is advisable to let it at the default value NULL . This way the package-wide option xy.offset is always used.

Value

A vector of indices or a two-columns matrix of Xs and Ys.

Seealso

indexProbes

Author

L.

Examples

if (require(affydata)) {
data(Dilution)
pm.i <- indexProbes(Dilution, which="pm", genenames="AFFX-BioC-5_at")[[1]]
mm.i <- indexProbes(Dilution, which="mm", genenames="AFFX-BioC-5_at")[[1]]

pm.i.xy <- indices2xy(pm.i, abatch = Dilution)
mm.i.xy <- indices2xy(mm.i, abatch = Dilution)

## and back to indices
i.pm <- xy2indices(pm.i.xy[,1], pm.i.xy[,2], cdf = "hgu95av2cdf")
i.mm <- xy2indices(mm.i.xy[,1], mm.i.xy[,2], cdf = "hgu95av2cdf")

identical(pm.i, as.integer(i.pm))
identical(mm.i, as.integer(i.mm))

image(Dilution[1], transfo=log2)
## plot the pm in red
plotLocation(pm.i.xy, col="red")
plotLocation(mm.i.xy, col="blue")
}