A Simple Aggregation Mechanism.

Description

Given an environment and an aggregator (an object of class aggregate simple aggregations are made.

Usage

Aggregate(x, agg)

Arguments

Details

Given some data, x the user can accumulate (or aggregate) information in env using the two supplied functions. See the accompanying documentation for a more complete example of this function and its use.

Value

No value is returned. This function is evaluated purely for side effects. The symbols and values in env are altered.

Seealso

new.env , class:aggregator

Author

R. Gentleman

Examples

agg1 <- new("aggregator")
Aggregate(letters[1:10], agg1)
# the first 10 letters should be symbols in env1 with values of 1
Aggregate(letters[5:11], agg1)
# now letters[5:10] should have value 2
bb <- mget(letters[1:11], env=aggenv(agg1), ifnotfound=NA)
t1 <- as.numeric(bb); names(t1) <- names(bb)
t1
# a b c d e f g h i j k
# 1 1 1 1 2 2 2 2 2 2 1

Biobase Package Overview

Description

Biobase Package Overview

Details

Important data classes: ExpressionSet , AnnotatedDataFrame MIAME . Full help on methods and associated functions is available from within class help pages.

Additional data classes: eSet , MIAxE , MultiSet . Additional manipulation and data structuring classes: Versioned , VersionedBiobase , aggregator , container .

Vignette routines: openVignette , getPkgVigs , openPDF .

Package manipulation functions: createPackage and package.version

Data sets: aaMap , sample.ExpressionSet , geneData .

Introductory information is available from vignettes, type openVignette() .

Full listing of documented articles is available in HTML view by typing help.start() and selecting Biobase package from the Packages menu or via library(help="Biobase") .

Author

O. Sklyar

Utility classes for length one (scalar) objects

Description

These classes represent scalar quantities, such as a string or a number and are useful because they provide their own validity checking. The classes ScalarCharacter , ScalarLogical , ScalarInteger , and ScalarNumeric all extend their respective base vector types and can be used interchangeably (except they should always have length one).

The mkScalar factory function provides a convenient way of creating Scalar<type> objects (see the examples section below).

Usage

mkScalar(obj)

Arguments

Author

Seth Falcon

Examples

v <- list(mkScalar("a single string"),
mkScalar(1),
mkScalar(1L),
mkScalar(TRUE))
sapply(v, class)
sapply(v, length)

Retrieve Meta-data from eSets and ExpressionSets.

Description

These generic functions access generic data, abstracts, PubMed IDs and experiment data from instances of the eSet-class or ExpressionSet-class .

Usage

abstract(object)
pubMedIds(object)
pubMedIds(object) <- value
experimentData(object)
experimentData(object) <- value

Arguments

Value

abstract returns a character vector containing the abstract (as in a published paper) associated with object .

pubMedIds returns a character vector of PUBMED IDs associated with the experiment.

experimentData returns an object representing the description of an experiment, e.g., an object of MIAME-class

Seealso

ExpressionSet-class , eSet-class , MIAME-class

Author

Biocore

Add Menu Items to an Existing/New Menu of Window

Description

This function adds a menu item for a package's vignettes.

Usage

addVigs2WinMenu(pkgName)

Arguments

Details

The original functions addVig2Menu , addVig4Win , addVig4Unix , addNonExisting , addPDF2Vig have been replaced by addVigs2WinMenu , please use those instead.

Value

The functions do not return any value.

Author

Jianhua Zhang and Jeff Gentry

Examples

# Only works for windows now
if(interactive() && .Platform$OS.type == "windows" &&
.Platform$GUI == "Rgui"){
addVigs2WinMenu("Biobase")
}

Methods for Function annotatedDataFrameFrom in Package Biobase' ## DescriptionannotatedDataFrameFromis a convenience for creating [AnnotatedDataFrame`](#annotateddataframe) objects. ## Author Biocore team

Checks if there are any missing values in an object or not

Description

Checks if there are any missing values in an object or not.

Usage

anyMissing(x=NULL)

Arguments

Details

The implementation of this method is optimized for both speed and memory.

Value

Returns TRUE if a missing value was detected, otherwise FALSE .

Author

Henrik Bengtsson ( http://www.braju.com/R/ )

Examples

x <- rnorm(n=1000)
x[seq(300,length(x),by=100)] <- NA
stopifnot(anyMissing(x) == any(is.na(x)))

Retrieve assay data from eSets and ExpressionSets.

Description

This generic function accesses assay data stored in an object derived from the eSet or ExpressionSet class.

Usage

assayData(object)
assayData(object) <- value

Arguments

Value

assayData applied to eSet -derived classes returns a list or environment; applied to ExpressionSet , the method returns an environment. See the class documentation for specific details.

Seealso

eSet-class , ExpressionSet-class , SnpSet-class

Author

Biocore

Evaluate an expression if its value is not already cached.

Description

Cache the evaluation of an expression in the file system.

Usage

cache(expr, dir=".", prefix="tmp_R_cache_")

Arguments

Details

This function can be useful during the development of computationally intensive workflows, for example in vignettes or scripts. The function uses a cache file in dir which defaults to the current working directory whose name is obtained by paste(prefix, name, .

When cache is called and the cache file exists, it is loaded and the object whose name is given on the left of <- in expr is returned. In this case, expr is not evaluted.

When cache is called and the cache file does not exist, expr is evaluted, its value is saved into a cache file, and then its value is returned.

The expr argument must be of the form of someVar <- . That is, the left hand side must be a single symbol name and the next syntactic token must be <- .

To flush the cache and force recomputation, simply remove the cache files. You can use file.remove to do this.

Value

The (cached) value of expr .

Author

Wolfgang Huber, huber@ebi.ac.uk Seth Falcon, sfalcon@fhcrc.org

Examples

bigCalc <- function() runif(10)
cache(myComplicatedObject <- bigCalc())
aCopy <- myComplicatedObject
remove(myComplicatedObject)
cache(myComplicatedObject <- bigCalc())
stopifnot(all.equal(myComplicatedObject, aCopy))
allCacheFiles <-
list.files(".", pattern="^tmp_R_cache_.*\.RData$", full.name=TRUE)
file.remove(allCacheFiles)

Create a new ExpressionSet instance by selecting a specific channel

Description

This generic function extracts a specific element from an object, returning a instance of the ExpressionSet class.

Usage

channel(object, name, ...)

Arguments

Value

An instance of class ExpressionSet .

Author

Biocore

Examples

obj <- NChannelSet(
R=matrix(runif(100), 20, 5),
G=matrix(runif(100), 20, 5))
## G channel as ExpressionSet
channel(obj, "G")

Retrieve and set channel names from object

Description

This generic function reports or updates the channels in an object.

Usage

channelNames(object, ...)
channelNames(object, ...) <- value

Arguments

Details

channelNames returns the names of the channels in a defined order. Change the order using the replacement method with a permuation of the channel names as value . Rename channels using the replacement method with a named list, where the vector elements are a permutation of the current channels, with corresponding names the new identifier for the channel.

Value

character .

Author

Biocore

Examples

## channelNames default to alphabetical order of channels
obj <- NChannelSet(
R=matrix(runif(100), 20, 5),
G=matrix(-runif(100), 20, 5))
channelNames(obj)
channelNames(obj) <- c(Gn="G", Rd="R")   ## rename
channelNames(obj)
channelNames(obj) <- c("Rd", "Gn")       ## reorder
channelNames(obj)
all(assayData(obj)[["Gn"]] <= 0)
all(assayData(obj)[["Rd"]] >= 0)

Class Containing Measured Variables and Their Meta-Data Description.

Description

An AnnotatedDataFrame consists of two parts. There is a collection of samples and the values of variables measured on those samples. There is also a description of each variable measured. The components of an AnnotatedDataFrame can be accessed with pData and varMetadata .

Seealso

eSet , ExpressionSet , read.AnnotatedDataFrame

Author

V.J. Carey, after initial design by R. Gentleman

Examples

df <- data.frame(x=1:6,
y=rep(c("Low", "High"),3),
z=I(LETTERS[1:6]),
row.names=paste("Sample", 1:6, sep="_"))
metaData <-
data.frame(labelDescription=c(
"Numbers",
"Factor levels",
"Characters"))

AnnotatedDataFrame()
AnnotatedDataFrame(data=df)
AnnotatedDataFrame(data=df, varMetadata=metaData)
as(df, "AnnotatedDataFrame")

obj <- AnnotatedDataFrame()
pData(obj) <- df
varMetadata(obj) <- metaData
validObject(obj)

Class "AssayData"

Description

Container class defined as a class union of list and environment . Designed to contain one or more matrices of the same dimension.

Seealso

eSet-class ExpressionSet-class

Author

Biocore

Class to Contain and Describe High-Throughput Expression Level Assays.

Description

Container for high-throughput assays and experimental metadata. ExpressionSet class is derived from eSet , and requires a matrix named exprs as assayData member.

Usage

## Instance creation
ExpressionSet(assayData,
    phenoData=annotatedDataFrameFrom(assayData, byrow=FALSE),
    featureData=annotatedDataFrameFrom(assayData, byrow=TRUE),
    experimentData=MIAME(), annotation=character(),
    protocolData=annotatedDataFrameFrom(assayData, byrow=FALSE),
    ...)
## Additional methods documented below

Arguments

Seealso

eSet-class , ExpressionSet-class .

Author

Biocore team

Examples

# create an instance of ExpressionSet
ExpressionSet()

ExpressionSet(assayData=matrix(runif(1000), nrow=100, ncol=10))

# update an existing ExpressionSet
data(sample.ExpressionSet)
updateObject(sample.ExpressionSet)

# information about assay and sample data
featureNames(sample.ExpressionSet)[1:10]
sampleNames(sample.ExpressionSet)[1:5]
experimentData(sample.ExpressionSet)

# subset: first 10 genes, samples 2, 4, and 10
expressionSet <- sample.ExpressionSet[1:10,c(2,4,10)]

# named features and their expression levels
subset <- expressionSet[c("AFFX-BioC-3_at","AFFX-BioDn-5_at"),]
exprs(subset)

# samples with above-average 'score' in phenoData
highScores <- expressionSet$score > mean(expressionSet$score)
expressionSet[,highScores]

# (automatically) coerce to data.frame
lm(score~AFFX.BioDn.5_at + AFFX.BioC.3_at, data=subset)

Class for Storing Microarray Experiment Information

Description

Class MIAME covers MIAME entries that are not covered by other classes in Bioconductor. Namely, experimental design, samples, hybridizations, normalization controls, and pre-processing information. The MIAME class is derived from MIAxE .

Seealso

class:characterORMIAME , read.MIAME

Author

Rafael A. Irizarry

References

http://www.mged.org/Workgroups/MIAME/miame_1.1.html

MIAxE objects

Description

The MIAxE virtual class is a general container for storing experiment metadata. Information such as experimental design, samples, normalization methods and pre-processing information can be stored in these objets.

The MIAxE class is virtual and MIAxE objects cannot be instantiated directly. The following classes derive directly from the MIAxE class: MIAME.

Seealso

Related classes MIAME-class , ExpressionSet-class . Derived classes MIAME-class .

Author

Biocore team

Examples

# Create a new class
MyData <- setClass("MyData", contains="MIAxE")
MyData()

# MIAxE objects cannot be instantiated directly
try(new("MIAxE"))

Class to Contain and Describe High-Throughput Expression Level Assays.

Description

Container for high-throughput assays and experimental metadata. MutliSet is derived from eSet-class . MultiSet differs from ExpressionSet-class because MultiSet can contain any element(s) in assayData ( ExpressionSet must have an element named exprs ).

Seealso

eSet-class , ExpressionSet-class

Author

Biocore team

Examples

# create an instance of ExpressionSet
new("MultiSet")

Class to contain data from multiple channel array technologies

Description

Container for high-throughput assays and experimental meta-data. Data are from experiments where a single chip contains several (more than 1) different channels . All channels on a chip have the same set of features . An experiment consists of a collection of several N-channel chips; each chip is a sample .

An NChannelSet provides a way to coordinate assay data (expression values) with phenotype information and references to chip annotation data; it extends the eSet class.

An NChannelSet allows channels to be extracted (using the channels method, mentioned below), and subsets of features or samples to be selected (using [<features>, ). Selection and subsetting occur so that relevant phenotypic data is maintained by the selection or subset.

Seealso

eSet , ExpressionSet .

Author

Martin Morgan mtmorgan@fhcrc.org

Examples

## An empty NChannelSet
obj <- NChannelSet()

## An NChannelSet with two channels (R, G) and no phenotypic data
obj <- NChannelSet(R=matrix(0,10,5), G=matrix(0,10,5))

## An NChannelSet with two channels and channel-specific phenoData
R <- matrix(0, 10, 3, dimnames=list(NULL, LETTERS[1:3]))
G <- matrix(1, 10, 3, dimnames=list(NULL, LETTERS[1:3]))
assayData <- assayDataNew(R=R, G=G)
data <- data.frame(ChannelRData=numeric(ncol(R)),
ChannelGData=numeric(ncol(R)),
ChannelRAndG=numeric(ncol(R)))
varMetadata <- data.frame(labelDescription=c(
"R-specific phenoData",
"G-specific phenoData",
"Both channel phenoData"),
channel=factor(c("R", "G", "_ALL_")))
phenoData <- AnnotatedDataFrame(data=data, varMetadata=varMetadata)
obj <- NChannelSet(assayData=assayData, phenoData=phenoData)
obj

## G channel as NChannelSet
selectChannels(obj, "G")

## G channel as ExpressionSet
channel(obj, "G")

## Samples "A" and "C"
obj[,c("A", "C")]

Class to Contain Objects Describing High-Throughput SNP Assays.

Description

Container for high-throughput assays and experimental metadata. SnpSet class is derived from eSet , and requires matrices call , callProbability as assay data members.

Seealso

eSet , ExpressionSet

Author

Martin Morgan, V.J. Carey, after initial design by R. Gentleman

Retrieve information about versioned classes

Description

These generic functions return version information for classes derived from Versioned-class , or VersionsNull-class for unversioned objects. The version information is an object of Versions-class .

By default, classVersion has the following behaviors: list(" ", " ", list(list(list("classVersion(Versioned-instance)")), list("Returns a ", list("Versions-class"), " object obtained from the object.")), " ", " ", list(list(list("classVersion{"class"}")), list("Consults the definition of ", list("class"), " and return the current version information, if available.")), " ", " ", list(list(list("classVersion(ANY)")), list("Return a ", list(list("VersionsNull-class")), " object to indicate no version information available.")), " ", " ")

By default, the classVersion<- method has the following behavior: list(" ", " ", list(list(list("classVersion(Versioned-instance)["id"] <- value")), list("Assign (update or add) ", list("value"), " to ", list("Versions-instance"), ". ", list("value"), " is coerced to a valid version description. see ", list(list("Versions-class")), " for additional access methods.")), " ", " ")

Usage

classVersion(object)
classVersion(object) <- value

Arguments

Value

classVersion returns an instance of Versions-class

Seealso

Versions-class

Author

Biocore team

Examples

obj <- new("VersionedBiobase")

classVersion(obj)
classVersion(obj)["Biobase"]
classVersion(1:10) # no version
classVersion("ExpressionSet") # consult ExpressionSet prototype

classVersion(obj)["MyVersion"] <- "1.0.0"
classVersion(obj)

Class "Versioned"

Description

Use this class as a superclass' for classes requiring information about versions. ## Seealso [Versions-class`](#versions-class) ## Author Biocore ## Examples r obj <- new("Versioned", versions=list(A="1.0.0")) obj classVersion(obj) A <- setClass("A", contains="Versioned") classVersion("A") a <- A() a # 'show' nothing by default classVersion(a) B <- setClass("B", contains="Versioned", prototype=prototype(new("Versioned",versions=list(B="1.0.0")))) classVersion("B") b <- B() classVersion(b) classVersion(b)["B"] <- "1.0.1" classVersion(b) classVersion("B") classVersion("B") < classVersion(b) classVersion(b) == "1.0.1" C <- setClass("C", representation(x="numeric"), contains=("VersionedBiobase"), prototype=prototype(new("VersionedBiobase", versions=c(C="1.0.1")))) setMethod("show", signature(object="C"), function(object) print(object@x)) c <- C(x=1:10) c classVersion(c)

Class "VersionedBiobase"

Description

Use this class as a superclass' for classes requiring information about versions. By default, the class contains versions for R and Biobase. See [Versioned-class](#versioned-class) for additional details. ## Seealso [Versioned-class`](#versioned-class) ## Author Biocore ## Examples r obj <- new("VersionedBiobase") classVersion(obj) obj <- new("VersionedBiobase", versions=list(A="1.0.0")) classVersion(obj) A <- setClass("A", contains="VersionedBiobase") classVersion("A") a <- A() classVersion(a) obj <- new("VersionedBiobase", versions=c(MyVersion="1.0.0")) classVersion(obj) B <- setClass("B", contains="VersionedBiobase", prototype=prototype(new("VersionedBiobase",versions=list(B="1.0.0")))) classVersion("B") b <- B() classVersion(b) removeClass("A") removeClass("B")

Class "Versions"

Description

A class to record version number information. This class is used to report versions; to add version information to your own class, use Versioned-class .

Seealso

classVersion isCurrent isVersioned

Author

Biocore

Examples

obj <- new("Versions", A="1.0.0")
obj

obj["A"] <- "1.0.1"
obj
obj["B"] <- "2.0"
obj

obj1 <- obj
obj1["B"] <- "2.0.1"

obj1 == obj
obj1["B"] > "2.0.0"
obj["B"] == "2.0" # TRUE!

Class "VersionsNull"

Description

A class used to represent the version' of unversioned objects. Useful primarily for method dispatch. ## Seealso [classVersion`](#classversion) ## Author Biocore ## Examples r obj <- new("VersionsNull") obj obj <- new("VersionsNull", A="1.0.0") # warning obj

A Simple Class for Aggregators

Description

A class of objects designed to help aggregate calculations over an iterative computation. The aggregator consists of three objects. An environment to hold the values. A function that sets up an initial value the first time an object is seen. An aggregate function that increments the value of an object seen previously.

Details

This class is used to help aggregate different values over function calls. A very simple example is to use leave one out cross-validation for prediction. At each stage we first perform feature selection and then cross-validate. To keep track of how often each feature is selected we can use an aggregator. At the end of the cross-validation we can extract the names of the features chosen from aggenv .

Seealso

Aggregate

Class to Make Older Versions Compatible

Description

This class can be either character or MIAME .

Seealso

See also MIAME

A Lockable List Structure with Constraints on Content

Description

Container class that specializes the list construct of R to provide content and access control

Examples

x1 <- new("container", x=vector("list", length=3), content="lm")
lm1 <- lm(rnorm(10)~runif(10))
x1[[1]] <- lm1

Class to Contain High-Throughput Assays and Experimental Metadata

Description

Container for high-throughput assays and experimental metadata. Classes derived from eSet contain one or more identical-sized matrices as assayData elements. Derived classes (e.g., ExpressionSet-class , SnpSet-class ) specify which elements must be present in the assayData slot.

eSet object cannot be instantiated directly; see the examples for usage.

Seealso

Method use in ExpressionSet-class . Related classes AssayData-class , AnnotatedDataFrame-class , MIAME-class . Derived classes ExpressionSet-class , SnpSet-class . To update objects from previous class versions, see updateOldESet .

Author

Biocore team

Examples

# update previous eSet-like class oldESet to existing derived class
updateOldESet(oldESet, "ExpressionSet")

# create a new, ad hoc, class, for personal use
# all methods outlined above are available automatically
.MySet <- setClass("MySet", contains="eSet")
.MySet()

# Create a more robust class, with constructor and validation methods
# to ensure assayData contains specific matricies
.TwoColorSet <- setClass("TwoColorSet", contains="eSet")

TwoColorSet <-
function(phenoData=AnnotatedDataFrame(), experimentData=MIAME(),
annotation=character(), R=new("matrix"), G=new("matrix"),
Rb=new("matrix"), Gb=new("matrix"), ...)
{
.TwoColorSet(phenoData=phenoData, experimentData=experimentData,
annotation=annotation, R=R, G=G, Rb=Rb, Gb=Gb, ...)
}

setValidity("TwoColorSet", function(object) {
assayDataValidMembers(assayData(object), c("R", "G", "Rb", "Gb"))
})

TwoColorSet()

# eSet objects cannot be instantiated directly, only derived objects
try(new("eSet"))

removeClass("MySet")
removeClass("TwoColorSet")

Function to retrieve contents of environments

Description

The contents method is used to retrieve the values stored in an environment.

Usage

contents(object, all.names)

Arguments

Value

A named list is returned, where the elements are the objects stored in the environment. The names of the elements are the names of the objects.

The all.names argument is identical to the one used in as.list.environment .

Seealso

as.list.environment

Author

R. Gentleman

Examples

z <- new.env()
multiassign(letters, 1:26, envir=z)
contents(z)

List-Environment interactions

Description

These functions can be used to make copies of environments, or to get/assign all of the objects inside of an environment.

Usage

copyEnv(oldEnv, newEnv, all.names=FALSE)

Arguments

Details

copyEnv : This function will make a copy of the contents from oldEnv and place them into newEnv .

Seealso

environment , as.list

Author

Jeff Gentry and R. Gentleman

Examples

z <- new.env(hash=TRUE, parent=emptyenv(), size=29L)
multiassign(c("a","b","c"), c(1,2,3), z)

a <- copyEnv(z)
ls(a)

Copy Between Connections or Files with Configure-Like Name-Value Substitution

Description

Copy files, directory trees or between connections and replace all occurences of a symbol by the corresponding value.

Usage

copySubstitute(src, dest, symbolValues, symbolDelimiter="@", allowUnresolvedSymbols=FALSE,
               recursive = FALSE, removeExtension = "\.in$")

Arguments

Details

Symbol substitution: this is best explained with an example. If the list symbolValues contains an element with name FOO and value bar , and symbolDelimiter is @ , then any occurrence of @FOO@ is replaced by bar . This applies both the text contents of the files in src as well as to the filenames. See examples.

If recursive is FALSE , both src and dest must be connection or a filenames. The text in src is read through the function readLines , symbols are replaced by their values, and the result is written to dest through the function writeLines .

If recursive is TRUE , copySubstitute works recursively down a directory tree (see details and example). src must be a character vector with multiple filenames or directory names, dest a directory name.

One use of this function is in createPackage for the automatic generation of packages from a template package directory.

Value

None. The function is called for its side effect.

Author

Wolfgang Huber http://www.dkfz.de/mga/whuber

Examples

## create an example file
infile  = tempfile()
outfile = tempfile()

writeLines(text=c("We will perform in @WHAT@:",
"So, thanks to @WHOM@ at once and to each one,",
"Whom we invite to see us crown'd at @WHERE@."),
con = infile)

## create the symbol table
z = list(WHAT="measure, time and place", WHOM="all", WHERE="Scone")

## run copySubstitute
copySubstitute(infile, outfile, z)

## display the results
readLines(outfile)



##--------------------------------------------------------------
## This is a slightly more complicated example that demonstrates
## how copySubstitute works on nested directories
##--------------------------------------------------------------
d = tempdir()
my.dir.create = function(x) {dir.create(x); return(x)}

unlink(file.path(d, "src"), recursive=TRUE)
unlink(file.path(d, "dest"), recursive=TRUE)

## create some directories and files:
src  = my.dir.create(file.path(d, "src"))
dest = file.path(d, "dest")
d1   = my.dir.create(file.path(src, "dir1.in"))
d2   = my.dir.create(file.path(src, "dir2@FOO@.in"))
d3   = my.dir.create(file.path(d2, "dir3"))
d4   = my.dir.create(file.path(d3, "dir4"))
d5   = my.dir.create(file.path(d4, "dir5@BAR@"))
writeLines(c("File1:", "FOO: @FOO@"),     file.path(d1, "file1.txt.in"))
writeLines(c("File2:", "BAR: @BAR@"),     file.path(d2, "file2.txt.in"))
writeLines(c("File3:", "SUN: @SUN@"),     file.path(d3, "file3.txt.in"))
writeLines(c("File4:", "MOON: @MOON@"),   file.path(d4, "@SUN@.txt"))

## call copySubstitute
copySubstitute(src, dest, recursive=TRUE,
symbolValues = list(FOO="thefoo", BAR="thebar",
SUN="thesun", MOON="themoon"))

## view the result
listsrc  = dir(src,  full.names=TRUE, recursive=TRUE)
listdest = dir(dest, full.names=TRUE, recursive=TRUE)
listsrc
listdest

cat(unlist(lapply(listsrc,  readLines)), sep="
")
cat(unlist(lapply(listdest, readLines)), sep="
")

Create a Package Directory from a Template

Description

Create a package directory from a template, with symbol-value substitution

Usage

createPackage(pkgname, destinationDir, originDir, symbolValues, unlink=FALSE, quiet=FALSE)

Arguments

Details

The intended use of this function is for the automated mass production of data packages, such as the microarray annotation, CDF, and probe sequence packages.

No syntactic or other checking of the package is performed. For this, use R CMD check.

The symbols @PKGNAME@ and @DATE@ are automatically defined with the values of pkgname and date() , respectively.

Value

The function returns a list with one element pkgdir : the path to the package.

Seealso

copySubstitute , the reference manual Writing R extensions .

Author

Wolfgang Huber http://www.dkfz.de/mga/whuber

Examples

sym  = list(AUTHOR = "Hesiod", VERSION = "1.0",
TITLE = "the nine muses",
FORMAT = "Character vector containg the names of the 9 muses.")

res  = createPackage("muses",
destinationDir = tempdir(),
originDir      = system.file("Code", package="Biobase"),
symbolValues   = sym,
unlink = TRUE, quiet = FALSE)

muses = c("Calliope", "Clio", "Erato", "Euterpe", "Melpomene",
"Polyhymnia", "Terpsichore", "Thalia", "Urania")

dir.create(file.path(res$pkgdir, "data"))

save(muses, file = file.path(res$pkgdir, "data", "muses.rda"))

res$pkgdir

Dataset: Names and Characteristics of Amino Acids

Description

The aaMap data frame has 20 rows and 6 columns. Includes elementary information about amino acids.

Format

This data frame contains the following columns: list(" ", " ", list(list("name"), list("amino acid name")), " ", " ", list(list("let.1"), list("one-letter code")), " ", " ", list(list("let.3"), list("three-letter code")), " ", " ", list(list("scProp"), list("side chain property at pH 7 (polar/nonpolar)")), " ", " ", list(list("hyPhilic"), list("logical: side chain is hydrophilic at pH 7")), " ", " ", list(list("acidic"), list("logical: side chain is acidic at pH 7")), " ", " ")

Usage

data(aaMap)

Examples

data(aaMap)

Sample expression matrix and phenotype data.frames.

Description

The geneData data.frame has 500 rows and 26 columns. It consists of a subset of real expression data from an Affymetrix U95v2 chip. The data are anonymous. The covariate data geneCov and geneCovariate are made up. The standard error data seD is also made up.

Format

A 500 by 26 data frame.

Usage

data(geneData)

Examples

data(geneData)
data(geneCovariate)
data(seD)

Example data.frame representing reporter information

Description

The reporter object is a 500 by 1 data frame. The rows represent the 500 probe IDs in the geneData data. The values in reporter are the predefined probe types for the probes. reporter is used in conjunction with the geneData object and its associates.

Format

A 500 by 1 data frame

Usage

data(reporter)

Details

There are 10 predefined probe types:

• AFFX- Quality Control (QC)

• _f_ SequenceFamily

• _g_ CommonGroups

• _s_ SimilarityConstraint

• _r_ RulesDropped

• _i_ Incomplete

• _b_ AmbiguousProbeSet

• _l_ LongProbeSet

• _at AntiSenseTarget

• _st SenseTarget

Examples

data(reporter)
## maybe str(reporter) ; plot(reporter) ...

Dataset of class 'ExpressionSet'

Description

The expression data are real but anonymized. The data are from an experiment that used Affymetrix U95v2 chips. The data were processed by dChip and then exported to R for analysis.

The data illustrate ExpressionSet-class , with assayData containing the required matrix element exprs and an additional matrix se.exprs . se.exprs has the same dimensions as exprs .

The phenoData and standard error estimates (se.exprs) are made up. The information in the "description" slot is fake.

Format

The data for 26 cases, labeled A to Z and 500 genes. Each case has three covariates: sex (male/female); type (case/control); and score (testing score).

Usage

data(sample.ExpressionSet)

Examples

data(sample.ExpressionSet)

Data set of class 'MultiSet'

Description

The expression data are real but anonymized. The data are from an experiment that used Affymetrix U95v2 chips. The data were processed by dChip and then exported to R for analysis.

The phenoData, standard error estimates, and description data are fake.

Format

The data for 4 cases, labeled a to d and 500 genes. Each case has five covariates: SlideNumber: number; FileName: name; Cy3: genotype labeled Cy3; Cy5: genotype labeled Cy5; Date: date.

Usage

data(sample.MultiSet)

Examples

data(sample.MultiSet)

Biobase Deprecated and Defunct

Description

The function, class, or data object you have asked for has been deprecated or made defunct.

Retrieve and set overall experimental information eSet-like classes.

Description

These generic functions access experimental information associated with eSet-class .

Usage

description(object, list())
description(object) <- value

Arguments

Value

description returns an object of MIAME-class .

Seealso

eSet-class , MIAME-class

Author

Biocore

Dump Textual Description of a Package

Description

Dump textual description of a package

Usage

dumpPackTxt(package)

Arguments

Details

dumps DESCRIPTION and INDEX files from package sources

Value

stdout output

Note

Other approaches using formatDL are feasible

Author

stvjc@channing.harvard.edu

Examples

dumpPackTxt("stats")

An apply-like function for ExpressionSet and related structures.

Description

esApply is a wrapper to apply for use with ExpressionSet s. The application of a function to rows of an expression array usually involves variables in pData . esApply uses a special evaluation paradigm to make this easy. The function FUN may reference any data in pData by name.

Usage

esApply(X, MARGIN, FUN, list())

Arguments

Details

The pData from X is installed in an environment. This environment is installed as the environment of FUN . This will then provide bindings for any symbols in FUN that are the same as the names of the pData of X . If FUN has an environment already it is retained but placed after the newly created environment. Some variable shadowing could occur under these circumstances.

Value

The result of with(pData(x), apply(exprs(X), MARGIN, FUN, ...)) .

Seealso

apply , ExpressionSet

Author

V.J. Carey stvjc@channing.harvard.edu, R. Gentleman

Examples

data(sample.ExpressionSet)
## sum columns of exprs
res <- esApply(sample.ExpressionSet, 1, sum)

## t-test, spliting samples by 'sex'
f <- function(x) {
xx <- split(x, sex)
t.test(xx[[1]], xx[[2]])$p.value
}
res <- esApply(sample.ExpressionSet, 1, f)

## same, but using a variable passed in the function call

f <- function(x, s) {
xx <- split(x, s)
mean(xx[[1]]) - mean(xx[[2]])
}
sex <- sample.ExpressionSet[["sex"]]
res <- esApply(sample.ExpressionSet, 1, f, s = sex)

# obtain the p-value of the t-test for sex difference
mytt.demo <- function(y) {
ys <- split(y, sex)
t.test(ys[[1]], ys[[2]])$p.value
}
sexPValue <- esApply(sample.ExpressionSet, 1, mytt.demo)

# obtain the p-value of the slope associated with score, adjusting for sex
# (if we were concerned with sign we could save the z statistic instead at coef[3,3]
myreg.demo <- function(y) {
summary(lm(y ~ sex + score))$coef[3,4]
}
scorePValue <- esApply(sample.ExpressionSet, 1, myreg.demo)

# a resampling method
resamp <- function(ESET) {
ntiss <- ncol(exprs(ESET))
newind <- sample(1:ntiss, size = ntiss, replace = TRUE)
ESET[newind,]
}

# a filter
q3g100filt <- function(eset) {
apply(exprs(eset), 1, function(x) quantile(x,.75) > 100)
}

# filter after resampling and then apply
set.seed(123)
rest <- esApply({bool <- q3g100filt(resamp(sample.ExpressionSet)); sample.ExpressionSet[bool,]},
1, mytt.demo)

Retrieve expression data from eSets.

Description

These generic functions access the expression and error measurements of assay data stored in an object derived from the eSet-class .

Usage

exprs(object)
exprs(object) <- value
se.exprs(object)
se.exprs(object) <- value

Arguments

Value

exprs returns a (usually large!) matrix of expression values; se.exprs returns the corresponding matrix of standard errors, when available.

Seealso

eSet-class , ExpressionSet-class , SnpSet-class

Author

Biocore

Retrieve information on features recorded in eSet-derived classes.

Description

These generic functions access feature data (experiment specific information about features) and feature meta-data (e.g., descriptions of feature covariates).

Usage

featureData(object)
featureData(object) <- value
fData(object)
fData(object) <- value
fvarLabels(object)
fvarLabels(object) <- value
fvarMetadata(object)
fvarMetadata(object) <- value

Arguments

Value

featureData returns an object containing information on both variable values and variable meta-data. fvarLabels returns a character vector of measured variable names. fData returns a data frame with features as rows, variables as columns. fvarMetadata returns a data frame with variable names as rows, description tags (e.g., unit of measurement) as columns.

Seealso

eSet , ExpressionSet

Author

Biocore

Retrieve feature and sample names from eSets.

Description

These generic functions access the feature names (typically, gene or SNP identifiers) and sample names stored in an object derived from the eSet-class .

Usage

featureNames(object)
featureNames(object) <- value
sampleNames(object)
sampleNames(object) <- value

Arguments

Value

featureNames returns a (usually long!) character vector uniquely identifying each feature. sampleNames returns a (usually shorter) character vector identifying samples.

Seealso

ExpressionSet-class , SnpSet-class

Author

Biocore

List Vignette Files for a Package

Description

This function will return a listing of all vignettes stored in a package's doc directory.

Usage

getPkgVigs(package = NULL)

Arguments

Value

A data.frame with columns package , filename , title .

Seealso

openVignette

Author

Jeff Gentry, modifications by Wolfgang Huber.

Examples

z <- getPkgVigs()
z # and look at them

Internals

Description

Use help.search("your keyword", package="Biobase").

Use version information to test whether class is current

Description

This generic function uses Versioned-class information to ask whether an instance of a class (e.g., read from disk) has current version information.

By default, isCurrent has the following behaviors: list(" ", " ", list(list(list("isCurrent(Versioned-instance)")), list("Returns a vector of ", " logicals, indicating whether each version matches the current version ", " from the class prototype.")), " ", " ", list(list(list("isCurrent(ANY)")), list("Return ", list("NA"), ", indicating that the ", " version cannot be determined")), " ", " ", list(list(list("isCurrent(Versioned-instance, "class")")), list("Returns a ", " logical vector indicating whether version identifiers shared between ",

"     ", list("Versioned-instance"), " and ", list(""class""), " are current.")), "

", " ")

Starting with R-2.6 / Bioconductor 2.1 / Biobase 1.15.1, isCurrent(Versioned-instance, ...) returns an element S4 indicating whether the class has the S4' bit set; a value ofFALSEindicates that the object needs to be recreated. ## Usage ```r isCurrent(object, value) ``` ## Arguments |Argument |Description| |------------- |----------------| |object| Object whose version is to be determined, as described above.| |value| (Optional) character string identifying a class with which to compare versions.| ## ValueisCurrentreturns a logical vector. ## Seealso [Versions-class`](#versions-class) ## Author Biocore team ## Examples r obj <- new("VersionedBiobase") isCurrent(obj) isCurrent(1:10) # NA A <- setClass("A", contains="VersionedBiobase", prototype=prototype(new("VersionedBiobase", versions=c(A="1.0.0")))) a <- A() classVersion(a) isCurrent(a, "VersionedBiobase") # is the 'VersionedBiobase' portion current? classVersion(a)["A"] <- "1.0.1" classVersion(a) isCurrent(a, "VersionedBiobase") isCurrent(a) # more recent, so does not match 'current' defined by prototype removeClass("A")

Determine Unique Elements

Description

Determines which elements of a vector occur exactly once.

Usage

isUnique(x)

Arguments

Value

A logical vector of the same length as x , in which TRUE indicates uniqueness.

Seealso

unique , duplicated .

Author

Wolfgang Huber

Examples

x <- c(9:20, 1:5, 3:7, 0:8)
isUnique(x)

Determine whether object or class contains versioning information

Description

This generic function checks to see whether Versioned-class information is present. When the argument to isVersioned is a character string, the prototype of the class corresponding to the string is consulted.

By default, isVersioned has the following behaviors: list(" ", " ", list(list(list("isVersioned(Versioned-instance)")), list("Returns ", list("TRUE"), " when the instance have version information.")), " ", " ", list(list(list("isCurrent("class-name")")), list("Returns ", list("TRUE"), " when the named class extends ", list(list("Versioned-class")), ".")), " ", " ", list(list(list("isVersioned(ANY)")), list("Returns ", list("FALSE"))), " ", " ")

Usage

isVersioned(object)

Arguments

Value

isVersioned returns a logical indicating whether version information is present.

Seealso

Versions-class

Author

Biocore team

Examples

obj <- new("VersionedBiobase")
isVersioned(obj)

isVersioned(1:10) # FALSE

A <- setClass("A", contains="VersionedBiobase",
prototype=prototype(new("VersionedBiobase", versions=c(A="1.0.0"))))
a <- A()
isVersioned(a)

removeClass("A")

Compute the longest common prefix or suffix of a string

Description

These functions find the longest common prefix or suffix among the strings in a character vector.

Usage

lcPrefix(x, ignore.case=FALSE)
lcPrefixC(x, ignore.case=FALSE)
lcSuffix(x, ignore.case=FALSE)

Arguments

Details

Computing the longest common suffix is helpful for truncating names of objects, like microarrays, that often have a common suffix, such as .CEL.

There are some potential problems with the approach used if multibyte character encodings are being used.

lcPrefixC is a faster implementation in C. It only handles ascii characters.

Value

The common prefix or suffix.

Seealso

nchar , nchar

Author

R. Gentleman

Examples

s1 <- c("ABC.CEL", "DEF.CEL")
lcSuffix(s1)

s2 <- c("ABC.123", "ABC.456")
lcPrefix(s2)


CHK <- stopifnot

CHK(".CEL" == lcSuffix(s1))
CHK("bc" == lcSuffix(c("abc", "333abc", "bc")))
CHK("c" == lcSuffix(c("c", "abc", "xxxc")))
CHK("" == lcSuffix(c("c", "abc", "xxx")))

CHK("ABC." == lcPrefix(s2))
CHK("ab" == lcPrefix(c("abcd", "abcd123", "ab", "abc", "abc333333")))
CHK("a" == lcPrefix(c("abcd", "abcd123", "ax")))
CHK("a" == lcPrefix(c("a", "abcd123", "ax")))
CHK("" == lcPrefix(c("a", "abc", "xxx")))

CHK("ab" == lcPrefixC(c("abcd", "abcd123", "ab", "abc", "abc333333")))
CHK("a" == lcPrefixC(c("abcd", "abcd123", "ax")))
CHK("a" == lcPrefixC(c("a", "abcd123", "ax")))
CHK("" == lcPrefixC(c("a", "abc", "xxx")))

Lengths of list elements

Description

This function returns an integer vector with the length of the elements of its argument, which is expected to be a list.

Usage

listLen(x)

Arguments

Details

This function returns a vector of the same length as the list x containing the lengths of each element.

The current implementation is intended for lists containing vectors and the C-level length function is used to determine length. This means no dispatch is done for the elements of the list. If your list contains S4 objects, you should use sapply(x, length) instead.

Seealso

sapply

Author

Jeff Gentry and R. Gentleman

Examples

foo = lapply(1:8, rnorm)
listLen(foo)

Make an R package from a data object

Description

This generic creates a valid R package from an R data object.

Usage

makeDataPackage(object, author, email,
                packageName=deparse(substitute(object)),
                packageVersion=package_version("1.0.0"),
                license="Artistic-2.0",
                biocViews="ExperimentData",
                filePath=tempdir(),
                list())

Arguments

Details

The function makes use of various tools in R and Bioconductor to automatically generate the source files for a valid R package.

Value

The return value is that from a call to link{createPackage} which is invoked once the default arguments are set up. The data instance is stored in the data directory with a name the same as that of the resulting package.

Seealso

createPackage

Note

Developers implementing derived methods might force correct package name evaluation by including 'packageName' in any callNextMethod() .

Author

R. Gentleman

Examples

data(sample.ExpressionSet)
## package created in tempdir()
s1 <- makeDataPackage(sample.ExpressionSet,
author = "Foo Author",
email = "foo@bar",
packageName = "FooBarPkg",
packageVersion = "1.0.0")

Nearest neighbor search.

Description

Find the nearest neighbors of a set of query points in the same or another set of points in an n-dimensional real vector space, using the Euclidean distance.

Usage

matchpt(x, y)

Arguments

Details

If y is provided, the function searches for each point in x its nearest neighbor in y . If y is missing, it searches for each point in x its nearest neighbor in x , excluding that point itself. In the case of ties, only the neighbor with the smaller index is given.

The implementation is simple and of complexity nrow(x) times nrow(y) . For larger problems, please consider one of the many more efficient nearest neighbor search algorithms.

Value

A data.frame with two columns and nrow(x) rows. The first column is the index of the nearest neighbor, the second column the distance to the nearest neighbor. If y was given, the index is a row number in y , otherwise, in x . The row names of the result are those of x .

Author

Oleg Sklyar osklyar@ebi.ac.uk

Examples

a <- matrix(c(2,2,3,5,1,8,-1,4,5,6), ncol=2L, nrow=5L)
rownames(a) = LETTERS[seq_len(nrow(a))]
matchpt(a)
b <- c(1,2,4,5,6)
d <- c(5.3, 3.2, 8.9, 1.3, 5.6, -6, 4.45, 3.32)
matchpt(b, d)
matchpt(d, b)

Assign Values to a Names

Description

Assign values to names in an environment.

Usage

multiassign(x, value, envir = parent.frame(), inherits=FALSE)

Arguments

Details

The pos argument can specify the environment in which to assign the object in any of several ways: as an integer (the position in the search list); as the character string name of an element in the search list; or as an environment (including using sys.frame to access the currently active function calls). The envir argument is an alternative way to specify an environment, but is primarily there for back compatibility.

If value is missing and x has names then the values in each element of x are assigned to the names of x .

Value

This function is invoked for its side effect, which is assigning the values to the variables in x . If no envir is specified, then the assignment takes place in the currently active environment.

If inherits is TRUE , enclosing environments of the supplied environment are searched until the variable x is encountered. The value is then assigned in the environment in which the variable is encountered. If the symbol is not encountered then assignment takes place in the user's workspace (the global environment).

If inherits is FALSE , assignment takes place in the initial frame of envir .

Examples

#-- Create objects  'r1', 'r2', ... 'r6' --
nam <- paste("r",1:6, sep=".")

multiassign(nam, 11:16)
ls(pat="^r..$")

#assign the values in y to variables with the names from y

y<-list(a=4,d=mean,c="aaa")
multiassign(y)

Informational Messages

Description

Generates an informational message that corresponds to its argument(s). Similar to warning() except prefaced by "Note:" instead of "Warning message:".

Usage

note(...)

Arguments

Details

This function essentially cat()'s the created string to the screen. It is intended for messages to the user that are deemed to be 'informational', as opposed to warnings, etc.

Seealso

warning , stop

Author

Jeff Gentry

Examples

note("This is an example of a note")

Retrieve and set eSet notes.

Description

These generic functions access notes (unstructured descriptive data) associated eSet-class .

notes(<ExpressionSet>) <- <character> is unusual, in that the character vector is appended to the list of notes; use notes(<ExpressionSet>) <- <list> to entirely replace the list.

Usage

notes(object)
notes(object) <- value

Arguments

Value

notes returns a list.

Seealso

ExpressionSet-class , SnpSet-class

Author

Biocore

Open PDF Files in a Standard Viewer

Description

Displays the specified PDF file.

Usage

openPDF(file, bg=TRUE)

Arguments

Details

Currently this function works on Windows and Unix platforms. Under Windows, whatever program is associated with the file extension will be used. Under Unix, the function will use the program named in the % option "pdfViewer" (default is 'acroread'). -- the code checks pdfviewer! % and that's the option that options() documents. option "pdfviewer" (see help(options) for information on how this is set.)

The bg argument is only interpreted on Unix.

Value

This function is executed for its side effects. The specified PDF file is opened in the PDF viewer and TRUE is returned.

Author

Jeff Gentry

Examples

openPDF("annotate.pdf")

Open a Vignette or Show Vignette Selection Menu

Description

Using the data returned by vignette this function provides a simple easy to use interface for opening vignettes.

Usage

openVignette(package=NULL)

Arguments

Details

If package is NULL then all packages are scanned for vignettes. The list of vignettes is presented to the user via the menu command. The user may select one of the vignettes to be opened in a PDF viewer.

Value

No value is returned; this function is run entirely for the side effect of opening the pdf document in the PDF viewer.

Seealso

vignette , openPDF , menu , getPkgVigs

Author

R. Gentleman

Examples

if( interactive() )
openVignette("Biobase")

Report Version of a Package

Description

Will report the version number of a requested installed package

Usage

package.version(pkg, lib.loc = NULL)

Arguments

Details

This function is a convenience wrapper around package.description , and will report simply the version number of the requested package. If the package does not exist or if the DESCRIPTION file can not be read, then an error will be thrown.

Value

A character string reporting the version number.

Seealso

package.description

Author

Jeff Gentry

Examples

package.version("Biobase")

Retrieve information on experimental phenotypes recorded in eSet and ExpressionSet-derived classes.

Description

These generic functions access the phenotypic data (e.g., covariates) and meta-data (e.g., descriptions of covariates) associated with an experiment.

Usage

phenoData(object)
phenoData(object) <- value
varLabels(object)
varLabels(object) <- value
varMetadata(object)
varMetadata(object) <- value
pData(object)
pData(object) <- value

Arguments

Value

phenoData returns an object containing information on both variable values and variable meta-data. varLabels returns a character vector of measured variables. pData returns a data frame with samples as rows, variables as columns. varMetadata returns a data frame with variable names as rows, description tags (e.g., unit of measurement) as columns.

Seealso

eSet-class , ExpressionSet-class , SnpSet-class

Author

Biocore

Protocol Metadata

Description

This generic function handles methods for adding and retrieving protocol metadata for the samples in eSets.

Usage

protocolData(object)
protocolData(object) <- value

Arguments

Value

protocolData(object) returns an AnnotatedDataFrame containing the protocol metadata for the samples.

Seealso

phenoData , AnnotatedDataFrame-class , eSet-class , ExpressionSet-class , SnpSet-class

Author

Biocore

Read and write 'AnnotatedDataFrame'

Description

Create an instance of class AnnotatedDataFrame by reading a file, or save an AnnotatedDataFrame to a file.

Usage

read.AnnotatedDataFrame(filename, path,
     sep = "    ", header = TRUE, quote = "", stringsAsFactors = FALSE, 
     row.names = 1L,
     varMetadata.char="#",
     widget = getOption("BioC")$Base$use.widgets,
     sampleNames = character(0), ...) 
write.AnnotatedDataFrame(x, file="", varMetadata.char="#", ...,
     append=FALSE, fileEncoding="")

Arguments

Details

The function read.table is used to read pData . The argument varMetadata.char is passed on to that function as its argument comment.char . Lines beginning with varMetadata.char are expected to contain further information on the column headers of pData . The format is of the form: # variable: textual explanation of the (assuming that # is the value of varMetadata.char ). See also examples.

write.AnnotatedDataFrame outputs varLabels and varMetadata(x)$labelDescription as commented header lines, and pData(x) as a with write.table .

Value

read.AnnotatedDataFrame : An instance of class AnnotatedDataFrame

write.AnnotatedDataFrame : NULL , invisibly.

Seealso

AnnotatedDataFrame for additional methods, read.table for details of reading in phenotypic data

Author

Martin Morgan mtmorgan@fhcrc.org and Wolfgang Huber, based on read.phenoData by Rafael A. Irizarry.

Examples

exampleFile = system.file("extdata", "pData.txt", package="Biobase")

adf <- read.AnnotatedDataFrame(exampleFile)
adf
head(pData(adf))
head(noquote(readLines(exampleFile)), 11)

write.AnnotatedDataFrame(adf)           # write to console by default

Read 'ExpressionSet'

Description

Create an instance of class ExpressionSet by reading data from files. widget functionality is not implemented for readExpressionSet.

Usage

readExpressionSet(exprsFile,
      phenoDataFile,
      experimentDataFile,
      notesFile,
      path,
      annotation,
      ## arguments to read.* methods 
      exprsArgs=list(sep=sep, header=header, row.names=row.names,
        quote=quote, ...),
      phenoDataArgs=list(sep=sep, header=header, row.names=row.names,
        quote=quote, stringsAsFactors=stringsAsFactors, ...),
      experimentDataArgs=list(sep=sep, header=header,
        row.names=row.names, quote=quote,
        stringsAsFactors=stringsAsFactors, ...),
      sep = "   ", header = TRUE, quote = "", stringsAsFactors = FALSE,
      row.names = 1L,
      ## widget
      widget = getOption("BioC")$Base$use.widgets,
      ...)

Arguments

Details

Expression values are read using the read.table function. Phenotypic data are read using the read.AnnotatedDataFrame function. Experiment data are read using the read.MIAME function. Notes are read using the readLines function. The return value must be a valid ExpressionSet . Only the exprsFile argument is required.

Value

An instance of the ExpressionSet class.

Seealso

ExpressionSet for additional methods.

Author

Martin Morgan mtmorgan@fhcrc.org

Examples

exprsFile = system.file("extdata", "exprsData.txt", package="Biobase")
phenoFile = system.file("extdata", "pData.txt", package="Biobase")

## Read ExpressionSet with appropriate parameters
obj = readExpressionSet(exprsFile, phenoFile, sep = "   ", header=TRUE)
obj

Read MIAME Information into an Instance of Class 'MIAME'

Description

Reads MIAME information from a file or using a widget.

Usage

read.MIAME(filename = NULL, widget = getOption("BioC")$Base$use.widgets, ...)

Arguments

Details

Notice that the MIAME class tries to cover the MIAME entries that are not covered by other classes in Bioconductor. Namely, experimental design, samples, hybridizations, normalization controls, and pre-processing information.

The function scan is used to read. The file must be a flat file with the different entries for the instance of MIAME class separated by carriage returns. The order should be: name, lab, contact, title, abstract, and url.

Alternatively a widget can be used.

Value

An object of class MIAME .

Seealso

MIAME , tkMIAME

Author

Rafael Irizarry rafa@jhu.edu

Examples

miame <- read.MIAME(widget=FALSE) ##creates an empty instance
show(miame)

A function to reverse the role of names and values in a list.

Description

Given a list with names x and values in a set y this function returns a list with names in y and values in x .

Usage

reverseSplit(inList)

Arguments

Details

First the list is unrolled to provide a two long vectors, names are repeated, once for each of their values. Then the names are split by the values.

This turns out to be useful for inverting mappings between one set of identifiers and an other.

Value

A list with length equal to the number of distinct values in the input list and values from the names of the input list.

Seealso

split

Author

R. Gentleman

Examples

l1 = list(a=1:4, b=c(2,3), d=c(4,5))
reverseSplit(l1)

Calculates the median for each row in a matrix

Description

Calculates the median for each row in a matrix.

Usage

rowMedians(x, na.rm=FALSE, ...)

Arguments

Details

The implementation of rowMedians() is optimized for both speed and memory. To avoid coercing to double s (and hence memory allocation), there is a special implementation for integer matrices. That is, if x is an integer matrix , then rowMedians(as.double(x)) would require three times the memory of rowMedians(x) , but all this is avoided.

Value

Returns a numeric vector of length N.

Seealso

See rowMeans() in colSums ().

Author

Henrik Bengtsson

Examples

set.seed(1)
x <- rnorm(n=234*543)
x[sample(1:length(x), size=0.1*length(x))] <- NA
dim(x) <- c(234,543)
y1 <- rowMedians(x, na.rm=TRUE)
y2 <- apply(x, MARGIN=1, FUN=median, na.rm=TRUE)
stopifnot(all.equal(y1, y2))

x <- cbind(x1=3, x2=c(4:1, 2:5))
stopifnot(all.equal(rowMeans(x), rowMedians(x)))

A function to compute empirical row quantiles.

Description

This function computes the requested quantile for each row of a matrix, or of an ExpressionSet .

Usage

rowQ(imat, which)
rowMax(imat)
rowMin(imat)

Arguments

Details

rowMax and rowMin simply call rowQ with the appropriate argument set.

The argument which takes values between 1, for the minimum per row, and ncol(imat) , for the maximum per row.

Value

A vector of length equal to the number of rows of the input matrix containing the requested quantiles.

Seealso

rowMedians . rowMeans() in colSums ().

Author

R. Gentleman

Examples

data(sample.ExpressionSet)
rowMin(sample.ExpressionSet)
rowQ(sample.ExpressionSet, 4)

Create a new NChannelSet instance by selecting specific channels

Description

This generic function extracts specific elements from an object, returning a instance of that object.

Usage

selectChannels(object, names, ...)

Arguments

Value

Instance of class object .

Author

Biocore

Examples

obj <- NChannelSet(R=matrix(runif(100), 20, 5), G=matrix(runif(100), 20, 5))

## G channel as NChannelSet
selectChannels(obj, "G")

Extract elements of a vector for concise rendering

Description

Extract the first and last several elements of a vector for concise rendering; insert ellipses to indicate elided elements. This function is primarily meant for developer rather than end-user use.

Usage

selectSome(obj, maxToShow=5)

Arguments

Details

This function can be used in 'show' methods to give users exemplars of the tokens used in a vector. For example, an ExpressionSet built from a yeast experiment might have features enumerated using systematic gene names (e.g., YPR181C) or standard gene names (e.g., SEC23). The show method for ExpressionSet uses selectSome to alert the user to the tokens used, and thereby to indicate what vocabulary must be understood to work with the feature names.

Value

A string vector with at most maxToShow plus 1 elements, where an ellipsis ("...") is included to indicate incompleteness of the excerpt.

Author

Martin Morgan mtmorgan@fhcrc.org

Examples

selectSome(1:20)

Get and retrieve SNP call and call probability data.

Description

These generic functions access the calls and call probabilities stored in objects.

Usage

snpCall(object, ...)
snpCall(object, ...) <- value
snpCallProbability(object, ...)
snpCallProbability(object, ...) <- value

Arguments

Value

snpCall returns a matrix of SNP calls; snpCallProbability returns the corresponding matrix of standard errors, when available.

Seealso

SnpSet-class

Author

Biocore

Retrieve or set storage mode for eSets.

Description

These generic functions report or change the storage mode used for assayData .

Usage

storageMode(object)
storageMode(object) <- value

Arguments

Value

storageMode returns a length-1 character vector

Seealso

AssayData-class , eSet-class ExpressionSet-class , SnpSet-class

Author

Biocore

Break Character Strings to Fit Width

Description

Inserts line breaks ( collapse ) into input character strings. The main intention of this function is to prepare long strings for printing, so the output is not wider than width .

Usage

strbreak(x, width=getOption("width"), exdent=2, collapse="
")

Arguments

Seealso

strwrap , substring

Author

Wolfgang Huber http://www.ebi.ac.uk/huber

Examples

longString = paste(rep(LETTERS, 10), collapse="", sep="")
cat(strbreak(longString))

Extract the same element from the sublists of a list

Description

Given a list of lists, this function can be used to extract a named element from each sublist.

Usage

subListExtract(L, name, simplify = FALSE, keep.names = TRUE)

Arguments

Details

This function is implemented in C and is intended to be faster than calling lapply or sapply .

Value

If simplify=FALSE , a list will be returned having the same length as L , but with each element containing the element named name from the corresponding inner list of L .

When simplify=TRUE , an atomic vector will be returned containing the extracted elements. If any of the inner list elements do not have length one or cannot be put inside an atomic vector, an error will be raised.

Author

Seth Falcon

Examples

list_size = 500000
innerL = list(foo="foo", bar="bar")
L = rep(list(innerL), list_size)

system.time({j0 = sapply(L, function(x) x$foo)})
system.time({j1 = subListExtract(L, "foo", simplify=TRUE)})
stopifnot(all.equal(j0, j1))

LS = L[1:3]
names(LS) = LETTERS[1:3]
subListExtract(LS, "bar", simplify=TRUE)
subListExtract(LS, "bar", simplify=FALSE)
subListExtract(LS, "bar", simplify=TRUE, keep.names=FALSE)

A function to check internet connectivity to Bioconductor

Description

This function will attempt to determine if the user has internet connectivity to the Bioconductor website. This is useful in many situations dealing with code that uses automated downloads and other such things.

Usage

testBioCConnection()

Value

TRUE if a connection is possible, FALSE if not.

Author

Jeff Gentry

Examples

z <- testBioCConnection()

Update an object to the class definition of a template

Description

The updateObjectTo generic function returns an instance of object updated to the class definition of template .

It requires that the class of the returned object be the same as the class of the template argument, and that the object is valid. Usually, updating proceeds by modifying slots in template with information from object , and returning template . Use as to coerce an object from one type to another; updateObjectTo might be useful to update a virtual superclass. By default, updateObjectTo has the following behavior: list(" ", " ", list(list(list("updateObjectTo(ANY-object,ANY-template)")), list("Attempt ", list("as(ANY-object,class(ANY-template))"), ".")), " ", " ")

Usage

updateObjectTo(object, template, list(), verbose=FALSE)

Arguments

Value

updateObjectTo returns a valid instance of template .

Seealso

updateObject , Versions-class

Author

Biocore team

Update previously created eSet object to current eSet structure

Description

This function updates eSet objects created in previous versions of Biobase to the current class structure. Warnings indicate when coercions change how data in the from object are altered. If the from object was not a valid object of the original eSet class, then updateOldESet may fail.

Usage

updateOldESet(from, toClass, ...)

Arguments

Value

Valid object of class toClass .

Seealso

eSet-class , ExpressionSet-class , SnpSet-class

Author

Biocore

Examples

updateOldESet(oldESet, "ExpressionSet")

A function to query the user for input

Description

This function will output a given message and seek a response from the user, repeating the message until the input is from a valid set provided by the code.

Usage

userQuery(msg, allowed = c("y", "n"), default = "n", case.sensitive = FALSE)

Arguments

Value

The input from the user

Author

Jeff Gentry

Conditionally append result to validity message

Description

This function facilitates constructing messages during S4 class validation, and is meant for developer rather than end-user use.

Usage

validMsg(msg, result)

Arguments

Details

This function appends result to msg , but only if result is a character vector.

Author

Martin Morgan mtmorgan@fhcrc.org

Examples

msg <- NULL
validMsg(msg, FALSE) # still NULL
msg <- validMsg(msg, "one")
validMsg(msg, "two")