IcaSet {MineICA} | R Documentation

## Class to Contain and Describe an ICA decomposition of High-Throughput Data.

### Description

Container for high-throughput data and results of ICA decomposition obtained on these data. `IcaSet` class is derived from `eSet`, and requires a matrix named `dat` as `assayData` member.

### Extends

Directly extends class `eSet`.

### Creating Objects

`new("IcaSet")`

`new("IcaSet", annotation = character(0), experimentData = new("MIAME"), featureData = new("AnnotatedDataFrame"), phenoData = new("AnnotatedDataFrame"), protocolData = phenoData[,integer(0)], dat = new("matrix"), A=new("data.frame"), S=new("data.frame"), ...)`

This creates an `IcaSet` with `assayData` implicitly created to contain `dat`.

`new("IcaSet", annotation = character(0), assayData = assayDataNew(dat=new("matrix")), experimentData = new("MIAME"), featureData = new("AnnotatedDataFrame"), phenoData = new("AnnotatedDataFrame"), protocolData = phenoData[,integer(0)], A=new("data.frame"), S=new("data.frame"), ...)`

This creates an `IcaSet` with `assayData` provided explicitly.

`IcaSet` instances are usually created through `new("IcaSet", ...)`. Usually the arguments to `new` include `dat` ('features x samples', e.g a matrix of expression data), `phenoData` ('samples x annotations', a matrix of sample annotations), `S` the Source matrix of the ICA decomposition ('features x comp'), `A` the Mixing matrix of the ICA decomposition ('samples x comp'), `annotation` the annotation package, `typeID` the description of the feature and gene IDs.

The other attributes can be missing, in which case they are assigned default values.

The function `buildIcaSet` is a more convenient way to create `IcaSet` instances, and allows to automatically annotate the features.

### Slots

Inherited from `eSet`:

`annotation`:

  See `eSet`

`assayData`:

  Contains matrices with equal dimensions, and with column number equal to `nrow(phenoData)`. `assayData` must contain a matrix `dat` with rows representing features (e.g., reporters) and columns representing samples. Class:`AssayData-class`

`experimentData`:

  See `eSet`

`featureData`:

  See `eSet`

`phenoData`:

  See `eSet`

`protocolData`:

  See `eSet`

Specific slot:

`organism`:

  Contains the name of the species. Currently only Human ("Human" or "Homo sapiens") and Mouse ("Mouse" or "Mus musculus") are supported. Only used when `chipManu`="illumina"

`mart`:

  An output of `useMart` of package `biomaRt`. Only useful if no annotation package is available for argument `icaSet`.

`datByGene`:

  Data.frame containing the data `dat` where features have been replaced by their annotations (e.g, gene IDs). Rows represent annotations of the features (e.g., gene IDs) and columns represent samples.

`A`:

  The mixing matrix of the ICA decomposition, contained in a data.frame whose column number equals the number of components and row number equals `nrow(phenoData)` (dimension: 'samples x comp').

`S`:

  The source matrix of the ICA decomposition, contained in a data.frame whose column number equals the number of components and row number equals `nrow(assayData)` (dimension: 'features x comp').

`SByGene`:

  The matrix Source of the ICA decomposition, contained in a data.frame whose column number equals the number of components and row number equals `nrow(datByGene)` (dimension: 'annotatedFeatures x comp').

`compNames`:

  A vector of component labels with length equal to the number of component.

`indComp`:

  A vector of component indices with length equal to the number of component.

`witGenes`:

  A vector of gene IDs with length equal to the number of component.

`chipManu`:

  The manufacturer of the technology the data originates from. Useful for the annotation of the features when data originates from an _illumina_ microarray.

`chipVersion`:

  The version of the chip, only useful for when `chipManu`="illumina"

`refSamples`:

  A vector of sample IDs including the reference samples, e.g the "normal" samples. Must be included in `sampleNames(object)`, i.e in `colnames(dat)`.

`typeID`:

  A vector of characters providing the annotation IDs. It includes three elements:

  geneID_annotation

    the IDs from the package to be used to annotate the features into genes. It will be used to fill the attributes `datByGene` and `SByGene` of the `icaSet`. It must match one of the objects the corresponding package supports (you can access the list of objects by typing ls("package:packagename")). If no annotation package is provided, this element is not useful.

  geneID_biomart

    the type of gene IDs, as available in `listFilters(mart)`; where mart is specified as described in `useMart`. If you have directly built the IcaSet at the gene level (i.e if no annotation package is used), `featureID_biomart` and `geneID_biomart` will be identical.

  featureID_biomart

    the type of feature IDs, as available in `listFilters(mart)`; where `mart` is specified as described in function `useMart`. Not useful if you work at the gene level.

### Methods

Class-specific methods.

`getComp(IcaSet, ind, level=c("features","genes"))`

  Given a component index, extract the corresponding sample contribution values from A, and the feature (`level`="features") or gene (`level`="genes") projections from S. Returns a list with two elements: `contrib` the sample contributions and `proj` the feature or gene projections.

Access and set any slot specific to IcaSet:

`slotName(IcaSet)`, and `slotName(IcaSet)<-`:

  Accessing and setting any slot of name `slotName` contained in an IcaSet object.

`IcaSet["slotName"]`, and `IcaSet["slotName"]<-`:

  Accessing and setting any slot of name `slotName` contained in an IcaSet object.

Most used accessors and settors:

`A(IcaSet)`, and `A(IcaSet)<-`:

  Accessing and setting Mixing matrix `A`.

`S(IcaSet)`, and `S(IcaSet)<-`:

  Accessing and setting the data.frame Source `S`.

`Slist(IcaSet)`:

  Accessing the data.frame Source as a list where names are preserved.

`SByGene(IcaSet)`, and `SByGene(IcaSet)<-`:

  Accessing and setting the _annotated_ data.frame Source `SByGene`.

`SlistByGene(IcaSet)`:

  Accessing the _annotated_ Source matrix as a list where names are preserved.

`organism(IcaSet)`, `organism(IcaSet,characte)<-`

  Access and set value in the `organism` slot.

`dat(IcaSet)`, `dat(IcaSet,matrix)<-`

  Access and set elements named `dat` in the `AssayData-class` slot.

Derived from `eSet`:

`pData(IcaSet)`, `pData(IcaSet,value)<-`:

  See `eSet`

`assayData(IcaSet)`:

  See `eSet`

`sampleNames(IcaSet)` and `sampleNames(IcaSet)<-`:

  See `eSet`

`featureNames(IcaSet)`, `featureNames(IcaSet, value)<-`:

  See `eSet`

`dims(IcaSet)`:

  See `eSet`

`phenoData(IcaSet)`, `phenoData(IcaSet,value)<-`:

  See `eSet`

`varLabels(IcaSet)`, `varLabels(IcaSet, value)<-`:

  See `eSet`

`varMetadata(IcaSet)`, `varMetadata(IcaSet,value)<-`:

  See `eSet`

`varMetadata(IcaSet)`, `varMetadata(IcaSet,value)`

  See `eSet`

`experimentData(IcaSet)`,`experimentData(IcaSet,value)<-`:

  See `eSet`

`pubMedIds(IcaSet)`, `pubMedIds(IcaSet,value)`

  See `eSet`

`abstract(IcaSet)`:

  See `eSet`

`annotation(IcaSet)`, `annotation(IcaSet,value)<-`

  See `eSet`

`protocolData(IcaSet)`, `protocolData(IcaSet,value)<-`

  See `eSet`

`combine(IcaSet,IcaSet)`:

  See `eSet`

`storageMode(IcaSet)`, `storageMode(IcaSet,character)<-`:

  See `eSet`

Standard generic methods:

`initialize(IcaSet)`:

  Object instantiation, used by `new`; not to be called directly by the user.

`validObject(IcaSet)`:

  Validity-checking method, ensuring that `dat` is a member of `assayData`, and that the number of features, genes, samples, and components are consistent across all the attributes of the IcaSet object. `checkValidity(IcaSet)` imposes this validity check, and the validity checks of `eSet`.

`IcaSet[slotName]`, `IcaSet[slotName]<-`:

  Accessing and setting any slot of name `slotName` contained in an IcaSet object.

`IcaSet[i, j, k]`:

  Extract object of class "IcaSet" for features or genes with names i, samples with names or indices j, and components with names or indices k.

`makeDataPackage(object, author, email, packageName, packageVersion, license, biocViews, filePath, description=paste(abstract(object), collapse="\n\n"), ...)`

  Create a data package based on an IcaSet object. See `makeDataPackage`.

`show(IcaSet)`:

  See `eSet`

`dim(IcaSet)`, `ncol`:

  See `eSet`

`IcaSet[(index)]`:

  See `eSet`

`IcaSet$`, `IcaSet$<-`:

  See `eSet`

`IcaSet[[i]]`, `IcaSet[[i]]<-`:

  See `eSet`

### Author(s)

Anne Biton

### See Also

`eSet-class`, `buildIcaSet`, `IcaSet-class`, `MineICAParams-class`.

### Examples

    # create an instance of IcaSet
    new("IcaSet")
    dat <- matrix(runif(100000), nrow=1000, ncol=100)
    rownames(dat) <- 1:nrow(dat)
    new("IcaSet",
        dat=dat,
        A=as.data.frame(matrix(runif(1000), nrow=100, ncol=10)),
        S=as.data.frame(matrix(runif(10000), nrow=1000, ncol=10), row.names = 1:nrow(dat)))

[Package MineICA version 1.26.0 Index]