ISAModules-class {eisa} | R Documentation

## A set of ISA modules

### Description

An `ISAModules` object stores the results of one ISA run. It contains a set of biclusters (=modules or transcription modules) and some meta information about the ISA run and the input data.

### Usage

    ## S4 method for signature 'ISAModules'
    dim(x)
    ## S4 method for signature 'ISAModules'
    featureNames(modules)
    ## S4 method for signature 'ISAModules'
    sampleNames(modules)
    ## S4 method for signature 'ISAModules'
    annotation(modules)
    ## S4 method for signature 'ISAModules'
    getOrganism(modules)
    ## S4 method for signature 'ISAModules'
    pData(modules)

    ## S4 method for signature 'ISAModules'
    seedData(modules)
    ## S4 method for signature 'ISAModules'
    runData(modules)
    ## S4 method for signature 'ISAModules'
    featureThreshold(modules, mods)
    ## S4 method for signature 'ISAModules'
    sampleThreshold(modules, mods)

    ## S4 method for signature 'ISAModules'
    length(x)
    ## S4 method for signature 'ISAModules'
    getNoFeatures(modules, mods)
    ## S4 method for signature 'ISAModules'
    getNoSamples(modules, mods)

    ## S4 method for signature 'ISAModules'
    getFeatures(modules, mods)
    ## S4 method for signature 'ISAModules'
    getSamples(modules, mods)
    ## S4 method for signature 'ISAModules'
    getFeatureNames(modules, mods)
    ## S4 method for signature 'ISAModules'
    getSampleNames(modules, mods)
    ## S4 method for signature 'ISAModules'
    getFeatureScores(modules, mods)
    ## S4 method for signature 'ISAModules'
    getSampleScores(modules, mods)
    ## S4 method for signature 'ISAModules'
    getFeatureMatrix(modules, binary = FALSE,
          sparse = FALSE, mods)
    ## S4 method for signature 'ISAModules'
    getSampleMatrix(modules, binary = FALSE,
          sparse = FALSE, mods)
    ## S4 method for signature 'ISAModules'
    getFullFeatureMatrix(modules, eset, mods)
    ## S4 method for signature 'ISAModules'
    getFullSampleMatrix(modules, eset, mods)

    ## S4 method for signature 'ISAModules,ANY,ANY'
    x[i, j, ..., drop = FALSE]
    ## S4 method for signature 'ISAModules,ANY,ANY'
    x[[i, j, ..., drop = FALSE]]

### Arguments

`x,modules` | An `ISAModules` object.
`mods` | An optional numeric index vector for the modules. If given, the information is only returned only for the specified modules.
`binary` | Logical scalar. Whether to binarize the feature or sample scores.
`sparse` | Logical scalar. Whether to return a sparse matrix. The `Matrix` package is required for sparse matrices.
`eset` | An `ExpressionSet` or `ISAExpressionSet` object. This is needed for calculating the scores of the features/samples that are not in the module. If an `ExpressionSet` object is supplied, then it is normalised by calling `ISANormalize` on it.
`i` | For ‘`[`’ an index vector for selecting features (=probes, genes). For ‘`[[`’ an index vector for selecting modules.
`j` | For ‘`[`’ an index vector for selecting samples. It is ignored for ‘`[[`’.
`...` | Additional indexing arguments, they are not used, just ignored.
`drop` | This argument is currently not used, just silently ignored.

### Details

An `ISAModules` object contains a set of biclusters, obtained using one run of the Iterative Signature Algorithm.

Various operations are defined such an object, here we document all of them, in several groups.

### Value

`dim` returns a numeric vector of length two. `featureNames` and `sampleNames` return a character vector. `annotation` and `getOrganism` return a character vector of length one. `pData` returns a data frame.

`seedData` returns a data frame, see more below. `runData` returns a named list, see more below. `featureThreshold` and `sampleThreshold` return a numeric vector.

`length` returns a numeric scalar. `getNoFeatures` and `getNoSamples` return a numeric vector.

`getFeatures` and `getSamples` return a list of named numeric vectors. `getFeatureNames` and `getSampleNames` return a list of character vectors. `getFeatureScores` and `getSampleScores` return a list of named numeric vectors. `getFeatureMatrix`, `getSampleMatrix`, `getFullFeatureMatrix` and `getFullSampleMatrix` return a numeric matrix.

### Information about the input data.

`dim` returns the dimension of the input expression matrix, number of features times number of samples.

`featureNames` returns a character vector, the names of the features in the original input matrix. I.e. in the input was an `ExpressionSet` for an Affymetrix array, then the Affymetrix probe IDs are returned.

`sampleNames` returns a character vector, the names of the samples in the original expression set.

`annotation` returns a character scalar, the name of the array for the input expression set. More precisely, the `annotation` slot of the input `ExpressionSet` is returned, this is the name of the annotation package to use for the `ExpressionSet`.

`getOrganism` returns the scientific name of the organism for which the input expression data was measures. This is obtained by loading the annotation package of the input `ExpressionSet` object, so that must be installed.

`pData` returns the phenotypic data attached to the input `ExpressionSet` object, in a data frame, samples as rows and various phenotypic variables as columns.

### Information about the ISA run

`seedData` returns information about the modules. Each row of the returned data frame corresponds to one module, the columns are various variables:

iterations

  The number of ISA iterations needed to find the module.

oscillation

  The length of the oscillation cycle for oscillating modules, zero for others.

thr.row

  The feature (=gene) threshold used for finding the module.

thr.col

  The sample (=condition) threshold used for finding the module.

freq

  The number of times the module was found. This is always one, unless `ISAUnique` was performed.

rob

  The robustness score of the module. See `ISARobustness` for details.

rob.limit

  The robustness limit that was used for filtering the modules. As this depends of the feature and sample thresholds, it may be different for different modules.

`runData` returns information about the ISA runs, it is a named list with elements:

annotation

  The annotation package corresponding to the input expression set.

organism

  The scientific name of the organism.

direction

  The `direction` parameter of the ISA. Please see `ISAIterate` for details.

convergence

  The method to determine ISA convergence, a character scalar. Please see `ISAIterate` for details.

cor.limit

  Correlation limit for the “`cor`” convergence criterium, see `ISAIterate` for details.

eps

  Difference limit for the “`eps`” convergence criterium, see `ISAIterate` for details.

corx

  Size of the time window for the “`corx`” convergence criterium, see `ISAIterate` for details.

maxiter

  The maximum number of ISA iterations that was allowed.

oscillation

  Logical, whether oscillating modules were considered during the ISA iteration.

N

  Numeric scalar, the number of input seeds that were used to find the modules.

unique

  Logical scalar, whether `ISAUnique` was run on the modules.

prenormalize

  Logical scalar, whether the input data was prenormalized during ISA normalization, see `ISANormalize`.

hasNA

  Logical scalar, whether the normalized input data contained some `NA` or `NaN` values.

rob.perms

  Numeric scalar, the number of times the input data was scrambled when the modules were filtered according to robustness.

Note that some of these might be missing, i.e. `rob.perms` is only present if `ISAFilterRobust` was performed.

`featureThreshold` returns the feature thresholds that were used to find the modules.

`sampleThreshold` returns the sample thresholds that were used to find the modules.

### Information about the modules

`length` returns the number of modules.

`getNoFeatures` returns the number of features (=genes) in the input data. The number of features after filtering is returned if the input data was filtered.

`getNoSamples` returns the number of samples (=conditions) in the input data.

### Retrieve the modules

`getFeatures` returns the indices of the features included in the modules. It returns a list, with one entry for each module. Each entry contains the indices of the features (=genes) in the corresponding module.

`getSamples` does the same as `getFeatures`, but for samples.

`getFeatureNames` is similar to `getFeatures`, but returns feature names instead of feature indices.

`getSampleNames` is similar to `getSamples`, but returns sample names instead of sample indices.

`getFeatureScores` returns the feature scores for the selected modules (all modules by default). It returns a list, with one entry for each module. Each list entry contains the feature scores for one module, in a named numeric vector.

`getSampleScores` is similar to `getFeatureScores`, but for samples and sample scores.

`getFeatureMatrix` returns feature scores for the specified modules (all modules by default) in a matrix form. The number of rows is the number of features and the number of columns is the number of modules requested. It can optionally binarize the values.

`getSampleMatrix` is similar to `getFeatureMatrix`, but for sample scores.

`getFullFeatureMatrix` is similar to `getFeatureMatrix`, but is also calculates scores for the features that were not included in the module. For this it performs one ISA iteration and omits the thresholding step. You need to supply the normalized (or the original) expression data to make this possible.

`getFullSampleMatrix` is the same as `getFullFeatureMatrix`, but for sample scores.

### Indexing

A couple of indexing operations were defined to make it easier selecting subsets of modules, features or samples from an `ISAModules` object.

The ‘`[[`’ double bracket indexing operator can be used with a single index vector to select a subset of modules.

The ‘`[`’ single bracket indexing operator can be used to restrict an `ISAModules` object to a subset of features and/or samples. The first index corresponds to features, the second to samples. Indices can be numeric, logical or character vectors, for the latter feature and sample names are used.

### Author(s)

Gabor Csardi csardi.gabor@gmail.com

### References

Bergmann S, Ihmels J, Barkai N: Iterative signature algorithm for the analysis of large-scale gene expression data Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Mar;67(3 Pt 1):031902. Epub 2003 Mar 11.

### See Also

The vignette included in the `eisa` package.

### Examples

    data(ALLModulesSmall)
    ALLModulesSmall

    length(ALLModulesSmall)
    dim(ALLModulesSmall)
    annotation(ALLModulesSmall)
    getOrganism(ALLModulesSmall)

    seedData(ALLModulesSmall)

    getNoFeatures(ALLModulesSmall)
    getNoSamples(ALLModulesSmall)

    getFeatureScores(ALLModulesSmall, 1)[[1]]

[Package eisa version 1.40.0 Index]