ISAIterate {eisa} | R Documentation

## Perform the Iterative Signature Algorithm

### Description

`ISAIterate` performs the ISA on an `ExpressionSet` object, from the given input seeds.

### Usage

    ISAIterate(data, feature.seeds, sample.seeds, thr.feat,
        thr.samp = thr.feat, ...)

### Arguments

`data` | An `ExpressionSet` or `ISAExpressionSet` object. If an `ExpressionSet` object is supplied, then it is normalised by calling `ISANormalize` on it.
`feature.seeds` | A matrix of feature seeds. The number of rows should match the number of features in the `ExpressionSet`, each column is a seed. Either this, or the `sample.seeds` argument must be given.
`sample.seeds` | A matrix of sample seeds. The number of rows should match the number of samples in the `ExpressionSet`, each column in a seed. Either this, or the `feature.seeds` argument must be given.
`thr.feat` | Numeric scalar or vector giving the threshold parameter for the features. Higher values indicate a more stringent threshold and the result biclusters will contain less features on average. The threshold is measured by the number of standard deviations from the mean, over the values of the feature vector. If it is a vector, then it must contain an entry for each seed.
`thr.samp` | Numeric scalar or vector giving the threshold parameter for the columns. The analogue of `thr.feat`.
`...` | Additional arguments, these are passed to the `isa.iterate` function in the `isa2` package. See also details below.

### Details

Performs the ISA from the given seeds. It is allowed to specify both type of seeds, then a half-iteration is performed on the `sample.seeds` and they are appended to the `feature.seeds`.

The `isa.iterate` function of the `isa2` package is called to do all the work, this has the following extra parameters: `direction`, `convergence`, `cor.limit`, `eps`, `corx`, `oscillation`, `maxiter`. Please see the `isa.iterate` manual for details about them.

### Value

An `ISAModules` object.

### 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 `ISA` function for an easier interface with parameters.

### Examples

    library(ALL)
    data(ALL)

    # Only use a small sample, to make this example finish faster
    ALL.normed <- ISANormalize(ALL)[sample(1:nrow(ALL), 1000),]

    # Generate seeds and do ISA
    seeds <- generate.seeds(nrow(ALL.normed), count=100)
    modules <- ISAIterate(ALL.normed, seeds, thr.feat=3, thr.samp=2)
    modules

[Package eisa version 1.36.0 Index]