eisa (version 1.24.0)

ISAIterate: 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.

Value

ISAModules object.

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.

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

Run this code
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

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