ISA.unique: Remove duplicated ISA modules

In eisa: Expression data analysis via the Iterative Signature Algorithm

Description

From a potentially non-unique set of ISA modules remove all modules that are similar to another module that was found earlier.

Usage

ISAUnique(data, isaresult, ...)

Arguments

data	An ExpressionSet or ISAExpressionSet object. If an ExpressionSet object is supplied, then it is normalised by calling ISANormalize on it.
isaresult	An ISAModules object to be filtered.
...	Additional arguments, these are passed to the isa.unique function in the isa2 package. See also details below.

Details

The ISA algorithm might very well find the same modules from many different input seeds, so the output of the ISAIterate function is usually not unique: many modules are very similar to each other.

ISAUnique eliminates the duplicates and potentially also the non-convergent modules.

The work is performed by calling the isa.iterate function in the isa2 package. The following additional arguments can be specified to be passed to this function:

method

Character scalar giving the method to be used to determine if two biclusters are similar. Right now only ‘cor’ is implemented, this keeps both biclusters if their Pearson correlation is less than cor.limit, both for their row and column scores. See also the neg.cor argument.

ignore.div

Logical scalar, if TRUE, then the divergent biclusters will be removed.

cor.limit

Numeric scalar, giving the correlation limit for the ‘cor’ method.

neg.cor

Logical scalar, if TRUE, then the ‘cor’ method considers the absolute value of the correlation.

drop.zero

Logical scalar, whether to drop biclusters that have all zero scores.

Value

Another ISAModules object, with unique modules.

Author(s)

Gabor Csardi [e-mail]

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 ISA workflow.

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

# Merge the modules
modules2 <- ISAUnique(ALL.normed, modules)
modules2

eisa documentation built on Nov. 8, 2020, 6:47 p.m.