ISAUnique {eisa} | R Documentation

## Remove duplicated ISA modules

### 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 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 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

[Package eisa version 1.39.0 Index]