ISAFilterRobust {eisa} | R Documentation

## Robustness of ISA biclusters

### Description

Robustness of ISA biclusters. The more robust biclusters are more significant, in the sense that they are less likely to be found in random data.

### Usage

    ISARobustness(data, isaresult)
    ISAFilterRobust(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, a set of modules.
`...` | Additional arguments, they are passed to the `isa.filter.robust` function in the `isa2` package.

### Details

`ISARobustness` calculates robustness scores for ISA modules. The higher the score, the more robust the module.

`ISAFilterRobust` filters a set of ISA modules, by running ISA on the randomized expression data and then eliminating all modules that have a robustness score that is lower than at least one robustness score found in the randomized data.

The same feature and sample thresholds are used to calculate the randomized robustness scores. In other words the limit for the filtering depends on the feature and sample thresholds.

You can find more details in the manual of the `robustness` function in the `isa2` package.

### Value

`ISARobustness` returns a numeric vector, the robustness scores of the biclusters.

`ISAFilterRobust` returns the filtered `ISAModules` instance.

### 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 `robustness` function in the `isa2` package.

### Examples

    data(ALLModules)
    library(ALL)
    data(ALL)
    rob <- ISARobustness(ALL, ALLModules)
    summary(rob)

[Package eisa version 1.40.0 Index]