robustness: Robustness of ISA biclusters

In eisa: Expression data analysis via the Iterative Signature Algorithm

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 [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 robustness function in the isa2 package.

Examples

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

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