ISAFilterRobust {eisa} | R Documentation |
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.
ISARobustness(data, isaresult) ISAFilterRobust(data, isaresult, ...)
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. |
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.
ISARobustness
returns a numeric vector, the robustness scores
of the biclusters.
Gabor Csardi Gabor.Csardi@unil.ch
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.
The robustness
function in the isa2
package.
data(ALLModules) library(ALL) data(ALL) rob <- ISARobustness(ALL, ALLModules) summary(rob)