ISAUnique {eisa} | R Documentation |
From a potentially non-unique set of ISA modules remove all modules that are similar to others.
ISAUnique(data, isaresult, ...)
data |
An ExpressionSet or ISAExpressionSet
object. If 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. |
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
eliminate 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:
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.TRUE
, then the divergent
biclusters will be removed.cor
’ method.TRUE
, then the
‘cor
’ method considers the absolute value of the
correlation.
Another ISAModules
object, with unique modules.
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 ISA
function for an easier ISA workflow.
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