ISAIterate {eisa} | R Documentation |
ISAIterate
performs the ISA on an ExpressionSet
object, from the given input seeds.
ISAIterate(data, feature.seeds, sample.seeds, thr.feat, thr.samp = thr.feat, ...)
data |
An ExpressionSet or ISAExpressionSet
object. If ExpressionSet object is supplied, then it is
normalised by calling ISANormalize on it. |
feature.seeds |
A matrix of feature seeds. The number of rows
should match the number of features in the ExpressionSet ,
each column is a seed. Either this, or the sample.seeds
argument must be given. |
sample.seeds |
A matrix of sample seeds. The number of rows
should match the number of samples in the ExpressionSet , each
column in a seed. Either this, or the feature.seeds argument
must be given. |
thr.feat |
Numeric scalar or vector giving the threshold parameter for the features. Higher values indicate a more stringent threshold and the result biclusters will contain less features on average. The threshold is measured by the number of standard deviations from the mean, over the values of the feature vector. If it is a vector then it must contain an entry for each seed. |
thr.samp |
Numeric scalar or vector giving the threshold parameter
for the columns. The analogue of thr.feat . |
... |
Additional arguments, these are passed to the
isa.iterate function in the isa2
package. See also details below. |
Performs the ISA from the given seeds. It is allowed to specify both
type of seeds, then a half-iteration is performed on the
sample.seeds
and they are appended to the
feature.seeds
.
The isa.iterate
function of the isa2
package is called to do all the work, this has the following extra
parameters: direction
, convergence
, cor.limit
,
eps
, corx
, oscillation
, maxiter
. Please see the
isa.iterate
manual for details about them.
An ISAModules
object.
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 interface with
parameters.
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