| isa.iterate {isa2} | R Documentation |
Perform ISA on the (normalized) input matrix.
## S4 method for signature 'list': isa.iterate(normed.data, ...)
normed.data |
The normalized data. A list of two matrices,
usually coming from isa.normalize. |
... |
Additional arguments, see details below. |
isa.iterate performs the ISA iteration on the specified input
seeds. It can be called as
isa.iterate(normed.data, row.seeds, col.seeds,
thr.row, thr.col = thr.row,
direction = c("updown", "updown"),
convergence = c("corx", "cor", "eps"),
cor.limit = 0.99, eps = 1e-04, corx=3,
oscillation = FALSE, maxiter = 100)
where the arguments are:
isa.normalize.col.seeds are both present, then both of them are used.)row.seeds are both present, then both of them are used.)thr.row.up’) than average,
lower than average (‘down’), or both
(‘updown’).cor’, then convergence is
measured based on high Pearson correlation (see the cor.limit
argument below) of the subsequent row and
column vectors. If it is ‘eps’, then all entries of
the subsequent row and column vectors must be close to each other,
see the eps argument below.
‘corx’ is similar to ‘cor’, but the
current row/column vectors are compared to the ones corx
steps ago, instead of the ones in the previous step. See the
corx argument below, that gives the size of the shift.
cor’ method is used.eps’ method is
used.corx’ method.FALSE.
A named list with many components. Please see the manual page of
isa for a complete description.
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.
Ihmels J, Friedlander G, Bergmann S, Sarig O, Ziv Y, Barkai N: Revealing modular organization in the yeast transcriptional network Nat Genet. 2002 Aug;31(4):370-7. Epub 2002 Jul 22
Ihmels J, Bergmann S, Barkai N: Defining transcription modules using large-scale gene expression data Bioinformatics 2004 Sep 1;20(13):1993-2003. Epub 2004 Mar 25.
isa2-package for a short introduction on the Iterative
Signature Algorithm. See isa for an easy way of running
ISA.
## A basic ISA work flow for a single threshold combination
## In-silico data
set.seed(1)
insili <- isa.in.silico()
## Random seeds
seeds <- generate.seeds(length=nrow(insili[[1]]), count=100)
## Normalize input matrix
nm <- isa.normalize(insili[[1]])
## Do ISA
isares <- isa.iterate(nm, row.seeds=seeds, thr.row=2, thr.col=1)
## Eliminate duplicates
isares <- isa.unique(nm, isares)
## Filter out not robust ones
isares <- isa.filter.robust(insili[[1]], nm, isares)
## Print the sizes of the modules
cbind( colSums(isares$rows!=0), colSums(isares$columns!=0) )
## Plot the original data and the modules found
if (interactive()) {
layout(rbind(1:2))
image(insili[[1]], main="In silico data")
image(outer(isares$rows[,1],isares$columns[,1])+
outer(isares$rows[,2],isares$columns[,2])+
outer(isares$rows[,3],isares$columns[,3]), main="ISA modules")
}