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") }