isa.unique | R Documentation |
From a potentially non-unique set of ISA biclusters, create a unique set by removing all biclusters that are similar to others.
## S4 method for signature 'list,list' isa.unique(normed.data, isaresult, ...)
normed.data |
The normalized input data, a list of two matrices,
usually the output of |
isaresult |
The result of an ISA run, a set of biclusters. |
... |
Additional arguments, see details below. |
This function can we called as
isa.unique(normed.data, isaresult, method = c("cor"), ignore.div = TRUE, cor.limit = 0.9, neg.cor = TRUE, drop.zero = TRUE)
where the arguments are:
The normalized input data, a list of two matrices,
usually the output of isa.normalize
.
The result of an ISA run, a set of biclusters.
Character scalar giving the method to be used to
determine if two biclusters are similar. Right now only
‘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.
Logical scalar, if TRUE
, then the divergent
biclusters will be removed.
Numeric scalar, giving the correlation limit for the
‘cor
’ method.
Logical scalar, if TRUE
, then the
‘cor
’ method considers the absolute value of the
correlation.
Logical scalar, whether to drop biclusters that have all zero scores.
Because of the nature of the ISA algorithm, the set of biclusters
created by isa.iterate
is not unique; many input seeds
may converge to the same biclusters, even if the input seeds are not
random.
isa.unique
filters a set of biclusters and removed the ones
that are very similar to ones that were already found for another
seed.
A named list, the filtered isaresult
. See the return value of
isa.iterate
for the details.
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.
## Create an ISA module set set.seed(1) insili <- isa.in.silico(noise=0.01) ## Random seeds seeds <- generate.seeds(length=nrow(insili[[1]]), count=20) ## Normalize input matrix nm <- isa.normalize(insili[[1]]) ## Do ISA isares <- isa.iterate(nm, row.seeds=seeds, thr.row=2, thr.col=1) ## Check correlation among modules cc <- cor(isares$rows) if (interactive()) { hist(cc[lower.tri(cc)],10) } ## Some of them are quite high, how many? undiag <- function(x) { diag(x) <- 0; x } sum(undiag(cc) > 0.99, na.rm=TRUE) ## Eliminate duplicated modules isares.unique <- isa.unique(nm, isares) ## How many modules left? ncol(isares.unique$rows) ## Double check cc2 <- cor(isares.unique$rows) if (interactive()) { hist(cc2[lower.tri(cc2)],10) } ## High correlation? sum(undiag(cc2) > 0.99, na.rm=TRUE)
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