Description Usage Arguments Details Value Author(s) References See Also Examples
Run ISA with the default parameters
1 2 
data 
The input. It must be a numeric matrix. It may contain

... 
Additional arguments, see details below. 
Please read the isa2package manual page for an introduction on ISA.
This function can be called as
1 2 3 4 
where the arguments are:
The input. It must be a numeric matrix. It may contain
NA
and/or NaN
values, but then the algorithm might be a
bit slower, as R matrix multiplication might be slower for these
matrices, depending on your platform.
Numeric vector.
The row threshold parameters for which the ISA will be
run. We use all possible combinations of thr.row
and
thr.col
.
Numeric vector.
The column threshold parameters for which the ISA will be run. We
use all possible combinations of thr.row
and thr.col
.
Integer scalar, the number of seeds to use.
Character vector of length two, one for the rows, one
for the columns. It specifies whether we are interested in
rows/columns that are higher (‘up
’) than average,
lower than average (‘down
’), or both
(‘updown
’).
The isa
function provides an easy to use interface to the
ISA. It runs all steps of a typical ISA work flow with their default
parameters.
This involves:
Normalizing the data by calling isa.normalize
.
Generating random input seeds via
generate.seeds
.
Running ISA with all combinations of given row and column
thresholds, (by default 1, 1.5, 2, 2.5, 3); by calling
isa.iterate
.
Merging similar modules, separately for each threshold
combination, by calling isa.unique
.
Filtering the modules separately for each threshold combination,
by calling isa.filter.robust
.
Putting all modules from the runs with different thresholds into a single object.
Merging similar modules, across all threshold combinations, if two modules are similar, then the larger one, the one with the milder thresholds is kept.
Please see the manual pages of these functions for the details or if you want to change their default parameters.
A named list is returned with the following elements:
rows 
The row components in the biclusters, a numeric matrix. Every column in it corresponds to a bicluster, if an element (the score of the row) is nonzero, that means that the row is included in the bicluster, otherwise it is not. Scores are between 1 and 1. If the scores of two rows have the same (nonzero) sign, that means that the two corresponding rows “behave” the same way. If they have opposite sign, that means that they behave the opposite way. If the corresponding seed has not converged during the allowed
number of iterations, then that column of 
columns 
The column components of the biclusters, in the same format as the rows. If the corresponding seed has not converged during the allowed
number of iterations, then that column of 
seeddata 
A data frame containing information about the biclusters. There is one row for each bicluster. The data frame has the following columns:

rundata 
A named list with information about the ISA runs. It has the following entries:

Gabor Csardi [email protected]
Bergmann S, Ihmels J, Barkai N: Iterative signature algorithm for the analysis of largescale 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):3707. Epub 2002 Jul 22
Ihmels J, Bergmann S, Barkai N: Defining transcription modules using largescale gene expression data Bioinformatics 2004 Sep 1;20(13):19932003. Epub 2004 Mar 25.
isa2package for a short introduction on the Iterative Signature Algorithm. See the functions mentioned above if you want to change the default ISA parameters.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  ## Not run:
## We generate some noisy insilico data with modules and try to find
## them with the ISA. This might take one or two minutes.
data < isa.in.silico(noise=0.1)
isa.result < isa(data[[1]])
## Find the best bicluster for each block in the input
best < apply(cor(isa.result$rows, data[[2]]), 2, which.max)
## Check correlation
sapply(seq_along(best),
function(x) cor(isa.result$rows[,best[x]], data[[2]][,x]))
## The same for the columns
sapply(seq_along(best),
function(x) cor(isa.result$columns[,best[x]], data[[3]][,x]))
## Plot the data and the modules found
if (interactive()) {
layout(rbind(1:2,3:4))
image(data[[1]], main="Insilico data")
sapply(best, function(b) image(outer(isa.result$rows[,b],
isa.result$columns[,b]),
main=paste("Module", b)))
}
## End(Not run)

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