ISA {eisa} | R Documentation |
Run ISA on an ExpressionSet with the default parameters.
ISA (data, flist = filterfun(function(x) IQR(x) > 0.5), uniqueEntrez = TRUE, thr.gene = seq(2, 4, by = 0.5), thr.cond = seq(1, 3, by = 0.5), no.seeds = 100)
data |
The input, an ExpressionSet object. |
flist |
A ‘list’ of filter functions to apply to the
array. This is passed to the genefilter
function without touching it. Supply NA here if you don't
want to filter the expression set before running ISA on it. |
uniqueEntrez |
Logical scalar, whether to filter the input expression set to keep exactly one probeset for each Entrez gene. Probesets that are not mapped to an Entrez gene are dropped. |
thr.gene |
Numeric vector. The threshold parameters for the ISA,
for features (=probesets or genes). All combinations of
thr.gene and thr.cond
will be used to run ISA. |
thr.cond |
Numeric vector. The threshold parameters for the ISA,
for samples. All combinations of thr.gene and thr.cond
will be used to run ISA. |
no.seeds |
Number of seeds to run ISA from. |
Please read tutorial vignette included in this package for an
introduction on ISA. The isa2-package
manual page in the
isa2
package is also useful.
The ISA
function performs the ISA algorithm on the supplied
expression data. This involves the following steps:
genefilter
package for
this. The default filtering function keeps the features that have
an IQR
of 0.5 or more. See
genefilter
for details on how to create
filtering functions. If NA
is given as the flist
argument, then no filtering is performed.
isa
function in the
isa2
package to perform the Iterative Signature
Algorithm. This itself performs the following steps:
isa.normalize
.
generate.seeds
.
isa.iterate
.
isa.unique
.
isa.filter.robust
in the isa2
package.
ISAModules
object from the ISA
results.
An ISAModules-class
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.
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.
The vignette included in the eisa
package.
library(ALL) data(ALL) modules <- ISA(ALL, thr.gene=2.7, thr.cond=1.4) modules