ISA: Iterative Signature Algorithm on Gene Expression data

In eisa: Expression data analysis via the Iterative Signature Algorithm

Description

Run ISA on an ExpressionSet with the default parameters.

Usage

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) 

Arguments

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.

Details

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:

1. Filtering the features (i.e. probe sets) according to their variance. You will need the 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.

2. Filtering the features by mapping them to Entrez genes. Features that do not map to Entrez genes are removed from the data set. If more features map to the same Entrez gene, then only the one with the highest variance will be kept.

3. Calling the isa function in the isa2 package to perform the Iterative Signature Algorithm. This itself performs the following steps:

   1. Normalizing the data by calling isa.normalize.

   2. Generating random input seeds via generate.seeds.

   3. Running ISA with all combinations of given feature and sample thresholds, by calling isa.iterate.

   4. Merging similar modules, separately for each threshold combination, by calling isa.unique.

   5. Filtering the modules separately for each threshold combination, by calling isa.filter.robust in the isa2 package.

   6. Putting all modules from the runs with different thresholds into a single object.

   7. Merging similar modules, across all threshold combinations, if two modules are similar, then the one with the milder thresholds is kept.

4. Creates an ISAModules object from the ISA results.

Value

An ISAModules-class object.

Author(s)

Gabor Csardi [e-mail]

References

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.

See Also

The vignette included in the eisa package.

Examples

library(ALL)
data(ALL)
modules <- ISA(ALL, thr.gene=2.7, thr.cond=1.4)
modules

eisa documentation built on Nov. 8, 2020, 6:47 p.m.