ISA

Large sets of data, like expression profile from many samples, require analytic tools to reduce their complexity. Classical (bi-)clustering algorithms typically attribute elements (genes, arrays) to disjoint groups ("clusters"). Yet, in some cases overlapping cluster assignments would suit the biological reality much better.

The Iterative Signature Algorithm (ISA) was designed to overcome this and other limitations of standard clustering algorithms. It aims to reduce the complexity of very large sets of data by decomposing it into so-called "modules". In the context of gene expression data these modules consist of subsets of genes that exhibit a coherent expression profile only over a subset of microarray experiments. Genes and arrays may be attributed to multiple modules and the level of required coherence can be varied resulting in different "resolutions" of the modular mapping. Since the ISA does not rely on the computation of correlation matrices (like many other tools), it is extremely fast even for very large datasets.

Software

Gene expression data

We developed the eisa GNU R package to facilitate the modular analysis of gene expression data. The package uses standard BioConductor data structures and includes various visualization tools as well.

Requirements

To use eisa you will need a working GNU R and BioConductor installation. You will also need the isa2, Category and genefilter R packages. You can install these by typing

 install.packages("isa2")
 source("http://bioconductor.org/biocLite.R")
 biocLite(c("Category", "genefilter"))

at your R prompt.

Download and Installation

The eisa package is currently being reviewed by the BioConductor team. Until it is available from the standard BioConductor repositories, it can be downloaded from here. The most recent version of the eisa package is 0.2. Please follow the installation instructions for your platform.

• Microsoft Windows (all versions)
  Download this file], save it in a temporary directory, and then start R. From the Packages menu choose Install packages from local zip files and select the saved file.
• Mac OSX (all versions)
  Currently not available.
• Linux and Unix systems
  Download this file], save it in a temporary directory, and start R. Install the downloaded package using the install.packages() function: give the full path of the saved file and use the repos=NULL argument of install.packages().

License

The eisa package is licensed under the GNU General Public License, version 2 or later. For details, see http://www.gnu.org/licenses/old-licenses/gpl-2.0.html.

Other tabular data

The ISA can be applied to identify coherent substructures (i.e. modules) from any rectangular matrix of data. You can use the isa2 R package for such an analysis.

Installation

The isa2 package is available from CRAN, the standard R package repository. You can install it on any platform that is supported by GNU R, e.g. Microsoft Windows, Mac OSX and Linux systems. To install it, start R and type in

 install.packages("isa2")

at the prompt. On Linux and Unix-like systems, you will need a working C compiler for a successful installation.

License

The isa2 package is licensed under the Creative Commons Attribution-Noncommercial-Share Alike 3.0 License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/ or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA.

Papers

<biblio>

1. Ihmels2004 pmid=15606968 // PDF
2. Ihmels2004a pmid=15044247 // PDF
3. Bergmann2004 pmid=14737187 // PDF
4. Ihmels2002 pmid=12134151 // PDF

</biblio>

Tutorials

• ISA tutorial
• Running ISA in parallel
• EISA tutorial
• EISA module trees
• EISA and the biclust package