Difference between revisions of "ISA"

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[[image:expmat.png|An ISA transcription module|300px|right|link=ISA]]
Large sets of data, like expression profile from many samples, require
Large sets of data, like expression profile from many samples, require
analytic tools to reduce their complexity. Classical (bi-)clustering
analytic tools to reduce their complexity. Classical (bi-)clustering

Revision as of 11:38, 6 December 2009

An ISA transcription module

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 for 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.


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

 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, R source package
    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().


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.

Software for any 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.


No additional R package is required to install and use isa2. But on Linux and Unix systems you will need a C compiler to install it. E.g. on Ubuntu Linux you will need to install the 'build-essential package.


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


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


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.


The Iterative Signature Algorithm for Gene Expression Data

Shows the typical steps of modular analysis, from loading you expression data to the visualization of transcription modules.
HTML PDF Rnw R code

ISA and the biclust package

The biclust package implements several biclustering algorithms. It is possible to convert the results of biclust to transcription modules and vice-versa.
HTML PDF Rnw R code

Hierarchical module trees

A module tree is the hierarchical modular organization of a data set.
HTML PDF Rnw R code

The Iterative Signature Algorithm

Tutorial for the analysis of tabular data with the isa2 R package.
HTML PDF Rnw R code

Running ISA in parallel

Shows how to run ISA on a computer cluster or multi-processor machine, using MPI and the Rmpi and snow R packages.
HTML PDF Rnw R code



  1. Ihmels2004 pmid=15606968 // PDF
  2. Ihmels2004a pmid=15044247 // PDF
  3. Bergmann2004 pmid=14737187 // PDF
  4. Bergmann2003 pmid=12689096 //PDF
  5. Ihmels2002 pmid=12134151 // PDF