Modular analysis of gene expression data with R

SUMMARY Large sets of data, such as expression profiles from many samples, require analytic tools to reduce their complexity. The Iterative Signature Algorithm (ISA) is a biclustering algorithm. It was designed to decompose a large set of data 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. In this short note, we introduce two BioConductor software packages written in GNU R: The isa2 package includes an optimized implementation of the ISA and the eisa package provides a convenient interface to run the ISA, visualize its output and put the biclusters into biological context. Potential users of these packages are all R and BioConductor users dealing with tabular (e.g. gene expression) data. AVAILABILITY http://www.unil.ch/cbg/ISA CONTACT: sven.bergmann@unil.ch

[1]  Ann M. Hess,et al.  which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Filtering for increased power for microarray data analysis , 2008 .

[2]  Andrea Califano,et al.  Analysis of Gene Expression Microarrays for Phenotype Classification , 2000, ISMB.

[3]  Sven Bergmann,et al.  Iterative signature algorithm for the analysis of large-scale gene expression data. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Roded Sharan,et al.  Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[5]  Sven Bergmann,et al.  Challenges and prospects in the analysis of large-scale gene expression data , 2004, Briefings Bioinform..

[6]  Cheng Li,et al.  Adjusting batch effects in microarray expression data using empirical Bayes methods. , 2007, Biostatistics.

[7]  Eckart Zitzler,et al.  BicAT: a biclustering analysis toolbox , 2006, Bioinform..

[8]  R. Gentleman,et al.  Gene expression profile of adult T-cell acute lymphocytic leukemia identifies distinct subsets of patients with different response to therapy and survival. , 2004, Blood.

[9]  G. Getz,et al.  Coupled two-way clustering analysis of gene microarray data. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[10]  George M. Church,et al.  Biclustering of Expression Data , 2000, ISMB.

[11]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[12]  Sven Bergmann,et al.  Defining transcription modules using large-scale gene expression data , 2004, Bioinform..