Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Journal Article

stam - a Bioconductor compliant R package for structured analysis of microarray data


Lottaz,  Claudio
Max Planck Society;


Spang,  Rainer
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

(Any fulltext), 350KB

Supplementary Material (public)
There is no public supplementary material available

Lottaz, C., & Spang, R. (2005). stam - a Bioconductor compliant R package for structured analysis of microarray data. BMC Bioinformatics, 6(1), 1971-1978. doi:10.1186/1471-2105-6-211.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0010-85B5-2
Genome wide microarray studies have the potential to unveil novel disease entities. Clinically homogeneous groups of patients can have diverse gene expression profiles. The definition of novel subclasses based on gene expression is a difficult problem not addressed systematically by currently available software tools. Results We present a computational tool for semi-supervised molecular disease entity detection. It automatically discovers molecular heterogeneities in phenotypically defined disease entities and suggests alternative molecular sub-entities of clinical phenotypes. This is done using both gene expression data and functional gene annotations. We provide stam, a Bioconductor compliant software package for the statistical programming environment R. We demonstrate that our tool detects gene expression patterns, which are characteristic for only a subset of patients from an established disease entity. We call such expression patterns molecular symptoms. Furthermore, stam finds novel sub-group stratifications of patients according to the absence or presence of molecular symptoms. Conclusion Our software is easy to install and can be applied to a wide range of datasets. It provides the potential to reveal so far indistinguishable patient sub-groups of clinical relevance.