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

Claudio Lottaz1, Rainer Spang

  • 1Max Planck Institute for Molecular Genetics, Berlin Center for Genome Based Bioinformatics, Ihnestr. 73, D-14195 Berlin, Germany. Claudio.Lottaz@molgen.mpg.de

BMC Bioinformatics
|August 27, 2005
PubMed
Summary

This study introduces stam, a novel R software package for discovering new disease subtypes using gene expression data. It identifies molecular symptoms to stratify patients, potentially revealing clinically relevant subgroups.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Genome-wide microarray studies can identify novel disease entities.
  • Phenotypically similar patient groups often exhibit diverse gene expression profiles.
  • Discovering gene expression-based subclasses is challenging for current software.

Purpose of the Study:

  • To develop a computational tool for semi-supervised detection of molecular disease entities.
  • To automatically identify molecular heterogeneities within phenotypically defined diseases.
  • To suggest alternative molecular sub-entities based on gene expression and functional annotations.

Main Methods:

  • Developed 'stam', a Bioconductor-compliant software package for R.
  • Utilized gene expression data and functional gene annotations for analysis.

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  • Employed semi-supervised learning for molecular sub-entity detection.

    • Main Results:

    • The tool, 'stam', identifies molecular symptoms (gene expression patterns specific to patient subsets).
    • It successfully detects gene expression patterns characteristic of only a subset of patients within an established disease.
    • Discovered novel patient subgroup stratifications based on the presence or absence of molecular symptoms.

    Conclusions:

    • The 'stam' software is user-friendly and easily installable.
    • It can be applied to diverse biological datasets.
    • Offers potential for uncovering clinically relevant, previously indistinguishable patient subgroups.