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gCMAP: user-friendly connectivity mapping with R.

Thomas Sandmann1, Sarah K Kummerfeld, Robert Gentleman

  • 1Department of Bioinformatics and Computational Biology, Genentech Inc., South San Francisco, CA 94080, USA.

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New R packages, gCMAP and gCMAPWeb, enable the creation and querying of connectivity maps. These tools link disease phenotypes to drug effects by analyzing gene expression patterns for reproducible research.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Identifying connections between disease phenotypes and drug effects is crucial for therapeutic development.
  • Differential gene expression patterns offer a molecular basis for understanding these connections.
  • Existing methods for analyzing transcriptomic data can be complex and require specialized tools.

Purpose of the Study:

  • To introduce gCMAP and gCMAPWeb, two R packages designed for constructing and querying connectivity maps.
  • To provide a standardized framework for processing and analyzing user-defined differential gene expression datasets.
  • To facilitate reproducible research through automated reporting and a user-friendly web application.

Main Methods:

  • Development of two R packages, gCMAP and gCMAPWeb.
  • Standardized processing of microarray and RNAseq data.
  • Integration of gene set enrichment methods for data interrogation.
  • Implementation of an easy-to-deploy web application for report generation.

Main Results:

  • gCMAP and gCMAPWeb provide a comprehensive framework for building and querying transcriptomic connectivity maps.
  • The packages standardize data processing and analysis, enabling robust comparisons.
  • The web application simplifies the generation of graphical and tabular reports, enhancing reproducibility.

Conclusions:

  • gCMAP and gCMAPWeb offer a powerful and accessible solution for exploring gene expression-based biological connections.
  • These tools advance the discovery of commonalities between disease phenotypes and drug responses.
  • The integrated framework supports reproducible research in transcriptomics and drug discovery.