MergeMaid: R Tools for Merging and Cross-Study Validation of Gene Expression Data

Leslie Cope, Xiaogang Zhong, Elizabeth Garrett-Mayer and Giovanni Parmigiani
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Leslie Cope: Departments of Oncology and Biostatistics, Johns Hopkins University
Xiaogang Zhong: Department of Applied Mathematics, Johns Hopkins University
Elizabeth Garrett-Mayer: Departments of Oncology and Biostatistics, Johns Hopkins University
Giovanni Parmigiani: The Sydney Kimmel Comprehensive Cancer Center, Johns Hopkins University & Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health

No 1053, Johns Hopkins University Dept. of Biostatistics Working Paper Series from Berkeley Electronic Press

Abstract: Cross-study validation of gene expression investigations is critical in genomic analysis. We developed an R package and associated object definitions to merge and visualize multiple gene expression datasets. Our merging functions use arbitrary character IDs and generate objects that can efficiently support a variety of joint analyses. Visualization tools support exploration and cross-study validation of the data, without requiring normalization across platforms. Tools include "integrative correlation" plots that is, scatterplots of all pairwise correlations in one study against the corresponding pairwise correlations of another, both for individual genes and all genes combined. Gene-specific plots can be used to identify genes whose changes are reliably measured across studies. Visualizations also include scatterplots of gene-specific statistics quantifying relationships between expression and phenotypes of interest, using linear, logistic and Cox regression. Availability: Free open source from url http://www.bioconductor.org. Contact: Xiaogang Zhong zhong@ams.jhu.edu Supplementary information: Documentation available with the package.

Date: 2004-08-30
Note: oai:bepress.com:jhubiostat-1053
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