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

Author

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  • Cope Leslie

    (The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins)

  • Zhong Xiaogang

    (Department of Applied Mathematics and Statistics, Johns Hopkins University)

  • Garrett Elizabeth

    (The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins)

  • Parmigiani Giovanni

    (The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins)

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.

Suggested Citation

  • Cope Leslie & Zhong Xiaogang & Garrett Elizabeth & Parmigiani Giovanni, 2004. "MergeMaid: R Tools for Merging and Cross-Study Validation of Gene Expression Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-15, October.
    • Handle: RePEc:bpj:sagmbi:v:3:y:2004:i:1:n:29
    DOI: 10.2202/1544-6115.1046
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