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

Cope Leslie, Zhong Xiaogang, Garrett Elizabeth and Parmigiani Giovanni
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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

Statistical Applications in Genetics and Molecular Biology, 2004, vol. 3, issue 1, 15

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.

Keywords: gene expression micorarrays; R; validation; meta-analysis (search for similar items in EconPapers)
Date: 2004
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DOI: 10.2202/1544-6115.1046

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