MergeMaid: R tools for merging and cross-study validation of gene expression data

Statistical Applications in Genetics and Molecular Biology
Leslie CopeGiovanni Parmigiani

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.

Citations

May 24, 2008·Environmental Monitoring and Assessment·Yuying DongCheng Sun
Nov 17, 2007·BMC Bioinformatics·Kellie J ArcherCarleton T Garrett
Dec 25, 2010·Genome Biology·Xiangqun Zheng-BradleyAlvis Brazma
Jun 10, 2009·Bioinformatics·Audrey KauffmannWolfgang Huber
Jul 29, 2020·Genome Research·Tanvir AlamMichiel J L de Hoon
Jul 6, 2010·Clinical Cancer Research : an Official Journal of the American Association for Cancer Research·Windy Berkofsky-FesslerJonathan D Licht
Feb 7, 2021·Journal of Psychiatric Research·Brenda Cabrera-MendozaCristóbal Fresno
Mar 4, 2014·The Annals of Applied Statistics·Edoardo M AiroldiXiaodong Lin

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