Motivation: Gene-expression microarrays are currently being applied in a variety of biomedical applications. This article considers the problem of how to merge datasets arising from different gene-expression studies of a common organism and phenotype. Of particular interest is how to merge data from different technological platforms.#R##N##R##N#Results: The article makes two contributions to the problem. The first is a simple cross-study normalization method, which is based on linked gene/sample clustering of the given datasets. The second is the introduction and description of several general validation measures that can be used to assess and compare cross-study normalization methods. The proposed normalization method is applied to three existing breast cancer datasets, and is compared to several competing normalization methods using the proposed validation measures.#R##N##R##N#Availability: The supplementary materials and XPN Matlab code are publicly available at website: https://genome.unc.edu/xpn#R##N##R##N#Contact: shabalin@email.unc.edu#R##N##R##N#Supplementary information: Supplementary data are available at Bioinformatics online.
['Andrey A. Shabalin', 'Håkon Tjelmeland', 'Cheng Fan', 'Charles M. Perou', 'Andrew B. Nobel']
Merging two gene-expression studies via cross-platform normalization