title: Merging two gene-expression studies via cross-platform normalization.
abstract: BACKGROUND
  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.
  
  
  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.
  
  
  BACKGROUND
  The supplementary materials and XPN Matlab code are publicly available at website: https://genome.unc.edu/xpn
authors.affiliation: Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, NC, USA. shabalin@email.unc.edu
authors.forename: Andrey A
authors.initials: AA
authors.lastname: Shabalin
authors.forename: Håkon
authors.initials: H
authors.lastname: Tjelmeland
authors.forename: Cheng
authors.initials: C
authors.lastname: Fan
authors.forename: Charles M
authors.initials: CM
authors.lastname: Perou
authors.forename: Andrew B
authors.initials: AB
authors.lastname: Nobel
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods* / Q000592:standards; D005810:Multigene Family / Q000502:physiology*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000592:standards; D012016:Reference Values