title: Fusing microarray experiments with multivariate regression.
abstract: BACKGROUND
  It is widely acknowledged that microarray data are subject to high noise levels and results are often platform dependent. Therefore, microarray experiments should be replicated several times and in several laboratories before the results can be relied upon. To make the best use of such extensive datasets, methods for microarray data fusion are required. Ideally, the fused data should distil important aspects of the data while suppressing unwanted sources of variation and be amenable to further informal and formal methods of analysis. Also, the variability in the quality of experimentation should be taken into account.
  
  
  RESULTS
  We present such an approach to data fusion, based on multivariate regression. We apply our methodology to data from a previous study on cell-cycle control in Schizosaccharomyces pombe.
  
  
  BACKGROUND
  The algorithm implemented in R is freely available from the authors on request.
authors.affiliation: MRC Biostatistics Unit, Cambridge, UK. wally.gilks@mrc-bsu.cam.ac.uk
authors.forename: Walter R
authors.initials: WR
authors.lastname: Gilks
authors.forename: Brian D M
authors.initials: BD
authors.lastname: Tom
authors.forename: Alvis
authors.initials: A
authors.lastname: Brazma
mesh_terms: D000465:Algorithms; D002453:Cell Cycle / Q000502:physiology; D018797:Cell Cycle Proteins / Q000378:metabolism; D003627:Data Interpretation, Statistical; D003628:Database Management Systems; D030562:Databases, Protein; D020869:Gene Expression Profiling / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D015999:Multivariate Analysis; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012044:Regression Analysis; D012568:Schizosaccharomyces / Q000166:cytology* / Q000378:metabolism*; D018511:Systems Integration
chemical_list: D018797:Cell Cycle Proteins