title: Improved analysis of bacterial CGH data beyond the log-ratio paradigm.
abstract: BACKGROUND
  Existing methods for analyzing bacterial CGH data from two-color arrays are based on log-ratios only, a paradigm inherited from expression studies. We propose an alternative approach, where microarray signals are used in a different way and sequence identity is predicted using a supervised learning approach.
  
  
  RESULTS
  A data set containing 32 hybridizations of sequenced versus sequenced genomes have been used to test and compare methods. A ROC-analysis has been performed to illustrate the ability to rank probes with respect to Present/Absent calls. Classification into Present and Absent is compared with that of a gaussian mixture model.
  
  
  CONCLUSIONS
  The results indicate our proposed method is an improvement of existing methods with respect to ranking and classification of probes, especially for multi-genome arrays.
authors.affiliation: Biostatistics, Department of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, As, Norway. lars.snipen@umb.no
authors.forename: Lars
authors.initials: L
authors.lastname: Snipen
authors.forename: Otto L
authors.initials: OL
authors.lastname: Nyquist
authors.forename: Margrete
authors.initials: M
authors.lastname: Solheim
authors.forename: Agot
authors.initials: A
authors.lastname: Aakra
authors.forename: Ingolf F
authors.initials: IF
authors.lastname: Nes
mesh_terms: D055028:Comparative Genomic Hybridization; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods; D016680:Genome, Bacterial; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D012372:ROC Curve