title: Spatial normalization of array-CGH data.
abstract: BACKGROUND
  Array-based comparative genomic hybridization (array-CGH) is a recently developed technique for analyzing changes in DNA copy number. As in all microarray analyses, normalization is required to correct for experimental artifacts while preserving the true biological signal. We investigated various sources of systematic variation in array-CGH data and identified two distinct types of spatial effect of no biological relevance as the predominant experimental artifacts: continuous spatial gradients and local spatial bias. Local spatial bias affects a large proportion of arrays, and has not previously been considered in array-CGH experiments.
  
  
  RESULTS
  We show that existing normalization techniques do not correct these spatial effects properly. We therefore developed an automatic method for the spatial normalization of array-CGH data. This method makes it possible to delineate and to eliminate and/or correct areas affected by spatial bias. It is based on the combination of a spatial segmentation algorithm called NEM (Neighborhood Expectation Maximization) and spatial trend estimation. We defined quality criteria for array-CGH data, demonstrating significant improvements in data quality with our method for three data sets coming from two different platforms (198, 175 and 26 BAC-arrays).
  
  
  CONCLUSIONS
  We have designed an automatic algorithm for the spatial normalization of BAC CGH-array data, preventing the misinterpretation of experimental artifacts as biologically relevant outliers in the genomic profile. This algorithm is implemented in the R package MANOR (Micro-Array NORmalization), which is described at http://bioinfo.curie.fr/projects/manor and available from the Bioconductor site http://www.bioconductor.org. It can also be tested on the CAPweb bioinformatics platform at http://bioinfo.curie.fr/CAPweb.
authors.affiliation: Institut Curie, Service de Bioinformatique, 26, rue d'Ulm, Paris, 75248 cedex 05, France. pierre.neuvial@curie.fr
authors.forename: Pierre
authors.initials: P
authors.lastname: Neuvial
authors.forename: Philippe
authors.initials: P
authors.lastname: Hupé
authors.forename: Isabel
authors.initials: I
authors.lastname: Brito
authors.forename: Stéphane
authors.initials: S
authors.lastname: Liva
authors.forename: Elodie
authors.initials: E
authors.lastname: Manié
authors.forename: Caroline
authors.initials: C
authors.lastname: Brennetot
authors.forename: François
authors.initials: F
authors.lastname: Radvanyi
authors.forename: Alain
authors.initials: A
authors.lastname: Aurias
authors.forename: Emmanuel
authors.initials: E
authors.lastname: Barillot
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001483:Base Sequence; D002874:Chromosome Mapping / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D018628:Gene Dosage; D017403:In Situ Hybridization / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D008969:Molecular Sequence Data; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*