title: MSMAD: a computationally efficient method for the analysis of noisy array CGH data.
abstract: BACKGROUND
  Genome analysis has become one of the most important tools for understanding the complex process of cancerogenesis. With increasing resolution of CGH arrays, the demand for computationally efficient algorithms arises, which are effective in the detection of aberrations even in very noisy data.
  
  
  RESULTS
  We developed a rather simple, non-parametric technique of high computational efficiency for CGH array analysis that adopts a median absolute deviation concept for breakpoint detection, comprising median smoothing for pre-processing. The resulting algorithm has the potential to outperform any single smoothing approach as well as several recently proposed segmentation techniques. We show its performance through the application of simulated and real datasets in comparison to three other methods for array CGH analysis.
  
  
  METHODS
  Our approach is implemented in the R-language and environment for statistical computing (version 2.6.1 for Windows, R-project, 2007). The code is available at: http://www.iba.muni.cz/~budinska/msmad.html.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Institute of Biostatistics and Analyses, Masaryk University, Kamenice 126/3, 625 00 Brno, Czech Republic. budinska@iba.muni.cz
authors.forename: Eva
authors.initials: E
authors.lastname: Budinska
authors.forename: Eva
authors.initials: E
authors.lastname: Gelnarova
authors.forename: Michael G
authors.initials: MG
authors.lastname: Schimek
mesh_terms: D000465:Algorithms; D055028:Comparative Genomic Hybridization / Q000379:methods*; D019295:Computational Biology / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D011381:Programming Languages