A versatile statistical analysis algorithm to detect genome copy number variation

Raoul Sam Daruwala, Archisman Rudra, Harry Ostrer, Robert Lucito, Michael Wigler, Bud Mishra

Research output: Contribution to journalArticlepeer-review


We have developed a versatile statistical analysis algorithm for the detection of genomic aberrations in human cancer cell lines. The algorithm analyzes genomic data obtained from a variety of array technologies, such as oligonucleotide array, bacterial artificial chromosome array, or array-based comparative genomic hybridization, that operate by hybridizing with genomic material obtained from cancer and normal cells and allow detection of regions of the genome with altered copy number. The number of probes (i.e., resolution), the amount of uncharacterized noise per probe, and the severity of chromosomal aberrations per chromosomal region may vary with the underlying technology, biological sample, and sample preparation. Constrained by these uncertainties, our algorithm aims at robustness by using a priorless maximum a posteriori estimator and at efficiency by a dynamic programming implementation. We illustrate these characteristics of our algorithm by applying it to data obtained from representational oligonucleotide microarray analysis and array-based comparative genomic hybridization technology as well as to synthetic data obtained from an artificial model whose properties can be varied computationally. The algorithm can combine data from multiple sources and thus facilitate the discovery of genes and markers important in cancer, as well as the discovery of loci important in inherited genetic disease.

Original languageEnglish (US)
Pages (from-to)16292-16297
Number of pages6
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number46
StatePublished - Nov 16 2004


  • Array-based comparative genomic hybridization
  • Copy-number fluctuations
  • Maximum a posteriori estimator

ASJC Scopus subject areas

  • General


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