title: Distance-based clustering of CGH data.
abstract: BACKGROUND
  We consider the problem of clustering a population of Comparative Genomic Hybridization (CGH) data samples. The goal is to develop a systematic way of placing patients with similar CGH imbalance profiles into the same cluster. Our expectation is that patients with the same cancer types will generally belong to the same cluster as their underlying CGH profiles will be similar.
  
  
  RESULTS
  We focus on distance-based clustering strategies. We do this in two steps. (1) Distances of all pairs of CGH samples are computed. (2) CGH samples are clustered based on this distance. We develop three pairwise distance/similarity measures, namely raw, cosine and sim. Raw measure disregards correlation between contiguous genomic intervals. It compares the aberrations in each genomic interval separately. The remaining measures assume that consecutive genomic intervals may be correlated. Cosine maps pairs of CGH samples into vectors in a high-dimensional space and measures the angle between them. Sim measures the number of independent common aberrations. We test our distance/similarity measures on three well known clustering algorithms, bottom-up, top-down and k-means with and without centroid shrinking. Our results show that sim consistently performs better than the remaining measures. This indicates that the correlation of neighboring genomic intervals should be considered in the structural analysis of CGH datasets. The combination of sim with top-down clustering emerged as the best approach.
  
  
  BACKGROUND
  All software developed in this article and all the datasets are available from the authors upon request.
  
  
  BACKGROUND
  juliu@cise.ufl.edu.
authors.affiliation: Computer and Information Science and Engineering, University of Florida Gainesville, FL 32611, USA. juliu@cise.ufl.edu
authors.forename: Jun
authors.initials: J
authors.lastname: Liu
authors.forename: Jaaved
authors.initials: J
authors.lastname: Mohammed
authors.forename: James
authors.initials: J
authors.lastname: Carter
authors.forename: Sanjay
authors.initials: S
authors.lastname: Ranka
authors.forename: Tamer
authors.initials: T
authors.lastname: Kahveci
authors.forename: Michael
authors.initials: M
authors.lastname: Baudis
mesh_terms: D000465:Algorithms; D000818:Animals; D002869:Chromosome Aberrations; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D004273:DNA, Neoplasm; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D023281:Genomics / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000378:metabolism; D009693:Nucleic Acid Hybridization; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D012984:Software
chemical_list: D004273:DNA, Neoplasm