Markers improve clustering of CGH data.
BACKGROUND
We consider the problem of clustering a population of Comparative Genomic Hybridization (CGH) data samples using similarity based clustering methods. A key requirement for clustering is to avoid using the noisy aberrations in the CGH samples.


RESULTS
We develop a dynamic programming algorithm to identify a small set of important genomic intervals called markers. The advantage of using these markers is that the potentially noisy genomic intervals are excluded during the clustering process. We also develop two clustering strategies using these markers. The first one, prototype-based approach, maximizes the support for the markers. The second one, similarity-based approach, develops a new similarity measure called RSim and refines clusters with the aim of maximizing the RSim measure between the samples in the same cluster. Our results demonstrate that the markers we found represent the aberration patterns of cancer types well and they improve the quality of clustering significantly.


BACKGROUND
All software developed in this paper and all the datasets used are available from the authors upon request.
Liu
Jun
J
Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA. juliu@cise.ufl.edu
Ranka
Sanjay
S
Kahveci
Tamer
T
D014408:Biomarkers, Tumor / Q000235:genetics*; D002874:Chromosome Mapping / Q000379:methods; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D005819:Genetic Markers / Q000235:genetics*; D006801:Humans; D017403:In Situ Hybridization / Q000379:methods; D005810:Multigene Family / Q000235:genetics; D009363:Neoplasm Proteins / Q000032:analysis / Q000235:genetics*; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*
D014408:Biomarkers, Tumor; D005819:Genetic Markers; D009363:Neoplasm Proteins