Accurate confidence aware clustering of array CGH tumor profiles.
BACKGROUND
Chromosomal aberrations tend to be characteristic for given (sub)types of cancer. Such aberrations can be detected with array comparative genomic hybridization (aCGH). Clustering aCGH tumor profiles aids in identifying chromosomal regions of interest and provides useful diagnostic information on the cancer type. An important issue here is to what extent individual aCGH tumor profiles can be reliably assigned to clusters associated with a given cancer type.


RESULTS
We introduce a novel evolutionary fuzzy clustering (EFC) algorithm, which is able to deal with overlapping clusterings. Our method assesses these overlaps by using cluster membership degrees, which we use here as a confidence measure for individual samples to be assigned to a given tumor type. We first demonstrate the usefulness of our method using a synthetic aCGH dataset and subsequently show that EFC outperforms existing methods on four real datasets of aCGH tumor profiles involving four different cancer types. We also show that in general best performance is obtained using 1- Pearson correlation coefficient as a distance measure and that extra preprocessing steps, such as segmentation and calling, lead to decreased clustering performance.


BACKGROUND
The source code of the program is available from http://ibi.vu.nl/programs/efcwww
Centre for Integrative Bioinformatics VU (IBIVU), Faculty of Sciences and Faculty of Earth and Life Sciences, VU University Amsterdam, De Boelelaan 1081A, 1081 HV Amsterdam, The Netherlands.
Bart P P
BP
van Houte
Jaap
J
Heringa
D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D016000:Cluster Analysis; D016001:Confidence Intervals; D006801:Humans; D017403:In Situ Hybridization / Q000379:methods*; D009369:Neoplasms / Q000175:diagnosis* / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity