A comparison of cluster analysis methods using DNA methylation data.
BACKGROUND
Aberrant DNA methylation is common in cancer. DNA methylation profiles differ between tumor types and subtypes and provide a powerful diagnostic tool for identifying clusters of samples and/or genes. DNA methylation data obtained with the quantitative, highly sensitive MethyLight technology is not normally distributed; it frequently contains an excess of zeros. Established tools to analyze this type of data do not exist. Here, we evaluate a variety of methods for cluster analysis to determine which is most reliable.


RESULTS
We introduce a Bernoulli-lognormal mixture model for clustering DNA methylation data obtained using MethyLight. We model the outcomes using a two-part distribution having discrete and continuous components. It is compared with standard cluster analysis approaches for continuous data and for discrete data. In a simulation study, we find that the two-part model has the lowest classification error rate for mixture outcome data compared with other approaches. The methods are illustrated using DNA methylation data from a study of lung cancer cell lines. Compared with competing hierarchical clustering methods, the mixture model approaches have the lowest cross-validation error for detecting lung cancer subtype (non-small versus small cell). The Bernoulli-lognormal mixture assigns observations to subgroups with the lowest uncertainty.


BACKGROUND
Software is available upon request from the authors.


BACKGROUND
http://www-rcf.usc.edu/~kims/SupplementaryInfo.html
Siegmund
Kimberly D
KD
Department of Preventive Medicine, Norris Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles 90089, USA. kims@usc.edu
Laird
Peter W
PW
Laird-Offringa
Ite A
IA
D000465:Algorithms; D016000:Cluster Analysis; D018899:CpG Islands / Q000235:genetics*; D019175:DNA Methylation; D004273:DNA, Neoplasm / Q000145:classification / Q000235:genetics; D005820:Genetic Testing / Q000379:methods; D006801:Humans; D008175:Lung Neoplasms / Q000145:classification* / Q000175:diagnosis / Q000235:genetics*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software
D004273:DNA, Neoplasm