title: Cluster analysis for DNA methylation profiles having a detection threshold.
abstract: BACKGROUND
  DNA methylation, a molecular feature used to investigate tumor heterogeneity, can be measured on many genomic regions using the MethyLight technology. Due to the combination of the underlying biology of DNA methylation and the MethyLight technology, the measurements, while being generated on a continuous scale, have a large number of 0 values. This suggests that conventional clustering methodology may not perform well on this data.
  
  
  RESULTS
  We compare performance of existing methodology (such as k-means) with two novel methods that explicitly allow for the preponderance of values at 0. We also consider how the ability to successfully cluster such data depends upon the number of informative genes for which methylation is measured and the correlation structure of the methylation values for those genes. We show that when data is collected for a sufficient number of genes, our models do improve clustering performance compared to methods, such as k-means, that do not explicitly respect the supposed biological realities of the situation.
  
  
  CONCLUSIONS
  The performance of analysis methods depends upon how well the assumptions of those methods reflect the properties of the data being analyzed. Differing technologies will lead to data with differing properties, and should therefore be analyzed differently. Consequently, it is prudent to give thought to what the properties of the data are likely to be, and which analysis method might therefore be likely to best capture those properties.
authors.affiliation: Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA. pmarjora@usc.edu
authors.forename: Paul
authors.initials: P
authors.lastname: Marjoram
authors.forename: Jing
authors.initials: J
authors.lastname: Chang
authors.forename: Peter W
authors.initials: PW
authors.lastname: Laird
authors.forename: Kimberly D
authors.initials: KD
authors.lastname: Siegmund
mesh_terms: D001185:Artificial Intelligence; D001483:Base Sequence; D016000:Cluster Analysis; D019175:DNA Methylation; D004056:Differential Threshold; D020869:Gene Expression Profiling / Q000379:methods*; D008969:Molecular Sequence Data; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*