title: Two-part permutation tests for DNA methylation and microarray data.
abstract: BACKGROUND
  One important application of microarray experiments is to identify differentially expressed genes. Often, small and negative expression levels were clipped-off to be equal to an arbitrarily chosen cutoff value before a statistical test is carried out. Then, there are two types of data: truncated values and original observations. The truncated values are not just another point on the continuum of possible values and, therefore, it is appropriate to combine two statistical tests in a two-part model rather than using standard statistical methods. A similar situation occurs when DNA methylation data are investigated. In that case, there are null values (undetectable methylation) and observed positive values. For these data, we propose a two-part permutation test.
  
  
  RESULTS
  The proposed permutation test leads to smaller p-values in comparison to the original two-part test. We found this for both DNA methylation data and microarray data. With a simulation study we confirmed this result and could show that the two-part permutation test is, on average, more powerful. The new test also reduces, without any loss of power, to a standard test when there are no null or truncated values.
  
  
  CONCLUSIONS
  The two-part permutation test can be used in routine analyses since it reduces to a standard test when there are positive values only. Further advantages of the new test are that it opens the possibility to use other test statistics to construct the two-part test and that it avoids the use of any asymptotic distribution. The latter advantage is particularly important for the analysis of microarrays since sample sizes are usually small.
authors.affiliation: Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Hufelandstr, 55, D-45122 Essen, Germany. markus.neuhaeuser@medizin.uni-essen.de
authors.forename: Markus
authors.initials: M
authors.lastname: Neuhäuser
authors.forename: Tanja
authors.initials: T
authors.lastname: Boes
authors.forename: Karl-Heinz
authors.initials: KH
authors.lastname: Jöckel
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004247:DNA / Q000737:chemistry; D019175:DNA Methylation; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D015233:Models, Statistical; D009010:Monte Carlo Method; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D018401:Sample Size; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA; D012984:Software; D013997:Time Factors
chemical_list: D004247:DNA
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