Two-part permutation tests for DNA methylation and microarray data.
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.
Neuhäuser
Markus
M
Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Hufelandstr, 55, D-45122 Essen, Germany. markus.neuhaeuser@medizin.uni-essen.de
Boes
Tanja
T
Jöckel
Karl-Heinz
KH
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
D004247:DNA
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