An evaluation of statistical methods for DNA methylation microarray data analysis.

ABSTRACT:

Background

DNA methylation offers an excellent example for elucidating how epigenetic information affects gene expression. β values and M values are commonly used to quantify DNA methylation. Statistical methods applicable to DNA methylation data analysis span a number of approaches such as Wilcoxon rank sum test, t-test, Kolmogorov-Smirnov test, permutation test, empirical Bayes method, and bump hunting method. Nonetheless, selection of an optimal statistical method can be challenging when different methods generate inconsistent results from the same data set.

Results

We compared six statistical approaches relevant to DNA methylation microarray analysis in terms of false discovery rate control, statistical power, and stability through simulation studies and real data exampl

SUBMITTER: Li D 

PROVIDER: S-EPMC4497424 | biostudies-literature | 2015 Jul

REPOSITORIES: biostudies-literature