Can one compare Beta values of methylation, such as those found at TCGA, using t-Test? This qutation is from from: An evaluation of statistical methods for DNA methylation microarray data analysis, BMC Bioinformatics. 2015; 16: 217.

Currently available methylation differential analysis methods implemented in Bioconductor/R include several approaches such as Wilcoxon rank sum test (used in methyAnalysis package), t-test (used in methyAnalysis, CpGAssoc, RnBeads, and IMA package), Kolmogorov-Smirnov Tests (although not implemented in packages, but used by some investigators [10]), permutation test (used in CpGAssoc package), empirical Bayes method (used in RnBeads, IMA and minfi package), and bump hunting method (used in bumphunter and minfi package).

What I mean is, if beta values of condition a are c(0.5,0.5,0.5,0.50001) are these considered different from c(0.51,0.51, 0.51, 0.51)?

while

```
> t.test( c(0.5,0.5,0.5,0.50001), c(0.51,0.51, 0.51, 0.51))$p.value
[1] 3.44839e-11
```

but we know that beta value of 0.5 itself means data are heterogeneous??

Thank you Kevin Blighe. I'll go for non-parametric tests to compare beta values. Another question: is there a threshold (like |logFC|>1 in determining DEGs) used for diffrentially methylated regions/genes or does statistical tests suffice? As when I analyse methylation data with GEO2R I get logFC among other columns.

I have seen people use a difference in mean of 0.1, 0.15, and 0.2. There is no standard, though. You should definitely filter on both: