What you list are no p-value adjustment methods.

Empirical bayes calculates an odds ratio for differential expression (DE) based on an a-priori guess of the proportion of DE genes. The others are methods to calculate p-values.

I suggest simply using limma. It will calculate an empirical bayes statistic and robust p-values which can be adjusted by any method you like. However, for high-throughput screening experiments mostly the correction of Benjamini and Hochberg is applied which can be used to control the false-discovers rate (FDR).

I personally don't think that it is of any use to control error-rates in screening experiments. If it is to select some subset of candidates, then better decide first how large this subset can be to be useful and select this number of genes from an appropriately ranked list. The ranking might be done on p-values or odds-ratios but also on fold-changes. It is also a good idea to have a look at diagnostic plots of the data like volcanoplots and mean-difference plots to see if there might be a group of interesting candidates coming out of the noise and select those.

The test empirical Bayes is essentially a t-test where variance estimates are shrank towards a pooled estimate, resulting in far more stable inference when the number of samples is small. The empiricalBayes test is also much faster than the usual t-test. LPE test is especially well suited for small sample sizes. F-test compares the differences in variance between the two groups, while the other tests compare the means.

cite from http://chipster.csc.fi/manual/stat-two-groups.html

Thank you Jochen Wilhelm. I will check on the helpful Information you provided.

Yes, you are right. They are test methods, not p-value adjustment methods. @Jochen Wilhelm