Hi,

My experimental design is as follows: 1 factor with 4 levels and want to consider batch effects. My approach is to considerate the design as ( full= ~ Batch + Condition, reduced = ~ Batch).

My question now is that I'm not quite sure if betaPrior for LRT test should be TRUE or FALSE. If I do not include the betaPrior in the function

dds1 <- DESeq(dds, test="LRT", full= ~ Batch + Condition, reduced = ~ Batch )

the MA-plot appears with unshrunken results.

plotMA(dds1, main= "DESeq2", ylim = c(-2,2))

However if I do apply betaPrior TRUE:

dds2 <- DESeq(dds, test="LRT", betaPrior = TRUE, full= ~ Batch + Condition, reduced = ~ Batch )

then the MA plot appears as the shrunken data

plotMA(dds2, main= "DESeq2", ylim = c(-2,2))

Using the help I get this: "whether or not to put a zero-mean normal prior on the non-intercept coefficients While the beta prior is used typically, for the Wald test, it can also be specified for the likelihood ratio test. For more details on the calculation, see `nbinomWaldTest"`

.

So it seems that for LRT the betaPrior is not set as Default. Could you please clarify why betaPrior is only used in the Wald test and when (if ever) should the betaPrior be used for LRT?

Thanks!