I've received a couple questions from users about why I moved the shrinkage of log2 fold changes from the DESeq() to the lfcShrink() function. Here's my answer I give in the vignette:

"In version 1.16, the log2 fold change shrinkage is no longer default for the DESeq and nbinomWaldTest functions, by setting the defaults of these to betaPrior=FALSE, and by introducing a separate function lfcShrink, which performs log2 fold change shrinkage for visualization and ranking of genes. While for the majority of bulk RNA-seq experiments, the LFC shrinkage did not affect statistical testing, DESeq2 has become used as an inference engine by a wider community, and certain sequencing datasets show better performance with the testing separated from the use of the LFC prior. Also, the separation of LFC shrinkage to a separate function lfcShrink allows for easier methods development of alternative effect size estimators."

As shown in the new vignette, the following code produces moderated log2 fold changes:

dds <- DESeq(dds)
resultsNames(dds)
res <- lfcShrink(dds, coef=2)
plotMA(res)

Note: If you have version 1.16 (no longer release), you need to include res=res

res <- results(dds)
res <- ​lfcShrink(dds, coef=2, res=res)

The last reason given above was a big motivation, we have some nice new estimators with better performance than the one we proposed in the 2014 paper, and this new function will allow us to have finer control of these without cluttering the DESeq() function arguments.

The 2014 shrinkage estimator for fold change worked well for most bulk RNA-seq datasets, but not for all of the kinds of datasets in which DESeq2 is being used. In particular, one kind of dataset where combining shrinkage with testing was not good were those in which there were singular very large fold changes (e.g. abs(LFC) > 6), despite all of the other log2 fold changes being very close to 0. Here the shrinkage was too great. This case looks much better with our new estimators. I think the 2014 shrinkage estimator applied to zero inflated count data, without any adjustment of the estimator to account for the zero component, would likely produce too much shrinkage as well.

Thank you very much, Michael, for the clarification, as I was looking for this answer and was getting confused about the removal of the  shrinkage feature by default from DESeq2. 

So, would you say that for regular bulk RNAseq, we should use this shrinkage feature by default as usual like in the older DESeq2?

If yes, then can I simply get that old DESeq2 effect by just explicitly setting "betaPrior=TRUE" in the DESeq2 command?

It will be great if you could please clarify.

Many thanks.

As one of the question-askers, thanks for the clarification!
I guess the new shrinkage estimators will be added in the devel branch over time?

caveat:  I am new to Bioconductor and have been going through the workshop content and using the workflows to get familiar with it.  So, apologies if the answers to my questions are actually staring me in the face and I cannot see them. 

While running the RNA-seq Gene workflows R code (last build - April 27, 2017),    I get the following error message:

" Error in lfcShrink(dds, contrast = c("dex", "trt", "untrt"), res = res) :
  could not find function "lfcShrink"

when I run the

res <- lfcShrink(dds, contrast=c("dex","trt","untrt"), res=res)

code fragment.

I checked on the github site for the function and found one in helper.R, but when I use that function, I get the following error message:

Error in plotMA(res, ylim = c(-5, 5)) :
  'x' must be a data frame with columns named 'baseMean', 'log2FoldChange'.

Any advice on dealing with this?

Many thanks.