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Question: DESeq2 and shrinkage of log2 fold changes
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gravatar for riccardo
22 months ago by
riccardo40
riccardo40 wrote:

Hi I have some questions about the shrinkage of log2 fold changes:

Is it always useful or in some cases is it advisable to disable it?

Are there plots helpful to show when use it or not?

The shrinkage can be useful also for the small rna-seq or only for the rna seq?

Thank you.

ADD COMMENTlink modified 22 months ago by Michael Love15k • written 22 months ago by riccardo40
2
gravatar for Michael Love
22 months ago by
Michael Love15k
United States
Michael Love15k wrote:

The shrinkage is generally useful, which is why it is enabled by default. Full methods are described in the DESeq2 paper (see DESeq2 citation), but in short, it looks at the largest fold changes that are not due to low counts and uses these to inform a prior distribution. So the large fold changes from genes with lots of statistical information are not shrunk, while the imprecise fold changes are shrunk. This allows you to compare all estimated LFC across experiments, for example, which is not really feasible without the use of a prior.

One case where I would not use it, is if it is expected that nearly all genes will have no change and there is little to no variation across replicates (so near technical replication), and then say < 10 genes with very large fold changes. This scenario could occur in non-biological samples, for example technical replicates plus DE spike ins. The reason this would cause a problem is that the prior is formed according to a high percentile of the large fold changes, but it could miss if there were singular DE genes, and form a prior which is not wide enough to accommodate very large fold changes. It is trivial to turn off the prior in this case (betaPrior=FALSE).

I don't have a comment on small RNA-seq, as I haven't personally analyzed this, but I know the moderated LFC have been used in some small RNA-seq analyses.

You can plot fold changes with and without shrinkage like so:

res <- results(dds, addMLE=TRUE)
plotMA(res)
plotMA(res, MLE=TRUE)
ADD COMMENTlink modified 22 months ago • written 22 months ago by Michael Love15k

Thank you. In the case of the small rna seq could you give me some advice in order to assess when use it or not?

ADD REPLYlink written 22 months ago by riccardo40
It should be fine to use it. To give an example, I wouldn't use it if all LFCs were nearly equal to 0 (say between -.1 and .1) except one or two LFCs which were > 4 in the MA plot of MLE fold changes. This could occur in a technical dataset but unlikely with real biological samples. These numbers are totally contrived though.
ADD REPLYlink modified 6 months ago • written 22 months ago by Michael Love15k

Hi Mike, would I be right in thinking that another situation you might not want to use shrinkage is if you wanted to compare the change for two sets of genes, and one of the those sets was more lowly expressed than the other?

ADD REPLYlink written 6 months ago by i.sudbery10
1

hi, in this case, I'd say the shrinkage is actually the most useful. It will tamper down any non-informative differences in the small count gene. Only when the LFCs between two groups both with small counts are above what is expected simply due to sampling variability will a large value come through. And I should note, on nearly all the bulk RNA-seq datasets we try, we do not observe too much shrinkage. It's only observable for extremely large LFCs in datasets where all the other genes have nearly no change. And for this we have a new estimator in development which works quite well across the board.

ADD REPLYlink written 6 months ago by Michael Love15k
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