Can EnhancedVolcano label genes that aren't significant or meet fold change cut off? Can we select? I tried to but it comes up as a geneid on the x-axis with vertical bar. Just wondering. 4.8 seems to suggest no. 

How does EnhancedVolcano determine which genes to label? Is there a way to label more to the ones selected automatically? I guess following 4.8, you can selectLab but they have to be present already in lab. What happens if you selectLab genes that interfere with ones automatically selected by EnhancedVolcano, do they get bumped or removed? 
Can we remove select labels we don't want? It seems no matter how genes are labeled, it picks the same ones to label.

The default is Log2FC=2, so means minimum 4 fold change? or total 2, so 2 fold change, log2 = ratio 1?  

Should I use lfcShrink(res) to get volcano plot or is just res fine? The vignette seems to suggest lfcShrink but pdf manual example has just res. Not sure what the proper or standard way for volcano plots from RNA-seq data run through DESeq2 is and how people normally publish them.

This package does look great. I'm pretty beginner at this and this is pretty user-friendly with nice vignette.
I wrote *EnhancedVolcano* - thanks for your comments.

You mentioned section 4.8: <a href="https://bioconductor.org/packages/release/bioc/vignettes/EnhancedVolcano/inst/doc/EnhancedVolcano.html#only-label-key-transcripts">4.8 Only label key transcripts</a>. This should also label genes that do not pass your thresholds. However, the points for these non-statistically significant genes is usually dense, at the bottom of the plot; so, labeling all of them in a clear way is almost always impossible. If you want to favour the labeling of certain genes, then you can (I believe) re-order your results data-frame to have these genes appear in the first few rows of your data-frame.

You can also supply custom labeling and colouring via <a href="https://bioconductor.org/packages/release/bioc/vignettes/EnhancedVolcano/inst/doc/EnhancedVolcano.html#override-colouring-scheme-with-custom-key-value-pairs">4.11 Override colouring scheme with custom key-value pairs</a>:

<a href="https://ibb.co/eNGZEf"><img src="https://preview.ibb.co/ejhkLL/download.png" alt="download" border="0"></a>



For the fold-changes, you can technically supply any numbers as the value of `x`. These can be log2 fold changes, linear fold changes, etc. The corresponding cut-off, as `FCcutoff`, is not assuming any distribution. If you supply linear fold changes to `x` and have `FCcutoff=2`, then the cut-off is linear FC = 2. If you supply log2 fold-changes as x and have `FCcutoff=2`, then this cut-off relate to log2FC = 2

`lfcshrink()` can be used when you have initially used `betaPrior = FALSE` during the `DESeq()` function. `lfcshrink()` was a recent implementation into DESeq2, so, I am not sure of the exact situations where it should (or should not) be used.

Kevin
