I would like to get sample distances between different samples of an RNA-seq experiment. Read that VST and rlog function of DEseq R package were good to make a correction so that standard deviation of expression of a gene across all samples doesn't change with the mean (of expression of that gene across all samples).
My questions are:

1 - Should these corrections be applied after normalising raw counts for sequencing depth (with the DESeq() function) or directly applied on the raw data?

2 - To do a heatmap with a dendrogram representing the distances between samples, is it better to plot in a heatmap the values corrected with VST/rlog or FPKM values?

3 - 'VST' method seems to be better for big sets (n>30). I have 3 samples, so that means need to choose 'rlog' instead?

4 - In both methods we can set parameter 'blind'. Should I set it to 'TRUE' or 'FALSE' in which situations?

Regards.

> 1 - Should these corrections be applied after normalising raw counts
> for sequencing depth (with the DESeq() function) or directly applied
> on the raw data?

These should only be applied to the normalised expression levels ('counts'), as per the <a href="https://bioconductor.org/packages/3.7/bioc/vignettes/DESeq2/inst/doc/DESeq2.html">DESeq2 vignette</a>.

> 2 - To do a heatmap with a dendrogram representing the distances
> between samples, is it better to plot in a heatmap the values
> corrected with VST/rlog or FPKM values?

Don't use FPKM values - the method of normalisation that produces FPKM expression levels should no longer be used for multi-sample studies. Instead, use either the VST- or rlog-transformed counts. Please see the answer that I gave earlier today: https://www.biostars.org/p/328304/#328324

**Edit April 21, 2020:** if you must use FPKM in a heatmap or for any downstream application, I would transform them to z scale via *zFPKM* package

> 3 - 'VST' method seems to be better for big sets (n>30). I have 3
> samples, so that means need to choose 'rlog' instead?

You can justify the use of either. rlog is not recommended for large datasets because it can take a very long time. I tend to check both, where possible, and find that results don't largely change between both of these methods (provided that there are no outliers in your dataset).

> 4 - In both methods we can set parameter 'blind'. Should I set it to
> 'TRUE' or 'FALSE' in which situations?

Please read: http://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#blind-dispersion-estimation

Kevin