*(This thread is related to a Bioconductor package so I also posted this question on the dedicated forum. However, I thought this could relate to bioinformatics in general.)*

Using DESeq2 I would like to obtain a heatmap of sample-to-sample distances using the rlog-transformed values. However, I'm not sure if I should use the "Euclidian" distance or the "Poisson" distance (both are suggested here). I have obtained both graphs but don't know which one I should "trust". While I think I understand the "Euclidian" approach I'm not sure to get the advantages of the "Poisson" approach. I've tried reading the fundament paper (Witten 2011) but got lost at some point B-)

Could someone:

- illustrate a simple sample case where both methods would give
**the same result**? - illustrate a simple sample case where both methods would give
**different results**? - explain the advantages and drawbacks of both methods?

Many thanks!

I understand much better now, thanks.

That's indeed the case.