I have to do a heatmap from fold change data. I got a large diversity in the values (for example a few are between 1200 and 500, but the vast majority ot them are below 2).
According to what I could read, I suppose I have to "scale" my heatmap but I can't understand why and how it is done. I couldn't find any clear information about it.
Can somebody explain?
To follow on from ATpoint: plotting log2 fold changes is fine, or even just plotting normalised values on the negative binomial scale. There are no standards.
In the case where your data is not normally-distributed ('bell curve'), though, you should be using something like Spearman correlation distances instead of Euclidean distances, i.e., for the purposes of hierarchical clustering.
Scaling the data (to the Z-scale) just helps to 'even out' any creases that may still exist in the data, which helps for visualisation. It's strictly not a necessary procedure and there are no standards.
Also realise the distinction: we can cluster the data on one distribution (e.g. log2 expression values) for generating row and column dendrograms, and then use a different distribution (e.g. Z scores) for display in the heatmap.