As per the title: why?

Does the preliminary PCA step merely maximise the chances of identifying the clusters that tSNE / UMAP later identify?

When doing this, in tutorials, it seems that people blindly choose a number of PCs as input to tSNE / UMAP, without checking the amount of variation explained by the PCs. For example, 20 PCs may only account for 30% overall variation.

Assuming one follows a standard procedure for, e.g., scRNA-seq, is this one step too many. A pipeline could be:

 1. normalisation of raw counts
 2. transformation of normalised counts
 3. PCA-transformation (may include an additional pre-scaling step)
 4. tSNE/UMAP

Kevin
At least from a clustering perspective, I'd probably try it both ways to be on the safe side, i.e. with PCA to get the top N PCs and without. I'm a bit skeptical of reducing to N PCs for clustering because there is inevitable information loss. The same will apply for t-SNE, UMAP, etc. I'd prefer to use the most variable genes instead.

Although I think it is less of a problem when just trying to visualise the data, rather than define some new cell types using cluster analysis where we might want to be a bit more cautious.