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
The primary reason for this given in the tSNE literature is for the sake of computational efficiency. See, for example, [vanderMaaten 2008](https://lvdmaaten.github.io/publications/papers/JMLR_2008.pdf). In essence, tSNE requires pairwise comparison of datapoints, so it can be incredibly computationally taxing on scRNA-seq datasets unless the dimensionality undergoes an initial reduction.

I can't speak to UMAP, I'm not familiar enough with its inner-workings, but I presume the initial PCA is done for similar reasons.