Which preprocessing choices most affect the final clusters?

Normalization, the number of highly variable genes, and the number of PCs all feel like they could swing the downstream clustering a lot. In your experience, which of these is the most consequential / most often mis-set, and are there sensible defaults that travel across datasets?