I guess, it's just a matter of choice of Seurat developers. And in fields other than bioinformatics, natural log is the one most widely used - one of the major reasons is that the exponential function `(e^x)`

, which is the reciprocal "cousin" of natural log, comes naturally in many calculations - like differentials and integrals and by extension in the sums of infinite series. No surprise that `e`

is also defined as the limiting sum of the infinite series `(1 + 1/n)^n as n approaches infinity`

Changing the base of log amounts to multiplying by a constant factor

```
log2(N) = log2(e) * ln(N)
```

So it doesn't affect the performance of downstream analyses like clustering, PCA etc as most of these analyses are invariant to scaling transformations (i.e they do not change if all the values are multiplied by a constant number).

PS: The best way to convince is to try yourself. Try to run the analyses by multiplying with a constant factor and see if the downstream results change. You may add a different scaling factor than 10000 while normalizing

NormalizeData(object, assay.type = "RNA", normalization.method = "LogNormalize", **scale.factor = 10000**, display.progress = TRUE)

I agree that, in bioinformatics, log2 is so often used and easily interpretable that should be the default option of any tool.