umap and Louvain clustering on normalized data I know that scaled data must be used for PCA for example as it is based on variance maximization.

However I'm wondering if it's the same case for UMAP ? If the data are single-cell RNA seq, after normalization can we do UMAP and Louvain clustering ? 

I tried both and get similar results, however the Louvain clustering seems to be more adequate on normalized data than on scaled data. 

By adequate I mean the clusters are the same but some are split into two, which makes sens looking at other results (like transcription factor analysis).
If the data are single-cell RNA seq, after normalization can we do
  UMAP and Louvain clustering ?


Typically people run PCA, UMAP and Louvain clustering on the normalised and log-transformed expression counts (but do marker gene and differential expression analysis on the non-normalised values). However, this remains controversial. I recommend reading "Current best practices in single‐cell RNA‐seq analysis: a tutorial"- it's a bit outdated, but still a good reference.


  By adequate I mean the clusters are the same but some are split into
  two, which makes sens looking at other results (like transcription
  factor analysis).


The number of (or size of) clusters should not be used to interpret how good the clustering methodology is, since it is generally dependant on the input parameters, e.g. a higher resolution parameter leads to more clusters. Instead, display the clusters on the UMAP and see if they are continuous 'visually' and check what genes are differentially expressed between clusters and see if they make sense 'biologically'.