Hi everyone.

I find some protocol will recommend using 

```
pbmc <- ScaleData(pbmc, vars.to.regress = "percent.mt")
```

to regress out cell cycle influence or other  "noise". But I am confused why we can not drop these genes in `feature selection` steps. For example, we can drop these genes from high variable gene set, so that protecting our latter PCA and cluster analysis from these influence genes.

Best wishes

Guandong Shang
I still always advise people not to regress out cell cycle status or info. These can be biologically interesting and a big part of many phenotypes. Differing proportions of cycling cells between conditions or samples is both low hanging fruit and something that's easily validated at the bench. I really don't get why people try to remove that, as it's very simple to label populations as "cycling monocytes" and "monocytes" (or whatever) and create supersets as necessary. 

I'd prefer that Seurat drop that part of their cell cycle vignette, I have yet to see a case where it's actually helpful.