Why do you think it is not recommended?
The sctransform_vignette clearly states that "we can also remove confounding sources of variation" .
I wouldn't worry too much and I would run
SCTransform(myObject, vars.to.regress = "SampleID", verbose = TRUE) (assuming SampleID is where your sample names are stored).
If you don't want to regress them out, I often use a different approach, that allow you to not worry about batch effect coming from different samples:
1) create 1 object per sample (In your case, you would have 8 objects each containing the two regions)
2) create a list containing all the objects
3) Integrate the objects together (
---- Update after comments
SCTransform and integration as two different things.
SCTransform is a way of normalise and scaling, while integration is the process of combining datasets that are already normalised and scaled on their own.
You can regress out confounding with SCTransform, and you don't need to worry about it while integrating.
In the case presented @paria.alipour you can use 2 approaches:
A) combine all counts together and treat them as 1 dataset
In this case, which is not what @paria.alipour is doing, you need to regress out the samples
SCTransform(myObject, vars.to.regress = "SampleID", verbose = TRUE)
B) integrate the different samples
Depending on how you decide to group the samples, you may need to regress out or not
1st step: run
SCTransform(myObject, verbose = TRUE).
As all the cells comes from 1 single sample you don't need to regress the sample, but you could regress out other sources of variation (region, percentage of mito or any other things that you know could have an effect).
In this case I would account for the region of the brain (
region), even if PCA suggests there is no huge effect.
SCTransform(myObject, vars.to.regress = "region", verbose = TRUE) or
SCTransform(myObject, vars.to.regress = c("region","mt.percentage"), verbose = TRUE)
2nd step: Integrate the objects together (FindIntegrationAnchors + IntegrateData)
You don't need to care about regressing, because you did it already in the previous steps.