variable.features.n didn't change the number of genes used

[Evenlyeven]

Hi,

Firstly, thanks for this amazing package!

I just noticed that regardless of variable.features.n I set, it is always "2000 genes" used. Or do I have misunderstanding of the number of genes used in the process?

Thank you very much in advance!

c(Er_treated.sct, control.sct) %<-%
lapply(X = c(Er_treated.sub, control.sub), FUN = function(x){
SCTransform(x, variable.features.n = 4000)
})

Calculating cell attributes from input UMI matrix: log_umi
Variance stabilizing transformation of count matrix of size 17373 by 5045
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 5000 cells
|===============================================================================================================================| 100%
Found 130 outliers - those will be ignored in fitting/regularization step

Second step: Get residuals using fitted parameters for 17373 genes
|===============================================================================================================================| 100%
Computing corrected count matrix for 17373 genes
|===============================================================================================================================| 100%
Calculating gene attributes
Wall clock passed: Time difference of 2.110177 mins
Determine variable features
Place corrected count matrix in counts slot
Centering data matrix
|===============================================================================================================================| 100%
Set default assay to SCT
Calculating cell attributes from input UMI matrix: log_umi
Variance stabilizing transformation of count matrix of size 19138 by 7567
Model formula is y ~ log_umi
Get Negative Binomial regression parameters per gene
Using 2000 genes, 5000 cells
|===============================================================================================================================| 100%
Found 107 outliers - those will be ignored in fitting/regularization step

Second step: Get residuals using fitted parameters for 19138 genes
|===============================================================================================================================| 100%
Computing corrected count matrix for 19138 genes
|===============================================================================================================================| 100%
Calculating gene attributes
Wall clock passed: Time difference of 2.398781 mins
Determine variable features
Place corrected count matrix in counts slot
Centering data matrix
|===============================================================================================================================| 100%
Set default assay to SCT
There were 50 or more warnings (use warnings() to see the first 50)

[saketkc]

The 2000 you see here is the default number of genes for estimating the Negative Binomial parameters (you can change this by using n_genes parameter to SCTransform. The variable features are set to 3000 by default which can be confirmed by length(VariableFeatures(object))