I have a Seurat object which has a high expression of
> pbmc1 An object of class Seurat 36601 features across 18338 samples within 1 assay Active assay: RNA (36601 features, 0 variable features) >
I want to use
SCTransform in Seurat, I don't know if still I should define the mitochondrial percentage or not, if not, how SCTransform function knows which percentage suits for my data?
I used SCTransform without clipping
MT genes and I noticed I have the same number of cells
> pbmc_SCTransform <- SCTransform(pbmc, method = "glmGamPoi", vars.to.regress = "percent.mt", verbose = FALSE) > pbmc_SCTransform An object of class Seurat 58629 features across 18338 samples within 2 assays Active assay: SCT (22028 features, 3000 variable features) 1 other assay present: RNA
Is it fine or I should clip
MT genes before running SCTransform function?
I am not too sure about the input composition but from 10X websummary two of my samples likely suffer from poor sample prep or poor beginning cell health. The gene expression median genes per cell are 41 and 50 and differential expression show only mitochondrial (MT) and MALAT1 genes. This is indicative of subpar lysis conditions.