I have been trying to analyse an available scRNA seq data set with the script they provided, however as a beginner it was not that intuitive for me so I decided to combine it with Seurat/SingleCellExperiment pipelines on my own and try to get a similar final outcome.
Here is the available script in Github (https://github.com/saeyslab/brainimmuneatlas/blob/master/script_scRNAseq.R) as a reference.
In my case, I converted the data.matrix into a Seurat object, visualize QC metrics and did a first cleaning based on that, calculated cell-cycle scores for second cleaning, then I converted to SingleCellExperiment and used isOutlier function to clean outliers (cells, genes and mit DNA %) (mad=4). Then I examined genes levels and did a cutoff in 10^-3.5.
In this point I have two problems: 1) For generating PCA and cleaning from outliers, I used runPCA function and detected outliers, however I am a bit lost in how to delete them from my original sce object. Do I need to create a metadata matrix as in the original script or is there an easier way?
2) Once I want to normalize, I used computeSumFactors for correct calculation of size factors and then I normalize and from there I return the expression values matrix with exprs function. If now I want to create a new Seurat object with this expression matrix, would I need to normalized again (with NormalizeData function) or it is not necessary anymore?
Hope it was not too much!
Thanks a lot in advance