I need to perform differential expression analysis using the scDD package from R, but I am not able to since I miss the normcounts assay in my SCE object (of course in the example they show, the assay is already there).
As I found out in the preprocessing steps, one can use this function to generate the missing assay, except that it does not work:
# this is the SCE object > cTSubSet class: SingleCellExperiment dim: 19767 149 metadata(2): Samples Samples assays(2): counts logcounts rownames(19767): Xkr4 Gm19938 ... CAAA01147332.1 AC149090.1 rowData names(4): ID Symbol Type chrLoc colnames(149): AAAGGATGTTCACGAT-1 AACCATGTCTTTCGAT-1 ... TGTGGCGTCTCTTAAC-1 TTTGGTTAGACGCTCC-1 colData names(17): Sample Barcode ... slm_1 slm_1.5 reducedDimNames(0): mainExpName: NULL altExpNames(0): # setting parameters as suggested in the vignette linked above > paR <- list(alpha=0.01, mu0=0, s0=0.01, a0=0.01, b0=0.01) # setting zero.thresh to zero since I already removed unwanted genes > cTSubSet <- preprocess(cTSubSet, zero.thresh=0, scran_norm=TRUE) # the not so useful error: Performing scran Normalization Error in t.default(Data) : argument is not a matrix
I was also looking for other possible ways of obtaining such normcounts assay and I found this description:
normcounts: Normalized values on the same scale as the original counts. For example, counts divided by cell-specific size factors that are centred at unity.
Still, I can't figure out what this may mean. Is there a way of obtaining such normcounts?