we have a data set of 48 samples mapped with STARsolo to create a sparse matrix. After reading the mtx file into a SingleCellExperiment object we would like to normalize it by deconvolution using the computeSumFactors function from scran.

After qc we have only 45 samples left, each represent a separate cell, in two conditions. Interestingly, both the qc as well as the scatter plot of library size show a nice split based on this two conditions (see the two images below). red and black dots are the two conditions in the data set.

A short snippet if the workflow is also attached below.

sce <- SingleCellExperiment(assays = list(counts = cts) )
...
stats <- perCellQCMetrics(sce, subsets=list(Mito=mito))
sce <- sce[,!qc$discard]
lib.sf.sce <- librarySizeFactors(sce) # lib size factors
clust <- quickCluster(sce, min.size = 1)  # needed to be reduced for a successful run.
deconv.sf.sce <- calculateSumFactors(sce, cluster=clust) # deconvolution size factors

Is it possible to run a normalization and scaling by deconvolution with such a small data set?

I read here that it might be possible if the scatter plot show a nice correlation of the deconvolution factors and the size factors. But in there, there were three times the amount of cell as we have. In my opinion, they do correlate here quite nicely, but the separation on condition is a bit strange.

I would appreciate the advice.

thanks

Assa