I need to find a normalization method (or a sequence of several algorithms applied, not only normalization) for single-cell RNAseq that would allow me to compare directly values between the experiments. There is a thread, but I think it deals with bulk-RNAseq:
The point that I really need to somehow compare very different single-cell experiments, generated on different instruments, different tissues and by different methods. Is it possible at all? If not, what normalization method still could alleviate the issue for single-cell RNAseq? I am looking for some, preferably
python library, but
R would work too.
I know there is
scran normalization. Recently
SCnorm was released, but it does not work unfortunately for sparse enough datasets: their algorithm just does not converge in this case.
DESeq2 which uses
RLE might be a choice. What about these methods:
I also know that
CCA algorithm to combine and analyze the datasets, but I do not want to combine them. I have maybe
60 datasets, each several
Gb of size, and loading all of them at once into memory is a real problem with
Any suggestions would be greatly appreciated.