Is there an optimal way to do single-cell RNA-seq counts normalization? Most RNA-seq normalization tools are designed with bulk samples in mind and assume a certain distribution of reads. With bulk RNA-seq, you have millions of reads per sample with relatively consistent coverage across samples. With single-cell experiments, you have much fewer total reads and many low expressing genes will be completely absent from many samples. I am curious how much I should worry about that and if there is a good way to account for that.
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igor
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For single-cell experiments people tend to normalize the spike-ins and use the resulting size factors. I would be very hesitant to use the actual gene/transcript/whatever counts for normalization in cases like.
Yup, DESeq2 would work fine for this. There are some single-cell RNAseq oriented packages out there, such as SAMstrt. I don't work with single-cell data at the moment, so I can't say how good/bad things like SAMstrt are, but you should have a look at them.
Interesting, that makes life difficult. We use MARS-seq internally, cheaper and scales to many more cells.
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