Differential Expression analysis between single cell RNa seq & bulk RNa seq data

Question

[bioyas]

Hi,

I have two read count data sets for several samples in one organism. One has been generated from Bulk Ran seq and the other from single cell Ran seq. I would like to find the genes that are Differentially expressed between 2 experiments. For example I am looking for genes that are DE when comparing sample A from single cell versus sample A from Bulk experiment. Since the number of transcripts in single cell is low relative to bulk I need to scale the read counts to make a reasonable comparison for DE analysis. I tried to find some studies that has analyzed the combination of sc and bulk results but I could not find some similar work.

Do you have any idea how can I address the scaling issue of the counts?

Thank you

[shoujun.gu]

In short, you cannot compare these two datasets.

Answer 1

[swbarnes2]

RNASeq is very sensitive to batch effects. This kind of analysis is really not going to be valid, because all your differences could be due to the different prep methods, not biology.

If you really insist, I'd combine all the single cell data to make it look more like bulk RNASeq.

[bioyas]

As you said it does not make sense to do this analysis without modification.. IF we do DE without correcting the number of reads with normalization as the transcription is very low in single cell, everything will be Differentially expressed. So I am trying to find a way to scale the reads so that they become comparable and the DE analysis would have real result.