Hi Abhay, you may want to deconvolute your bulk RNA seq data to see the cell type composition. Based on this information you can normalize your fold changes.

Hi Tushar Deshpande ,

Thanks a lot for your response. Let me elaborate a little more on my experiment.

I have one cell line; MCF10A. I performed bulk RNA seq on certain knockouts of this cell line and another colleague in the lab had performed single cell RNA sequencing on the same knockouts after treating the cells for 6 days in serum starved media. Now we want to compare the DEGs between both these methods in the knockouts.

Hi Abhay,

You could try to 'pseudo-bulk' your colleague's scRNA-seq data and then compare that to your own.

Here, the expression values of all profiled cells in a single-cell experiment are bulked together for each replicate. This then can be processed using the same pipeline as your bulk data giving you a much more direct comparison.

There are multiple tutorials online: https://hbctraining.github.io/scRNA-seq_online/lessons/pseudobulk_DESeq2_scrnaseq.html

One would expect concordant fold-changes to validate underlying biology (ko effect) but I wouldn't expect results to be identical though... Some differences would arise from different batches (replicates), library chemistry, sequencing runs, etc.

Also, if you still have some cells available you could run some qPCR on a few selected genes to tell you which dataset is better to trust.