The inconsistency of bulk and single cell RNA-seq results
Hi,
Supposing two developmental stages and two clusters of genes belong to each step, I noticed that the overlap of these clusters in bulk and single cell RNA-seq are too low but when I just merged the single cell data to make a bulk RNA-seq, the overlap becomes reasonable. what do you think please?
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The reason of getting low overlap, what i assume, is heterogeneity or simply, the biology. Bulk RNA-seq could mask the variance (hidden biology), within cells. Recently, I have read a "Single-Cell Alternative Splicing Analysis with Expedition Reveals Splicing Dynamics during Neuron Differentiation" paper where they revealed the hidden cell state using single which can not be detected by bulk RNA-seq. So take home message from the paper was we can learn the more biology using single cell RAN-seq.
In your case, when you merge your single cell data, your data become like bulk of RNA that is why you get similar results. If i were you, i would not merge and would go using singe-cell data.
Thank you, Convincing
As many genes can be incorrectly read out as a zero in single cell RNA-seq, what would be a good normalization method for distinguishing more narrowly defined cell states such as cell cycle phase or stress responses, and discriminating very similar cell subtypes that differ only in the expression of a few genes??? For example monocle does not give these genes
Unfortunately, i am not expert in singe-cell rna-seq and can not give you a perfect answer, but in case if you haven't seen these papers before SCnorm: robust normalization of single-cell RNA-seq data and Normalizing single-cell RNA sequencing data: challenges and opportunities, you could look them and hopefully get your answers.
Thank you for your time and kindly considerations