This is another question about RNA Seq data normalization. Often, I have read papers using ERCC spike in as control for identifying experimental bias that may occur due to RNA species length and concentration. Then I came across this paper: Revisiting Global Gene Expression Analysis, where they talk about "Transcriptional Amplification".
The key message is proposed in this figure
They have demonstrated using cell lines that with usual RNA Seq experimental and normalization, we may not detect differentially expressed genes effectively (?) in cases where we have transcriptional amplification. The proposed solution here is to use the ERCC spike in standards proportional to cell number and then normalize accordingly
I am wondering how do we handle such a scenario, when we perform such an experiment in Tissue Samples, where we cannot determine the number of cells. We start with same quantities of total RNA for library preparation and do not account for the spatial gene expression patterns/transcriptional amplification.
Are there any controls or data handling procedures that is in use already? Any new strategy would be nice to discuss.
May be we can ERCC to normalize for tissues as well, but how?