Hello,
I have two different experiments for the same organism and same tissues:
Exp_1: control vs exposed (6 biological replicate per group) and I have almost 15 different treatments (in lab). DEGs was done using edgeR (I won't go further in detail).
Exp_2: control vs exposed but in the field (same 6 replicates per site). Control were ones collected in "pristine" site and exposed were collected close to human activities sites. Toxico data does not show any difference between control and exposed samples. For RNA-seq data, we do not want to do a regular DGEs since our control does not look like controls.
The question is: how can we use Exp_1 to help figure out if some genes are expressed in Exp_2? How can I validate genes expressed in Exp_1 using Exp_2?
Data I have:
- Exp_1: TMM values as well as LogFC and FDR (control vs treated) and raw counts
- Exp_2: TMM values and raw counts
Could I consider
- corr test between Exp_1 and Exp_2 (based on TMM values)
- re do DGEs mixing Exp_1 and Exp_2 samples and consider Exp_X as batch?
- rank the TMM values per genes and compare them?
- create a model?
Thank you for your help.
What you mean by "expressed"?
Note that the TMM values from edgeR are entirely technical values that correct for composition bias. They have no meaning in terms of being compared between different experiments. Do you maybe mean the normalized counts, e.g. output of the cpm function?
Model of what?
In summary, please describe the analysis goal better.
My question is how can I link Exp1 and Exp2?
Example: Exp_1 - HSP70 is being over expressed in Treatment B and F. I want to know if that gene is also modulated in Exp_2, or how can I use Exp_2 to validate HSP-70 modulation or link them (Exp_1 and Exp_2) since one is in "lab treatment" and the other one is "environmental exposure"?
edge R - normalization method TMM and then I use the cpm to have the TMM values for all samples in Exp_1 and Exp_2. Note: no DGEs analysis was done for Exp_2