Dear Bioconductor community,
I'm analyzing RNA-seq data with limma-voom.
This experiment involves 6 subjects, including 3 animals who dead after infection by a virus and 3 animal who survive.
From each subject, blood were collacted before any treatment (D0) and at different time points after infection (as animal are not dying the same day, we have missing time point for some animals). There is a single time point (Day 3) for which we have data in dead and alive animals. So, I would like to identify genes that respond differently at Day3 in Alive relative to the Dead animals.
The correlation is very low (0.18). Should I performed the analysis without duplicateCorrelation ?
What is the best way to analyse such data ?
Thanks in advance for your help
Thank you so much for your explanation. Now it's much more clear.
In my case, using duplicateCorrelation() blocking on each animal calculate the correlation measurements made on the same animal, this is why we do not expect a high value (due to experimental condition) is it ?
I don't understand your last question. From the description of your experimental design, I have no prior expectations whatsoever about the size of the correlation. If you have high animal-to-animal variability but a consistent response to time in each animal, then the consensus correlation will be high, as most of the variance in the data will be driven by the animal effect. Otherwise, the consensus correlation will be low; it just depends on how consistently your animals behave.