Hi,
I hope this is the appropriate forum for my question, please excuse me if not.
I have a question regarding my experimental design and a confounding factor. I am working on a RNAseq project in which I will have 4 conditions with 10 biological replicates each. I am working on brain mRNA. What I just realized based on other analyses on the same samples is that at times of sampling, the animals I sampled were transitioning across two behavioural states that are known to be associated with huge changes in brain gene expression (over 80% of genes change).
So I have about 60% of my samples in one state and the other in the other state. These states are all represented in each treatment but not in a perfectly equal way. The good thing is that now I know what sample was in what state and I can account for this additional factor in my DEG analyses. However, I am worried that a factor with such a huge effect on gene expression may hamper the recovery of DEGs from the treatments that I am interested in (which likely will have more mild effects than behavioural state).
What are your takes on this? Does the effect size of one factor matter in the ability to look beyond that? And since I have not yet prepared the libraries, should I go back and change some of the samples so that the behaviours are 50/50 and equally represented across treatments? Possibly I could also go back and sample only one of this states and remove this additional layer of complexity. However, looking into these different states and whether the treatment effects are general across them or if there is an interaction between these factors would be interesting and make conclusions stronger. I am just worried that I will lose power to detect what I am actually interested in.
Thanks!
A.
I hope this is the appropriate forum for my question, please excuse me if not.
I have a question regarding my experimental design and a confounding factor. I am working on a RNAseq project in which I will have 4 conditions with 10 biological replicates each. I am working on brain mRNA. What I just realized based on other analyses on the same samples is that at times of sampling, the animals I sampled were transitioning across two behavioural states that are known to be associated with huge changes in brain gene expression (over 80% of genes change).
So I have about 60% of my samples in one state and the other in the other state. These states are all represented in each treatment but not in a perfectly equal way. The good thing is that now I know what sample was in what state and I can account for this additional factor in my DEG analyses. However, I am worried that a factor with such a huge effect on gene expression may hamper the recovery of DEGs from the treatments that I am interested in (which likely will have more mild effects than behavioural state).
What are your takes on this? Does the effect size of one factor matter in the ability to look beyond that? And since I have not yet prepared the libraries, should I go back and change some of the samples so that the behaviours are 50/50 and equally represented across treatments? Possibly I could also go back and sample only one of this states and remove this additional layer of complexity. However, looking into these different states and whether the treatment effects are general across them or if there is an interaction between these factors would be interesting and make conclusions stronger. I am just worried that I will lose power to detect what I am actually interested in.
Thanks!
A.