Hello,
I have some gene expression data for different tissue types with multiple replicates. My species has undergone a historical duplication event and and I am looking to compare the genome copies to identify which one is more highly expressed. Due to the nature of the count data, most parametric models don't seem to be a good fit for my problem. I was looking into the voom
paper and am wondering if it is statistically sound to apply linear mixed models to the voom
transformed data using external R packages such as lme4
. I want to add the tissue types and gene groups as factors and treat the locus (for gene duplicates) as a random effect. I was unsure if limma
could accommodate this type of analysis since most examples construct factors for samples, and not groups of genes.
Any feedback is much appreciated. Thanks!
Thank very much for your feedback! I didn't realize that genes can be correlated to such an extent, I think I am going to have to rethink my problem.
Genes will be positive correlated if they are activated by the same molecular pathways. They might by negatively correlated if they activated by mutually exclusive pathways or if they are involved in the same pathway but with one as a suppressor of the other.
Interesting! This is really helpful to know - I am not a biologist so I was unaware of these dependencies. Thank you.