I have a dataset that is made up of percentage values of DNA methylation at a number of loci and we are looking to model the relationship with a) a treatment and b) a genomic annotation. The histogram of the data shows a left skewed data set, and as a percentage it obviously has an upper bound of 100.
The treatment predictor is binary (Y/N) and the annotation predictor can take 4 values.
#As I say above, we are trying to model methylation~treatment+annotation+annotation:methylation
Given there are 0s and the data is a percentage I have tried arcsine and folded root transformations followed by anova. Unsurprisingly, these have had little effect on the tails of the residuals in the
qqplot. There is no missing data.
So, are there any other potential transformations here or do I need to take a generalized linear model approach?