Hi Bonnie,
In our modeling, the methylation level ranges from 0 to 1. A methylation difference of -0.498148, then, corresponds to a -49.8% difference.
As the number of samples in your study increases, the power to detect significant changes at smaller effect sizes increases. If you have a reasonable sample size and are testing few enough regions, it's not unreasonable to see significant p-values at small effect sizes. I would caution against over-interpreting these small effect sizes though: generally I don't think methylation differences less than 10% are meaningful, or would be unwilling to try and correlate them to transcriptional changes (for example) without single-cell resolution. Usually my first follow-up to a radmeth run is to plot a volcano plot (effect size vs -log10 p-value) and decide on a threshold for effect size.
If you think your experiment is small enough that the p-values you're seeing are erroneous, it would be helpful to see the command you used to run, the design matrix, and a snippet of the proportion table for debugging purposes.
Thanks,
Ben
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Benjamin Decato, Ph.D.
Computational Biology Section,
Department of Biological Sciences,
University of Southern California
1050 Childs Way, RRI 408M
Los Angeles, CA 90089-2910