I have a dataframe where I have
regression estimates (for 5mC methylation data: a positive estimate would indicate hypermethylation, while a negative estimate would indicate hypomethylation in the disease group. These estimates are averaged at gene level, initially I had these values for each CpG site) and
logFC computed by limma (positive value means genes are up-regulated in disease, negative values means they are down-regulated in diseased state). This is how my dataframe looks like:
> data[1:3,] Gene Reg_Beta logFC 1 A1BG 0.012759505 -0.01594659 2 A1CF 0.003407954 0.01044036 3 A2M 0.004816774 0.37067536
Can anybody guide me if I can obtain correlation between
Reg_Beta (avg. beta value for methylation status of a gene) and
logFC (expression value of that gene) at gene level? So that at the end I can get those genes for which I can say they are highly anti-correlated to gene expression.
I am a newbie to methylation analysis, any constructive suggestion or comment will be highly appreciated! Thanks.