Entering edit mode
Hi all,
I am trying to figure out signals from DNA methylation for a continuous variable at multiple groups. We have mice samples at 4 time points in both sexes, and we are testing if DNA methylation positions respond to V1 using limma
I used the following design matrix and added duplicateCorrelation for random effect. I am asking the following three questions:
- which DNA methylation positions respond to V1 in females?
- which DNA methylation positions respond to V1 in males?
- which DNA methylation positions respond to V1 differently between females and males?
V1 = meta_dat$variable
sex = meta_dat$sex
X = ns(meta_dat$time_point, df = 3)
design = model.matrix(~ 0 + sex + sex:V1 + X)
Q1: Does this design matrix make any sense?
The results from limma gave the following columns
colnames(coef(fit2))
[1] "sexfemale" "sexmale" "X1" "X2" "X3"
[6] "sexfemale.V1" "sexmale.V1"
To answer the above questions,
topTable(fit2, coef = 6, n = Inf, adjust.method = "BH")topTable(fit2, coef = 7, n = Inf, adjust.method = "BH")cont_sexdiff = makeContrasts(sexfemale.V1 - sexmale.V1, levels = colnames(coef(fit2))) fit_cont = contrasts.fit(fit2, cont_sexdiff) fit_cont = eBayes(fit_cont) topTable(fit_cont, n = Inf, adjust.method = "BH")
Q2: Is my approach appropriate? Thank you.
Hi Gordon,
Thank you very much for your answer. Actually the variable X is the age, which I am also interested in. Somehow, age and V1 are co-related, that is, with age increases, V1 increases. Hence I wanted to identify DNA methy positions related with V1 adjusted for age/X. Rather than performing two sets of analysis, my plan now is to test DNA methylation positions that respond to age and V1 differently in males and females simultaneously. Hence I came up with the following design matrix:
By performing contrast, I can identify DNA methylation positions that respond to V1 and age in males, females, average both sexes, and sex differences.
Could you comment on this matrix design? Thank you.