I have 74 samples with log normalised gene counts. I want to see their expression pattern changes with age (range 2-72 years). I am not binning the ages and using them as a continuous variable.
This is the script I used to get p-values using splines:
fit<-lmFit(counts_UMary, design = design)
fit<- eBayes(fit, robust = TRUE)
results<-topTable(fit coef = 2:ncol(design), n=Inf, sort.by = "none")
I am a little confused about how to interpret the x1-5 coefficients obtained from the splines.
I then run the second model is a simpler model using age directly as a covariate and use the logFC from that. Since this model assumes linearity, I will consider the adjusted p-values from the first model.
Also, even though we "sort.by = none" for both, the order is different so it is quite difficult to compare the results. And the p-values are extremely different as well.