I have a question regarding paired analysis in the Limma package. I am analyzing Illumina methylation arrays of paired tumor samples from two different sites. I came up with the following script to analyze the data, which I put together from old scripts I found in our lab. But there is something that bothers me, I do not understand why the design matrix has an intercept that is forced to 0. From a lecture on linear models I remembered that forcing the intercept to 0 is bad practice in many cases, but I'm not sure if I'm missing the point here? The results are substantially different between `r~0+Location+Pairs`

and `r~Location+Pairs`

.

```
# this is the factor of interest
Location <- factor(pd$Sample_Group, levels = c("M","P"))
# the individual effect, each pair is a subject
Pairs <- factor(pd$Pair)
# design matrix
design <- model.matrix(~0+Location+Pairs, data=pd)
colnames(design) <- c(levels(Tissuetype),levels(Pairs)[-1])
# fit the linear model
fit <- lmFit(Beta_To_M(myCombatLimma), design)
# create a contrast matrix for specific comparisons
contMatrix <- makeContrasts(P-M, levels=design)
# fit the contrasts, estimate sample-variance, compute moderated t-statistics
fit2 <- contrasts.fit(fit, contMatrix)
fit2 <- eBayes(fit2)
# pairwise analysis
summary(decideTests(fit2,
lfc = 0, # cutoff based on beta values
p.value = 0.05,
adjust.method = "BY",
method = "separate"))
```