Hi ATpoint, thanks for your reply... I'm using Csaw for this project but i think don't matter since my doubt are about edgeR steps

After count the reads in windows and estimate the background I have filtered the data

fstats <- filterWindowsGlobal(count, background)
min.fc <- 3
keep <- fstats$filter > log2(min.fc)
filtered.data <- count[keep,]

and perform a TMM normalization

filtered.data <- normFactors(background, se.out=filtered.data)

Then, I have make a matrix

celltype <- file$Diet
celltype[grep("Cntl", celltype)] <- "Cntl"
celltype[grep("Treat", celltype)] <- "Treat"

celltype <- factor(celltype)
design <- model.matrix(~0+celltype)
colnames(design) <- levels(celltype)
y <- asDGEList(filtered.data)

and from here my nightmare start. I would like to know how from my count matrix I'll obtain fold change and Pvalue (how this is done step by step across the three functions reported below (estimateDisp, glmQLFit and glmQLFTest)

y <- estimateDisp(y, design)
fit <- glmQLFit(y, design, robust=TRUE)
contrast <- makeContrasts(YD-RD, levels=design)
res <- glmQLFTest(fit, contrast=contrast)
head(res$table)

I'm able to carry out the analysis, but how it is done is not really clear. Thanks in advance for you time