# edgeR
# following are my paired samples (column Patient) with other three columns 
Sample  Groups  Patient Groups1 
a   baseline    a   B   
b   baseline    b   B   
c   baseline    c   B   
d   treated     a   BF  
e   treated     b   BF  
f   treated     c   BF  
g   baseline    d   B   
h   baseline    e   B   
i   baseline    f   B   
j   treated     d   BF  
k   treated     e   BF  
l   treated     f   BF  
m   baseline    g   C   
n   baseline    h   C   
o   baseline    i   C   
p   treated     g   CF  
q   treated     h   CF  
r   treated     i   CF  
s   control     NA  control 
t   control     NA  control 
u   control     NA  control

Given above is the experimental design of my dataset after running the command of edgeR up to :

lcpm <- cpm(y2, log=TRUE)
boxplot(lcpm, las=2, col=group$Sample, main="")
title(main="B. Example: Normalised data",ylab="Log-cpm")

I created following design matrix to find out a) disease marker and b) treatment response marker miRNAs

design<-model.matrix(~0+group$Groups1)  #based B, BF, C, CF, control
colnames(design)<-levels(group$Groups1)
design
#DISEASE MARKER; DM=control-B-C  # substraction of B and C are diseae stage from control
#TREATMENT RESPONSE TR=NC-BF-CF  # substraction of BF and CF are diseae stage after treatment from control
#DM is disease marker which is comparision between patient B, C, and control (without treatment)
#TR is treatment response marker wihch is comparision between patients B after treatment, patients c after treatment and #control (without treatment)
contr.matrix<-makeContrasts(DM=control-B-C, TR=control-BF-CF, levels=colnames(design))

QUESTION: Is the data matrix created above is correct or not ?

The main idea behind is to find DE miRNAs in diseased and DE miRNAs after treatment. This will give those miRNAs that are DE due to treatment so that I can conclude the list of miRNAs that responded to treatment. Is this approach correct ? any suggestions will be highly appreciated ? how to create DE venn diagram using edgeR command