# 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