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Design matrix for edge R analysis?
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Entering edit mode
13 months ago
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Netherlands

I would like to compare Q method against L method, and I have considered 2 different contrasts (at the end), but I am not sure which on is correct?

There are 2 different methods (Q and L) and from each one there are 2 biological replicates (L4,L6-L8 and Q3,Q5-Q7), and 2 technical replications from each biological replicate. as below:

design

                    biological_replicate   method
 L4_rep1                              L4       L
 L4_rep2                              L4       L
 L6_L8_rep1                        L6_L8       L
 L6_L8_rep2                        L6_L8       L
 Q3_rep1                              Q3       Q
 Q3_rep2                              Q3       Q
 Q5_Q7_rep1                        Q5_Q7       Q
 Q5_Q7_rep2                        Q5_Q7       Q

design$biological_replicate <- factor(design$biological_replicate, levels = c("L4","L6_L8", "Q3", "Q5_Q7"))

design$method <- factor(design$method, levels = c("L", "Q"))

Group <- factor(paste(design$biological_replicate,design$method,sep="."))

design<- cbind(design,Group)

                    biological_replicate method   Group
L4_rep1                              L4      L    L4.L
L4_rep2                              L4      L    L4.L
L6_L8_rep1                        L6_L8      L L6_L8.L
L6_L8_rep2                        L6_L8      L L6_L8.L
Q3_rep1                              Q3      Q    Q3.Q
Q3_rep2                              Q3      Q    Q3.Q
Q5_Q7_rep1                        Q5_Q7      Q Q5_Q7.Q
Q5_Q7_rep2                        Q5_Q7      Q Q5_Q7.Q

design.matrix <- model.matrix(~0+Group,design)

colnames(design.matrix) <- levels(Group)

my.contrasts_1 <- makeContrasts(QvsL = (Q3.Q+Q5_Q7.Q)/2-(L4.L+L6_L8.L)/2, levels = design.matrix)

my.contrasts_2 <- makeContrasts(QvsL = (Q3.Q+Q5_Q7.Q)-(L4.L+L6_L8.L), levels = design.matrix)

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The contrasts are the same from a hypothesis testing perspective. I'd use the contrast 1 however (the difference between the averages) as the contrast coefficient that it returns will look like the difference on a log2 boxplot

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