Hi,
I have 8 RNA-Seq samples. Among them 4 are controls and other 4 are treatment. I'm interested in doing differential analysis with edgeR. Following is the column data.
Samples Type
Sample1 Control
Sample2 Control
Sample5 Control
Sample6 Control
Sample7 Treatment
Sample8 Treatment
Sample3 Treatment
Sample4 Treatment
Among the above table Sample1, Sample2 [Controls] and Sample3, Sample4 [Treatment] are done on one day and Sample5, Sample6 [Controls] and Sample7, Sample8 [Treatment] are done on other day.
As you see the replicates were not processed together, there is the batch effect. In this way how I can create the design matrix in edgeR for differential analysis.
1 answer
I had a look into it. This is the first time I'm working with such data. Could you please tell me whether this is right or not.
coldata
Samples Type Replicates
Sample1 Control rep1
Sample2 Control rep1
Sample5 Control rep2
Sample6 Control rep2
Sample7 Treatment rep2
Sample8 Treatment rep2
Sample3 Treatment rep1
Sample4 Treatment rep1
group <- factor(paste0(coldata$Type))
I crated design matrix like following:
design <- model.matrix(~ 0 + group + coldata$Replicates)
colnames(design) <- c("Control","Treatment","Repl")
And the design looks like below:
Control Treatment Repl
1 1 0 0
2 1 0 0
3 0 1 0
4 0 1 0
5 1 0 1
6 1 0 1
7 0 1 1
8 0 1 1
Then i have used following commands for linear model fit and DEA.
y <- estimateDisp(y, design, robust=TRUE)
fit <- glmQLFit(y, design, robust=TRUE)
contrast.matrix <- makeContrasts(Treatment-Control, levels=design)
contrast.matrix
Do you think this is right?
Yes, they were the same sample but RNA extraction is done on the next day.
As mentioned above Sample1, Sample2 [Controls] and Sample3, Sample4 [Treatment] are done on one day and Sample5, Sample6 [Controls] and Sample7, Sample8 [Treatment] are done on other day
All the samples are from the same cell-line.
How can you be sure that this 'batch' effect is going to bias your results? - what evidence have you seen? In many experiments, samples are processed on separate days with minimal or no effect on the end results. If we did a time-course experiment, for example, and assumed that time was a batch effect, then we would wipe out the very differences that we wanted to find based on time.
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