Dear all.
I am unsure about how to use DESeq2 in the case of 3 conditions vs. 2 conditions + 2 types. Assuming I have the following design table
I am unsure about how this would be treated differently from
The second design table describes a 3-condition scenario.
Now, obviously one would be interested in a detailed analysis of the counts for
Question 1: If I reduce the problem to that of a 3-condition no-type design table, is this correctly taken into account?
I know I would have to re-factor the columns of the 2nd matrix to reflect the correct order of fold changes that I want to calculate. So for example following re-factoring the levels as
and performing a DESeq2 analysis
Question 2: I could calculate the fold changes of B:T2 wrt A:T2 and A:T1 wrt A:T2, is this correct?
I do get some issues with non-convergent dispersion fits, which I can get around if I call estimateDispersions manually with fitType="local".
Question 3: But what happens in the case of the 1st condition+type table? I am confused as to the output of DESeq2. What role does the type play in the differential expression analysis and/or the dispersion fitting?
Any help on this issue would be greatly appreciated.
Regards,
Maurits
I am unsure about how to use DESeq2 in the case of 3 conditions vs. 2 conditions + 2 types. Assuming I have the following design table
Code:
condition type sample1 A T1 sample2 A T1 sample3 B T2 sample4 B T2 sample5 A T2 sample6 A T2
Code:
condition sample1 A:T1 sample2 A:T1 sample3 B:T2 sample4 B:T2 sample5 A:T2 sample6 A:T2
Now, obviously one would be interested in a detailed analysis of the counts for
- A:T2 vs. B:T2 (since they have the same type but a different conditions), and potentially
- A:T2 vs. A:T1 (since they have the same condition but different types).
Question 1: If I reduce the problem to that of a 3-condition no-type design table, is this correctly taken into account?
I know I would have to re-factor the columns of the 2nd matrix to reflect the correct order of fold changes that I want to calculate. So for example following re-factoring the levels as
Code:
levels=c("A:T2","B:T2","A:T1")
Code:
dds<-DESeqDataSetFromMatrix(countData = countData, colData = design, design = ~ condition + type); dds<-DESeq(dds);
I do get some issues with non-convergent dispersion fits, which I can get around if I call estimateDispersions manually with fitType="local".
Question 3: But what happens in the case of the 1st condition+type table? I am confused as to the output of DESeq2. What role does the type play in the differential expression analysis and/or the dispersion fitting?
Any help on this issue would be greatly appreciated.
Regards,
Maurits
Comment