SEQanswers DESeq2: Difference between condition+type vs. 3 conditions
 Register FAQ Members List Calendar Search Today's Posts Mark Forums Read

 Similar Threads Thread Thread Starter Forum Replies Last Post sisterdot Bioinformatics 23 02-24-2019 09:01 AM Simon Anders Bioinformatics 123 07-06-2015 02:45 AM sebastion RNA Sequencing 35 10-16-2014 07:04 AM ToddB Bioinformatics 13 09-05-2013 07:22 AM Noa Bioinformatics 1 05-10-2012 08:01 PM

 08-21-2013, 01:08 AM #1 mevers Junior Member   Location: Germany Join Date: Aug 2013 Posts: 2 DESeq2: Difference between condition+type vs. 3 conditions 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 Code: ``` condition type sample1 A T1 sample2 A T1 sample3 B T2 sample4 B T2 sample5 A T2 sample6 A T2``` I am unsure about how this would be treated differently from Code: ``` condition sample1 A:T1 sample2 A:T1 sample3 B:T2 sample4 B:T2 sample5 A:T2 sample6 A:T2``` The second design table describes a 3-condition scenario. 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")` and performing a DESeq2 analysis Code: ```dds<-DESeqDataSetFromMatrix(countData = countData, colData = design, design = ~ condition + type); dds<-DESeq(dds);``` 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 Last edited by mevers; 08-22-2013 at 02:38 AM. Reason: Typo
 08-22-2013, 01:39 AM #2 Simon Anders Senior Member   Location: Heidelberg, Germany Join Date: Feb 2010 Posts: 994 In your first table, the type is always the same. Is this a typo? If not, I'm not sure I understand your question.
 08-22-2013, 02:35 AM #3 mevers Junior Member   Location: Germany Join Date: Aug 2013 Posts: 2 Hi Simon. Yes, that was a silly mistake, you are absolutely right. I've changed it now in the original post. It should have read Code: `type=c("T1","T1","T2","T2","T2","T2")` Cheers, Maurits
 09-02-2013, 02:17 AM #4 Michael Love Senior Member   Location: Boston Join Date: Jul 2013 Posts: 333 Question 1: You can technically represent it either way, although I would recommend to keep the variables separate for the following reason: if you combined the variables (as in "A:T1"), then you cannot make a clean B vs A comparison. Instead you have a B:T2 vs A:T1 comparison which mixes the effect of B vs A and T2 vs T1. Question 2: Note that fitType is also an argument for DESeq() Question 3: Both variables are used for finding fitted means (mu in the GLM formula given in the reference manual and vignette). And then the fitted means mu is used to estimate the dispersion. Dispersion is a measure of how far the counts deviate from the mu for that sample. Both variables will have fitted coefficients (betas in the GLM formula) and you can extract tests for each variable of the null hypothesis that the coefficients are equal to zero. By default the results for the last variable is provided by results(). For more, see the section in the vignette on "Multi-factor designs" and the man page for results().

 Tags deseq, deseq2