[BioC] DESeq2 - paired sample, interaction and fold change

samuel collombet samuelcollombet at gmail.com
Wed Jun 4 10:38:13 CEST 2014

Dear all,

I would like to make an analysis with a multilevel design with an 
interaction therm, with paired samples.

If I have 2 patients, for which I have "tumour" and "control" samples, 
and for each I have them treated with treatment A or B. I want to know 
if treatment A as a specific effect on the tumor versus WT. As a toy 
 > dds <- makeExampleDESeqDataSet(n = 1000, m = 8)
 > dds$patient=c("1","1","1","1","2","2","2","2")
 > dds$condition=c("WT","WT","tumor","tumor","WT","WT","tumor","tumor")
 > dds$treatment=c("A","Ct","A","Ct","A","Ct","A","Ct")
 > dds <- DESeqDataSet(dds, design= ~ patient + condition*treatment )
 > dds <- DESeq(dds)

for which I got:
 > colData(dds)
DataFrame with 8 rows and 5 columns
              sample condition  patient treatment sizeFactor
         <character>  <factor> <factor> <factor>  <numeric>
sample1     sample1        WT        1         A   1.047160
sample2     sample2        WT        1         Ct   1.017620
sample3     sample3     tumor        1         A   1.056423
sample4     sample4     tumor        1         Ct   1.046753
sample5     sample5        WT        2         A   1.014201
sample6     sample6        WT        2         Ct   1.059479
sample7     sample7     tumor        2         A   1.035853
sample8     sample8     tumor        2         Ct   1.013360

 > resultsNames(dds)
[1] "Intercept"              "patient_2_vs_1" "condition_WT_vs_tumor"  
"treatment_Ct_vs_A" "conditionWT.treatmentCt"

If I do:
 > res <- results(dds, name="conditionWT.treatmentCt")

I will get the gene for which there is a specific effect of treatment in 
the tumor, am I right?

I understood that DESeq2 does make test taking into consideration paired 
samples, if the pairing information is put as a factor in the design 
formula (I understood it from 
http://seqanswers.com/forums/archive/index.php/t-34614.html, post by 
simon anders, 10-21-2013, 12:28 AM).

So in the example above, the fold change would be calculated for each 
patients, and the test made on these fold changes, I am wrong?
Is it possible to get the individual fold change for each patients?


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