Question: DESeq for paired samples from different time stamps
gravatar for lironyoffe
2.7 years ago by
lironyoffe0 wrote:


We have small RNA Seq data of sick and healthy pregnant women blood samples in 2 stages in their pregnancy. The samples are paired, i.e., for each woman there are 2 samples: one from the first trimester and one from the second trimester. 

I look for transcripts that are differentially expressed between "sick" and "healthy" in the first trimester and in the second trimester separately. Additionally I look for transcripts that their fold change is different between the 2 time points. 

I followed the instructions in with the design formula: ~ condition + trimester + condition:trimester (condition is either 1 which means sick, or 0 which means healthy) and: 

dds <- DESeq(dds, test="LRT", reduced = ~ Trimester + condition, fitType="mean")
res <- results(dds, alpha = 0.05)
res1trimester <- results(dds, name="condition_1_vs_0",alpha = 0.05, test="Wald")
res2trimester <- results(dds, contrast = c(list("condition_1_vs_0","Trimester2.condition1")),alpha = 0.05, test="Wald")

My question is whether this way I'm ignoring the fact that the samples are paired? If so, should I add to the design the woman ID? 

Another question: In a later analysis, I divided the data into 2 separate datasets: 1. samples from the first trimester, and 2. samples from the second trimester. I then analyzed each data set for differential expression between "sick" and "healthy". The results of these analyses were different from the results I got from the DE analysis described above (res1trimester and res2trimester). Shouldn't be the same? am I missing something?  The differences were quite big.. 



ADD COMMENTlink modified 2.7 years ago • written 2.7 years ago by lironyoffe0
Answer: DESeq for paired samples from different time stamps
gravatar for Michael Love
2.7 years ago by
Michael Love26k
United States
Michael Love26k wrote:

Can you describe what is the "condition" here?

ADD COMMENTlink written 2.7 years ago by Michael Love26k

Sorry, I added some missing data to my question, and also added another question regarding the same analysis. The "condition" is either 0 (healthy) or 1 (sick). This is the main feature of the differential expression analysis.

Thanks! :)

ADD REPLYlink written 2.7 years ago by lironyoffe0

Yes, the results are expected to be different when you subset to just pairs of groups of samples as to when you test coefficients in a larger model, most of all because the dispersion estimation will be different. See our FAQ which discusses the trade-off.

ADD REPLYlink written 2.7 years ago by Michael Love26k

With fixed effects, you can do the comparison across trimester within the individuals, but you can't directly compare across condition and control for individual, because individual is nested within condition (and so in a fixed effects model those are confounded variables). You would have to use something like duplicateCorrelation() in limma-voom to make those comparisons.

ADD REPLYlink written 2.7 years ago by Michael Love26k
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