Hi,

I am pretty new to DESeq2. I have read the manual and many question posts, but am still not sure if my design is appropriate or not. Could anyone help me with it?

I have samples from 2 tissues (kidney, liver) with 2 treatment condition (control, Drug) at 2 timepoint (1day, 5days). There are 3 replicates for each situation showing as below. 

I'd like to see 1) the effect of the treatment across time in the two tissues and 2)the treatment effect between liver and kidney.

My first design is  

dds <- DESeqDataSetFromHTSeqCount(sampleTable=meta, directory=InFolder, design=~treatment+time+tissue+treatment:time)

dds <- DESeq(dds, test="LRT", reduced=~treatment+time+tissue)

> resultsNames(ddsLRT)
[1] "Intercept"                      "treatment_treatment_vs_control"
[3] "time_5days_vs_1day"             "tissue_liver_vs_kidney"
[5] "treatmentDrug.time5days"

To answer question 1), I use res <- results(dds, contrast = list(c("time_5days_vs_1day", "treatment_Drug_vs_control")), test="Wald"). But I think it is for kidney. How do I get the effect for liver when I don't have an interaction term between tissue and other things? Or should I use design=~treatment+time+tissue+treatment:time+treatment:tissue instead? If it is the case, is it correct for me to use "reduced=~treatment+time+tissue+treatment:tissue"?

I tried both and the results are a little different, which one is correct?

To answer question 2), can I just use the "tissue_liver_vs_kidney" results with "reduced=~treatment+time+tissue"? Or do I need to use "tissue_liver_vs_kidney" with "reduced=~treatment+time+treatment:time"?

Please let me know if my design is appropriate for my purpose.

Thanks a lot!

The way I would approach this experiment is to use the following design ~tissue + time + tissue:treatment + tisse:treatment:time.

This gives you coefficents to account for different baselines for the tissues, different baselines for the two time points, different treatment effects in each tissue at the first time point, and then tissue- and treatment-specific changes between the two time points (so four overall, because there are four combinations of tissue and treatment).

You can take a look at the resultsNames(dds) for these, and they will line up with my description above. I'd recommend meeting with a local statistician to discuss these coefficients if you are new to linear models (there is nothing pariticular about DESeq2 here, these are the same coefficients produced by any linear model in R).