Hi,

I have an RNA-seq dataset where a treatment has been applied and treatment effects measured in replicate across several time points post-exposure. I have 3 treatment and 3 control replicates for each time point. I then run DESeq2's LR test below to find genes affected by treatment at any time point.

full_model ~ extraction_batch + SVs + timepoint + treatment + treatment:timepoint 
reduced_model ~ extraction_batch + SVs + timepoint

However, when I plot the model coefficients obtained using the coeff() function, I notice that the intercept is huge across all genes, whereas the other coefficients / LFCs are much smaller by comparison. What does this mean? Am I doing something wrong? Given the biological question I want to answer, I am positive these are the models I should use.

Thank you in advance for your advice.

Denise

sessionInfo( )

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The default parameterization in R is a treatment-contrasts parameterization that sets one of your groups as the baseline. So the intercept is the average expression value (I believe logCPM for DESeq2, but don't quote me on that) for the baseline group, which you can identify by inspecting your design matrix.