Here's a pairs plot of your counts in the log scale
Code:
y <- log10(counts(dds)+1) pairs(y, panel = function(...) smoothScatter(..., nrpoints = 0, add = TRUE),lower.panel=NULL)
However, for water vs others, a simple scaling factor automatically detected from the data will not work.
For the scatterplot of 1 vs 3 and 1 vs 10, there seems to be a faint line of genes on the diagaonal. Maybe you can investigate what is special about these genes. It is possible that nearly all the genes are differentially expressed (upregulated in the treated groups), but then the experiment really should use spike in controls for normalization.
I wonder if the experimental protocol might have been different for the water samples?
Another option for analysis would be to remove the water samples and use the 'contrast' argument to just compare the treatment groups against each other.
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