Thanks for above reply Mike - that's a cool idea which I'll use.

However, applying this to a different experimental design, this probably isn't the best way right. For example:

No. tissue      infection       rep condition
1   Bladder     Bacteria_A      1   Bladder_bact_A
2   Bladder     Bacteria_A      2   Bladder_bact_A
3   Bladder     Bacteria_B      1   Bladder_bact_B
4   Bladder     Bacteria_B      2   Bladder_bact_B
5   Bladder     Mock-infected   1   Bladder_mock
6   Bladder     Mock-infected   2   Bladder_mock
7   Kidney      Bacteria_A      1   Kidney_bact_A
8   Kidney      Bacteria_A      2   Kidney_bact_A
9   Kidney      Bacteria_B      1   Kidney_bact_B
10  Kidney      Bacteria_B      2   Kidney_bact_B
11  Kidney      Mock-infected   1   Kidney_mock
12  Kidney      Mock-infected   2   Kidney_mock

How does this look if I was to apply RUVs?

differences <- makeGroups(c("A","A","B","B","C","C","D","D","E","E","F","F"))
differences

set <- newSeqExpressionSet(counts(dds)) 
set <- newSeqExpressionSet(counts(dds, normalized = TRUE)) #Does this line make more sense to use?

idx  <- rowSums(counts(set) > 5) >= 2
set  <- set[idx, ]

set3 <- RUVs(set, genes, k=1, differences)

ddsruv <- dds
ddsruv$W1 <- set3$W[,1]
design(ddsruv) <- ~ W1 + condition


dds2 <- DESeq(ddsRUVs)
res <- results(dds2, contrast=c("condition","Bladder_bact_A","Bladder_mock")) #this does't incorporate the RUV factors right

So, I'd need to create a model.matrix (m1 below) as outlined here and run something like this?

dds2 <- DESeq(ddsRUVs, test="LRT", betaPrior=FALSE, full=m1, reduced=~0 + W1 + condition) 
res1 <- results(dds2, contrast=c("condition","Bladder_bact_A","Bladder_mock")) 
res2 <- results(dds2, contrast=c("condition","Bladder_bact_B","Bladder_mock"))

Thanks in advance - and apologies for all the nested questions