dealing with read counts under PE and SE scenarios

Hi,

I am unsure how to deal with this case to go about analysing RNA-seq data.

Suppose that you have a control and treatment setup with 4 biological replicates each. However, two in control and two in treatment were pooled together and paired-end sequenced and the other 4 were single-end sequenced. That is:

Condition    Sample_No    Sequence_Type
Control        1,2          paired-end
Control        3,4          single-end
Treatment      1,2          paired-end
Treatment      3,4          single-end

Now, suppose I'd want to perform a differential gene expression analysis, how would you take care of the difference in the reads (due to PE and SE) within conditions?

i) You map the reads as such - PE as PE and SE as SE libraries. You count the total number of reads that fall under each sample. You then normalize using edgeR's TMM method for difference in the library size and then perform the differential expression analysis.
ii) You map the reads as such - PE as PE and SE as SE libraries. You count the total number of reads first in pair (meaningful for PE samples) that fall under each sample. You then normalize using edgeR's TMM method for difference in the library size and then perform the differential expression analysis.
iii) You discard the second pair altogether and treat them as two conditions with 8 SE libraries.

Understanding the inherent mess-up in the experimental setup and the possibility of bias and accepting them, which one would you prefer and why?

I personally prefer (i) because, I think PE mappings are much more efficient than SE mappings. So, I don't want to lose this info (rules out iii). And this allows us for a better estimate of gene expression in two of the four replicates under each condition. Then by counting the total number of reads, and normalising for the difference in the library sizes, we attempt to compensate for the difference. The SE library would have the same pattern of expression genome-wide within each condition and therefore we just normalise for library size (just a scaling), which I guess makes sense. And I don't think there is a difference between (i) and (ii) except maybe (i) has more power during statistical testing due to more confidence, although not quite sure if its desirable. What do you guys think?