You're correct that there's no "right" answer (except "more data, better detection"). And the optimal data for differential expression and isoform detection are different, actually somewhat contradictory (more reads vs. longer/paired-end reads).

Re: variable 1, you're correct that more replicates will provide more statistical power. At some number, the marginal improvement in detection will be outweighed by the cost of library construction. This reference provides a cost-benefit analysis that may be useful.

Re: variable 2, increased read length beyond 50bp provides only a modest improvement (a few %) in alignment and offers little benefit for DE. However, it does increase the likelihood of spanning a splice junction, which is necessary for isoform analysis.

Re: variable 3, paired-end reads only modestly improve mapping, but greatly improve isoform detection.

However, the most important variable is the state of the rat genome/transcriptome. If it's fairly complete, then mapping for DE and isoforms is appropriate. If it is relatively incomplete, then you would need to construct transcript models from your data. That would virtually necessitate the use of paired-end 100bp (or longer) reads. Therefore, I would strongly encourage you to evaluate the existing rat data (e.g., the Bodymap study from SEQC) before beginning your experiment.

1) You are right, more replicates will generally increase the power of your differential tests more than increased read depth. The exception is for low expressed genes that require higher depth to be robustly detected.

2) Don't increase read length too much (no more than 100 bp). Longer reads tend to be of lower quality (at least with the current illumina technology) and you'll profit more from shorter paired-end reads than from longer SE reads.

3) I would go 100% for paired-end ! As you said, mapping is easier and it'll help a lot with splice variant detection.

? 4) Another parameter is what you are sequencing. Is it (A)total RNA ? (B)polyA-selected RNA ? (C)rRNA depleted RNA ? Depending on what you choose, it will impact the depth required to robustly measure expression.

A) total RNA is probably a bad idea since 99% of your reads will map on rRNA or tRNAs...

B) polyA selection is great but you can loose information on non-polyA transcripts (some ncRNAs, etc...). But if you are only interested in mRNA, then its fine.

C) rRNA depletion is cool because you only exclude rRNA from your RNA sample. However sometimes it is not 100% efficient and you'll still sequence rRNA (up to 40% of total reads in my experience). So you'll probably need a bit more depth than from poly-A selected samples in that case.