We are having a bit of trouble figuring out how explore our data, given our experimental design. Please allow me to detail the design.

We have three human cancer samples, taken from the same tumor, each coming from a very specific section of the tumor, and each sequenced at a high depth (100 M reads or more). I’m not sure if this would be best considered a single condition with three biological replicates, or three samples with one replicate each. In any event, what we are wanting to do, is to explore the similarities and differences between these samples.

Could you please offer some guidance, on how we might use the package to explore the aforementioned data? Basically, is there any way to do statistical testing (e.g., differential expression, etc.) or are we stuck with only looking at EDA-like analyses as outlined in the recent paper in F1000, “RNA-seq workflow: gene-level exploratory analysis…”?

Thanks

DESeq2 doesn't offer null hypothesis testing for single replicate data. You can create a DESeqDataSet with a design of ~1, and then you can compute the vst(), and make plots using the variance stabilized data (so, yes, you're stuck with just EDA). It's not possible to perform differential expression testing without replicates though in DESeq2.