N.B. Obviously, these results are what they are, and I expect they are true, but I wanted to see whether I've missed anything.
I've done a comparative analysis of RNAseq as I've done many a time before, using tophat, HTSeq and edgeR. Mapping was successful (80%+) and everything appeared fine. I have been doing the analysis for colleagues and the dataset is in a different species to which I'm used, but I can't see anything different, but it is less than successful.
Setup is like this with the table representing number of genes with Fischer's exact test at FDR <0.05:
vs | Control (x2) | Untreated (x3) | Untreated (excluding genes found in Control vs Untreated) Treatment1 (x3) | 0 | ~300 | ~90 | Treatment2 (x3) | ~10 | ~500 | ~200 | Treatment3 (x3) | ~10 | ~400 | ~150 |
There were only 2 transfection controls as one sample failed. The 'Untreated' was a cell line which did not undergo transfection. All ~10 stand out genes in the 'successful' treatments were snRNA such as U1 spliceosomal RNA (e.g.), with FDR upwards of E-100. These are also observed in the Untreated vs Treatments, but they are absent from Untreated vs Transfection Control, indicating Treatments 1&2 are the cause.
Long story short, my colleagues were expecting more genes and more mainstream pathways, and I'm unsure whether there could be something obscuring them with this snRNA business. Alternatively, are there any recommendations on analytical techniques to tease them out differently?