I am attempting to integrate different bulk RNA-Seq datasets. While this is not ideal, I'm trying to reduce the technical variability in these datasets by using data generated by similar protocols (for example, all of them have been sequenced with a Poly-A enrichment strategy and Illumina short read sequencing). I am aware the results I obtain from this analysis will be very limited, but still want to pursue the analysis. I'm looking for advice on two aspects of the analysis, but first some more info about the data:
- There are 5 bulk RNA-Seq datasets and 4 biological groups. Each datasets only comprises one biological group. Only two biological groups have two datasets, and such datasets overlap in PCA.
- In Principal Component Analysis (PCA) and the first PC (~26% of variation) is very correlated with the biological variable (cell differentiation), suggesting there is a biological signal among all the noise/technical bias.
Despite these preliminary results I'm not sure I'm accounting correctly for some aspects: The datasets have been sequenced with different sequencing coverage (in fact, different numbers and length of reads). Some are single read and some paired-end. My questions are:
For PCA, I'm normalizing with variance stabilizing transformation (VST). How should I account for the differences in read numbers and type between datasets?
For differential expression (DE) analysis I'm using DESeq2. How should I account for the differences in read numbers and type between datasets? There is no batch variable, as all datasets come from different sources (batches, laboratories)
I'm happy to provide more information if necessary. Thanks