I am wanting to perform a meta-analysis using 8 publicly available RNAseq datasets which each have disease and control samples (~330 samples in total). A few of these datasets have samples from different histological locations (e.g. low fibrosis vs high fibrosis). These 8 datasets contain two different sequencing platforms (6 datasets on Illumina and 2 datasets on Ion Torrent). What is the best way to remove the batch effects (datasets and platforms) so that I can perform a differential expression analysis?

I have attempted using Combat_seq() to remove the batch effects, but it only allows me to run one batch effect at a time, so I'm not sure if that is a viable approach.

Would it be better to try to add the batch effects to the model in DESeq2?

Alternatively, for this type of meta-analysis, would it be better to run each dataset individually and then perform the meta-analysis using p-value combination or other methods?

I would appreciate any suggestions.

This isn't really the place for general analysis questions (a better choice being biostars.org). That said, I have never personally found combining disparate datasets into one analysis to be particularly useful, but ymmv. I prefer a meta-analysis approach. Depending on the studies, you could use GeneMeta to make comparisons using effect sizes (in which case you are likely better off using limma-voom rather than DESeq2, because GeneMeta expects you to have t-statistics), or you could use metapod to combine using the p-values.