6 weeks ago by
Since the datasets are from different sources there will be a batch effect between them! This unfortunatly means you cannot directly compare the datasets as you do not know what changes are due to the condition changes and which are due to the batch effect.
If there are samples witch should be identical/similar (e.g. the same untreated cell line used for control in both datasets) you can use that to estimate the batch effect by incorporating the batch effect into e.g. a differential expression model.
If there are no overlap (e.g. one study is normal healthy samples and the other study contains the diseased states) the only way to analyse the data is to do an intra-study analysis and compare them afterwards. Such analysis will always have to be based on rank. Examples could be:
- Rank genes based on average expression and look for large rank changes.
- Use fGSEA on the expression in each dataset and compare the gene-set ranks.
Hope this gets you started