works best if the input is a raw (unnormalized) counts matrix from a single sample or a collection of similar samples from the same experiment. This function is a wrapper around functions that pre-process using Scanpy and directly call functions of Scrublet(). You may also undertake your own preprocessing, simulate doublets with scanpy.external.pp.scrublet_simulate_doublets(), and run the core scrublet function scanpy.external.pp.scrublet.scrublet().
Usually I did scrublet on separate samples’ raw counts without any filtering and using the default wrapper (of log normalization, pca embedding and hvg finder).
How do you suggest me to do? Should I try using scrublet after normal filtering and pearson residuals’ norm and hvg steps?
btw, the pearson residuals do normalization considering the batches. Using scrublet means I need to use pearson residuals separately on each batch of dataset before concat them?
Now I prilimarily decided to filter and normalize before scrublet, then concat and normalze again (on raw layer). Please tell me if you know it’s not the right way.