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.scrubet method after normal filtering and pearson residuals’ norm and hvg steps?