Scvi - denoising single-cell/single-nucleus transcription data


Thank you for the wonderful tool and efforts in maintaining this community.

We have been very interested in working with denoised single-cell/single-nucleus transcription vectors. Specifically, we are very interested in obtaining the batch-corrected “count” vectors after denoising. We have been using the scVI tool and found the following:

  1. For our project, we have been using scVI to run the raw counts from a cohort with about 12 separately-sequenced individuals (treated as batches). In addition, we included all the genes (rather than just the highly variable genes) because we want as many genes as possible for downstream analysis. We found that the latent space was very well batch-corrected, but the denoised vectors still showed batch-specific clustering. Is there something we should be doing differently, beyond following the tutorial?
  2. Are there any recommendations beyond using the original scVI model? Specifically, are there any updates or more recent approaches in your tool suite that we should consider?

Thank you in advance for any response!

Hi, what do you hope another tool to do? I can then link to other tools within scVI for this purpose. The normalized expression is by default not batch corrected but only when you provide transform_batch the generated counts will be projected to this batch leading to corrected gene expression