Comments (2)
ok, I added a feature in which you get the fixed z
as I explained. You need to set
return_mean
= True
in network.get_latent(reference_adata,condition_key, return_mean=True)
please pull from repo to have this feature :)
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Hi Nik,
This comes up due to the underlying structure of get_latent
function. When you project a cell to latent space you have a fixed value for that cell in z, however since the model is generative, a noise term will be added to this z. Thus, you have z = z_fix
+ N(0,1) in which N(0,1) is a sample from a random Gaussian that is why you get different values every time you use get_latent
. There is a way to avoid this that I provide the ability to either get z
or z_fix
. However, in total there should not be a big difference that avoids reproducibility.
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Related Issues (20)
- installation issue HOT 1
- Matrix multiplication error in scpoli_model.classify HOT 13
- "get_num_classes" not available in newer torchmetrics versions HOT 2
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- Include both gene IDs and gene symbols for check of feature correspondence between query and reference
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- Trouble with expimap -- float values wanted when anndata is in double HOT 5
- ImportError: cannot import name 'setup_anndata' from 'scvi.data' HOT 2
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- Install issue - no .yml in repo HOT 2
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