Hi,
Really thank you for providing scvi tool in seurat environment.
I recently happened to a problem:
I checked online, and found that there seems to be a solution:
https://github.com/scverse/scvi-tools/issues/2314
"Hi, this unfortunately occurs when the last batch of the training set contains one observation, leading to the batch norm layer complaining. You can solve this by passing in a batch_size to the train method that is different than the default one."
However, in seurat-wrapppers function
(```
#' obj <- IntegrateLayers(object = obj, method = scVIIntegration,
#' orig.reduction = "pca", new.reduction = 'integrated.scvi',
#' assay = "SCT", conda_env = '../miniconda3/envs/scvi-env', verbose = FALSE).
```), I can't modify the parameters provided to the train method.
Could you please help with this problem? Many thanks!
Hi,
Really thank you for providing scvi tool in seurat environment.
I recently happened to a problem:
I checked online, and found that there seems to be a solution:
https://github.com/scverse/scvi-tools/issues/2314
"Hi, this unfortunately occurs when the last batch of the training set contains one observation, leading to the batch norm layer complaining. You can solve this by passing in a batch_size to the train method that is different than the default one."
However, in seurat-wrapppers function
(```
#' obj <- IntegrateLayers(object = obj, method = scVIIntegration,
#' orig.reduction = "pca", new.reduction = 'integrated.scvi',
#' assay = "SCT", conda_env = '../miniconda3/envs/scvi-env', verbose = FALSE).
```), I can't modify the parameters provided to the
trainmethod.Could you please help with this problem? Many thanks!