You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Hi all, great tool! Just checking if any update on this?
I have tried a few variations to input only scRNAseq data by itself with no other modes, but the model seems to fail to fully train.
I am doing the following:
`
adata = mtm.data.organize_multimodal_anndatas(
adatas=[[adata_ref]], # a list of anndata objects per modality, RNA-seq always goes first
layers=[["counts"]], # if need to use data from .layers, if None use .X
)
I get the following error:
Expected parameter loc (Tensor of shape (256, 16)) of distribution Normal(loc: torch.Size([256, 16]), scale: torch.Size([256, 16])) to satisfy the constraint Real(), but found invalid values:
tensor([[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
...,
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan],
[nan, nan, nan, ..., nan, nan, nan]], device='cuda:0',
grad_fn=)
Which I presume has something to do with my single modality approach, as I can train the same dataset using scVI, MultiVI and scPoli fine (with the same hvg selections and preprocessing).
The way to enocde the data and dataloader for single-modality datasets is not clear
The text was updated successfully, but these errors were encountered: