Downloads · 30 days
0
valsv/mmColon_single_cell_85K.mouse.scvi.model
mmColon_single_cell_85K.mouse.scvi.model is a machine learning model from valsv. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for scvi-tools. The card lists the license as cc-by-4.0.
Downloads · 30 days
0
Access
Public
Updated Feb 12, 2024
Repo size
177 MB
Likes
0
Public
Click a slice to open those files.
.h5ad129 MB · 73%
From the Hugging Face model README
mmColon_single_cell_85K.mouse.h5ad
Many model properties are in the model tags. Some more are listed below.
model_init_params:
{
"n_hidden": 128,
"n_latent": 10,
"n_layers": 1,
"dropout_rate": 0.1,
"dispersion": "gene",
"gene_likelihood": "nb",
"latent_distribution": "normal"
}
model_setup_anndata_args:
{
"layer": null,
"batch_key": null,
"labels_key": null,
"size_factor_key": "total_counts",
"categorical_covariate_keys": null,
"continuous_covariate_keys": null
}
Training curve:

Embedding plots:

model_summary_stats:
| [1m [0m[1m Summary Stat Key [0m[1m [0m | [1m [0m[1mValue[0m[1m [0m |
|---|---|
| [94m [0m[94m n_batch [0m[94m [0m | [35m [0m[35m 1 [0m[35m [0m |
| [94m [0m[94m n_cells [0m[94m [0m | [35m [0m[35m84612[0m[35m [0m |
| [94m [0m[94mn_extra_categorical_covs[0m[94m [0m | [35m [0m[35m 0 [0m[35m [0m |
| [94m [0m[94mn_extra_continuous_covs [0m[94m [0m | [35m [0m[35m 0 [0m[35m [0m |
| [94m [0m[94m n_labels [0m[94m [0m | [35m [0m[35m 1 [0m[35m [0m |
| [94m [0m[94m n_latent_qzm [0m[94m [0m | [35m [0m[35m 10 [0m[35m [0m |
| [94m [0m[94m n_latent_qzv [0m[94m [0m | [35m [0m[35m 10 [0m[35m [0m |
| [94m [0m[94m n_vars [0m[94m [0m | [35m [0m[35m18416[0m[35m [0m |
model_data_registry:
| [1m [0m[1m Registry Key [0m[1m [0m | [1m [0m[1m scvi-tools Location [0m[1m [0m |
|---|---|
| [94m [0m[94m X [0m[94m [0m | [35m [0m[35m adata.X [0m[35m [0m |
| [94m [0m[94m batch [0m[94m [0m | [35m [0m[35m adata.obs['_scvi_batch'] [0m[35m [0m |
| [94m [0m[94m labels [0m[94m [0m | [35m [0m[35m adata.obs['_scvi_labels'] [0m[35m [0m |
| [94m [0m[94m latent_qzm [0m[94m [0m | [35m [0m[35m adata.obsm['_scvi_latent_qzm'] [0m[35m [0m |
| [94m [0m[94m latent_qzv [0m[94m [0m | [35m [0m[35m adata.obsm['_scvi_latent_qzv'] [0m[35m [0m |
| [94m [0m[94m minify_type [0m[94m [0m | [35m [0m[35madata.uns['_scvi_adata_minify_type'][0m[35m [0m |
| [94m [0m[94mobserved_lib_size[0m[94m [0m | [35m [0m[35madata.obs['_scvi_observed_lib_size'][0m[35m [0m |
| [94m [0m[94m size_factor [0m[94m [0m | [35m [0m[35m adata.obs['total_counts'] [0m[35m [0m |
model_parent_module: scvi.model
data_is_minified: True
This is an optional link to where the training data is stored if it is too large to host on the huggingface Model hub.
<!-- If your model is not uploaded with any data (e.g., minified data) on the Model Hub, then make sure to provide this field if you want users to be able to access your training data. See the scvi-tools documentation for details. -->Training data url: N/A
This is an optional link to the code used to train the model.
Training code url: N/A
10.6084/m9.figshare.24670038.v1