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Upload README.md with huggingface_hub

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  - annotated:False
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  ---
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- # Description
 
 
 
 
 
 
 
 
 
 
 
 
 
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  scVI model trained on synthetic IID data and uploaded with the full training data.
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- # Model properties
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- Many model properties are in the model tags. Some more are listed below.
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- **model_init_params**:
 
 
 
 
 
 
 
 
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  ```json
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  {
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  "n_hidden": 128,
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  }
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  ```
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- **model_setup_anndata_args**:
 
 
 
 
 
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  ```json
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  {
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  "layer": null,
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  }
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  ```
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- **model_summary_stats**:
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- | Summary Stat Key | Value |
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- |--------------------------|-------|
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- | n_batch | 1 |
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- | n_cells | 400 |
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- | n_extra_categorical_covs | 0 |
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- | n_extra_continuous_covs | 0 |
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- | n_labels | 1 |
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- | n_vars | 100 |
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- **model_data_registry**:
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  | Registry Key | scvi-tools Location |
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  |--------------|---------------------------|
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  | X | adata.X |
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  | batch | adata.obs['_scvi_batch'] |
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  | labels | adata.obs['_scvi_labels'] |
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- **model_parent_module**: scvi.model
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- **data_is_minified**: False
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- # Training data
 
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- This is an optional link to where the training data is stored if it is too large
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- to host on the huggingface Model hub.
 
 
 
 
 
 
 
 
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- <!-- If your model is not uploaded with any data (e.g., minified data) on the Model Hub, then make
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- sure to provide this field if you want users to be able to access your training data. See the
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- scvi-tools documentation for details. -->
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- Training data url: N/A
 
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- # Training code
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- This is an optional link to the code used to train the model.
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- Training code url: N/A
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  # References
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- To be added...
 
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  - annotated:False
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  ---
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+
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+ ScVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space, integrate technical batches and impute dropouts.
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+ The learned low-dimensional latent representation of the data can be used for visualization and clustering.
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+
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+ scVI takes as input a scRNA-seq gene expression matrix with cells and genes.
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+ We provide an extensive [user guide](https://docs.scvi-tools.org/en/1.2.0/user_guide/models/scvi.html).
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+
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+ - See our original manuscript for further details of the model: [scVI manuscript](https://www.nature.com/articles/s41592-018-0229-2).
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+ - See our manuscript on [scvi-hub](https://www.biorxiv.org/content/10.1101/2024.03.01.582887v2) how to leverage pre-trained models.
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+
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+ This model can be used for fine tuning on new data using our Arches framework: [Arches tutorial](https://docs.scvi-tools.org/en/1.0.0/tutorials/notebooks/scarches_scvi_tools.html).
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+
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+
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+ # Model Description
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  scVI model trained on synthetic IID data and uploaded with the full training data.
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+ # Model Properties
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+ We provide here key parameters used to setup and train the model.
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+ <!-- If your model is not uploaded with any data (e.g., minified data) on the Model Hub, then make
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+ sure to provide this field if you want users to be able to access your training data. See the
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+ scvi-tools documentation for details. -->
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+ **Training data url**: N/A
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+
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+ <details>
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+ <summary><strong>Model Parameters</strong></summary>
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+
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+ These provide the settings to setup the original model:
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  ```json
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  {
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  "n_hidden": 128,
 
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  }
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  ```
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+ </details>
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+
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+ <details>
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+ <summary><strong>Setup Data Arguments</strong></summary>
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+ Arguments passed to setup_anndata of the original model:
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  ```json
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  {
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  "layer": null,
 
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  }
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  ```
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+ </details>
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+
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+ <details>
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+ <summary><strong>Data Registry</strong></summary>
 
 
 
 
 
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+ Registry elements for AnnData manager:
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  | Registry Key | scvi-tools Location |
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  |--------------|---------------------------|
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  | X | adata.X |
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  | batch | adata.obs['_scvi_batch'] |
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  | labels | adata.obs['_scvi_labels'] |
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+ - **Data is Minified**: False
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+ </details>
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+ <details>
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+ <summary><strong>Summary Statistics</strong></summary>
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+ | Summary Stat Key | Value |
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+ |--------------------------|-------|
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+ | n_batch | 1 |
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+ | n_cells | 400 |
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+ | n_extra_categorical_covs | 0 |
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+ | n_extra_continuous_covs | 0 |
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+ | n_labels | 1 |
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+ | n_vars | 100 |
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+
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+ </details>
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+ <details>
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+ <summary><strong>Training</strong></summary>
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+ If provided by the original uploader, for those interested in understanding or replicating the training process, the code is available at the link below.
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+ **Training Code URL**: N/A
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+ </details>
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  # References
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+ To be added...