scembed.methods.HVGMethod

class scembed.methods.HVGMethod(adata, layer=None, n_top_genes=None, min_mean=None, max_mean=None, min_disp=None, max_disp=None, span=None, n_bins=None, flavor=None, check_values=None, scale=False, provide_batch=True, **kwargs)

Highly Variable Genes method using scanpy.

Uses scanpy [WAT18] for highly variable gene selection.

Methods table

fit()	Fit HVG method - no explicit fitting needed.
fit_transform()	Fit the method and transform the data.
get_model_info()	Get information about the fitted model.
load_artifact(source[, artifact_type, ...])	Load a pre-trained model or embedding from various sources.
save_embedding([format_type, filename, ...])	Save embedding to file with preserved cell names as index.
save_model(path)	Save the trained model (for deep learning methods).
setup([force_recompute])	Setup data preprocessing for the integration method.
transform()	Apply HVG selection and use HVG expression as embedding.
validate_adata(adata)	Validate the AnnData object has required keys and structure.
validate_spatial_adata(adata)	Validate spatial-specific data requirements.

Methods

HVGMethod.fit()

Fit HVG method - no explicit fitting needed.

HVGMethod.fit_transform()

Fit the method and transform the data.

Modifies self.adata in place.

Return type:

None

HVGMethod.get_model_info()

Get information about the fitted model.

Return type:

dict[str, Any]

Returns:

Dict[str, Any] Dictionary with model information.

HVGMethod.load_artifact(source, artifact_type='model', embedding_key=None, **kwargs)

Load a pre-trained model or embedding from various sources.

Parameters:

• source (str | Path | dict) – Source of the artifact. Can be: - str/Path: Local path to model directory or embedding file - dict: WandB parameters with keys ‘run_id’, ‘entity’, ‘project’

• artifact_type (Literal['model', 'embedding'] (default: 'model')) – Type of artifact to load: ‘model’ or ‘embedding’.

• embedding_key (str | None (default: None)) – Key to store embedding in adata.obsm. If None, uses self.embedding_key. Only used when artifact_type=’embedding’.

• **kwargs – Additional arguments passed to loading functions.

Return type:

None

HVGMethod.save_embedding(format_type='parquet', filename=None, compression=True)

Save embedding to file with preserved cell names as index.

Parameters:

• format_type (Literal['parquet', 'pickle', 'h5'] (default: 'parquet')) – Format to save embedding in. Options: ‘parquet’, ‘pickle’, or ‘h5’.

• filename (str | None (default: None)) – Custom filename (without extension). If None, uses “embedding”.

• compression (bool (default: True)) – Whether to use compression (gzip for all formats).

Return type:

Path

Returns:

Path Path to the saved embedding file.

Raises:

ValueError – If method is not fitted or embedding key not found in adata.obsm.

HVGMethod.save_model(path)

Save the trained model (for deep learning methods).

Parameters:

path (Path) – Directory to save the model.

Return type:

Path | None

Returns:

Optional[Path] Path to saved model file, None if method doesn’t support saving.

HVGMethod.setup(force_recompute=False)

Setup data preprocessing for the integration method.

Prepares the data for method-specific training/inference and stores the result in self.setup_state[“adata_prepared”].

Parameters:

force_recompute (bool (default: False)) – Whether to force recomputation of preprocessing steps.

Return type:

None

HVGMethod.transform()

Apply HVG selection and use HVG expression as embedding.

HVGMethod.validate_adata(adata)

Validate the AnnData object has required keys and structure.

Parameters:

adata (AnnData) – Annotated data object to validate.

Raises:

ValueError – If required keys are missing or data is malformed.

Return type:

AnnData

HVGMethod.validate_spatial_adata(adata)

Validate spatial-specific data requirements.

Parameters:

adata (AnnData) – Annotated data object to validate.

Raises:

ValueError – If required spatial keys are missing or data is malformed.

Return type:

None