scvi.external.Tangram

class scvi.external.Tangram(sc_adata, constrained=False, target_count=None, **model_kwargs)

Reimplementation of Tangram [Biancalani et al., 2021].

Maps single-cell RNA-seq data to spatial data. Original implementation: broadinstitute/Tangram.

Currently the “cells” and “constrained” modes are implemented.

Parameters:

• mdata – MuData object that has been registered via setup_mudata().

• constrained (bool (default: False)) – Whether to use the constrained version of Tangram instead of cells mode.

• target_count (int | None (default: None)) – The number of cells to be filtered. Necessary when constrained is True.

• **model_kwargs – Keyword args for TangramMapper

Examples

>>> from scvi.external import Tangram
>>> ad_sc = anndata.read_h5ad(path_to_sc_anndata)
>>> ad_sp = anndata.read_h5ad(path_to_sp_anndata)
>>> markers = pd.read_csv(path_to_markers, index_col=0)  # genes to use for mapping
>>> mdata = mudata.MuData(
        {
            "sp_full": ad_sp,
            "sc_full": ad_sc,
            "sp": ad_sp[:, markers].copy(),
            "sc": ad_sc[:, markers].copy()
        }
    )
>>> modalities = {"density_prior_key": "sp", "sc_layer": "sc", "sp_layer": "sp"}
>>> Tangram.setup_mudata(
        mdata, density_prior_key="rna_count_based_density", modalities=modalities
    )
>>> tangram = Tangram(sc_adata)
>>> tangram.train()
>>> ad_sc.obsm["tangram_mapper"] = tangram.get_mapper_matrix()
>>> ad_sp.obsm["tangram_cts"] = tangram.project_cell_annotations(
        ad_sc, ad_sp, ad_sc.obsm["tangram_mapper"], ad_sc.obs["labels"]
    )
>>> projected_ad_sp = tangram.project_genes(ad_sc, ad_sp, ad_sc.obsm["tangram_mapper"])

Notes

See further usage examples in the following tutorials:

1. Spatial mapping with Tangram

Attributes table

adata	Data attached to model instance.
adata_manager	Manager instance associated with self.adata.
device	The current device that the module's params are on.
get_normalized_function_name	What the get normalized functions name is
history	Returns computed metrics during training.
is_trained	Whether the model has been trained.
summary_string	Summary string of the model.
test_indices	Observations that are in test set.
train_indices	Observations that are in train set.
validation_indices	Observations that are in validation set.

Methods table

convert_legacy_save(dir_path, output_dir_path)	Converts a legacy saved model (<v0.15.0) to the updated save format.
deregister_manager([adata])	Deregisters the AnnDataManager instance associated with adata.
get_anndata_manager(adata[, required])	Retrieves the AnnDataManager for a given AnnData object.
get_from_registry(adata, registry_key)	Returns the object in AnnData associated with the key in the data registry.
get_mapper_matrix()	Return the mapping matrix.
get_normalized_expression()
load(dir_path[, adata, accelerator, device, ...])	Instantiate a model from the saved output.
load_registry(dir_path[, prefix])	Return the full registry saved with the model.
project_cell_annotations(adata_sc, adata_sp, ...)	Project cell annotations to spatial data.
project_genes(adata_sc, adata_sp, mapper)	Project gene expression to spatial data.
register_manager(adata_manager)	Registers an AnnDataManager instance with this model class.
save(dir_path[, prefix, overwrite, ...])	Save the state of the model.
setup_anndata()	Not implemented, use setup_mudata.
setup_mudata(mdata[, density_prior_key, ...])	Sets up the AnnData object for this model.
to_device(device)	Move model to device.
train([max_epochs, accelerator, devices, ...])	Train the model.
view_anndata_setup([adata, ...])	Print summary of the setup for the initial AnnData or a given AnnData object.
view_setup_args(dir_path[, prefix])	Print args used to setup a saved model.

Attributes

Tangram.adata

Data attached to model instance.

Tangram.adata_manager

Manager instance associated with self.adata.

Tangram.device

The current device that the module’s params are on.

Tangram.get_normalized_function_name

What the get normalized functions name is

Tangram.history

Returns computed metrics during training.

Tangram.is_trained

Whether the model has been trained.

Tangram.summary_string

Summary string of the model.

Tangram.test_indices

Observations that are in test set.

Tangram.train_indices

Observations that are in train set.

Tangram.validation_indices

Observations that are in validation set.

Methods

classmethod Tangram.convert_legacy_save(dir_path, output_dir_path, overwrite=False, prefix=None, **save_kwargs)

Converts a legacy saved model (<v0.15.0) to the updated save format.

Parameters:

• dir_path (str) – Path to directory where legacy model is saved.

• output_dir_path (str) – Path to save converted save files.

• overwrite (bool (default: False)) – Overwrite existing data or not. If False and directory already exists at output_dir_path, error will be raised.

• prefix (str | None (default: None)) – Prefix of saved file names.

• **save_kwargs – Keyword arguments passed into save().

Return type:

None

Tangram.deregister_manager(adata=None)

Deregisters the AnnDataManager instance associated with adata.

If adata is None, deregisters all AnnDataManager instances in both the class and instance-specific manager stores, except for the one associated with this model instance.

Tangram.get_anndata_manager(adata, required=False)

Retrieves the AnnDataManager for a given AnnData object.

Requires self.id has been set. Checks for an AnnDataManager specific to this model instance.

Parameters:

• adata (AnnData | MuData) – AnnData object to find manager instance for.

• required (bool (default: False)) – If True, errors on missing manager. Otherwise, returns None when manager is missing.

Return type:

AnnDataManager | None

Tangram.get_from_registry(adata, registry_key)

Returns the object in AnnData associated with the key in the data registry.

AnnData object should be registered with the model prior to calling this function via the self._validate_anndata method.

Parameters:

• registry_key (str) – key of object to get from data registry.

• adata (AnnData | MuData) – AnnData to pull data from.

Return type:

ndarray

Returns:

The requested data as a NumPy array.

Tangram.get_mapper_matrix()

Return the mapping matrix.

Return type:

ndarray

Returns:

Mapping matrix of shape (n_obs_sp, n_obs_sc)

Tangram.get_normalized_expression()

classmethod Tangram.load(dir_path, adata=None, accelerator='auto', device='auto', prefix=None, backup_url=None)

Instantiate a model from the saved output.

Parameters:

• dir_path (str) – Path to saved outputs.

• adata (AnnData | MuData | None (default: None)) – AnnData organized in the same way as data used to train model. It is not necessary to run setup_anndata, as AnnData is validated against the saved scvi setup dictionary. If None, will check for and load anndata saved with the model.

• accelerator (str (default: 'auto')) – Supports passing different accelerator types (“cpu”, “gpu”, “tpu”, “ipu”, “hpu”, “mps, “auto”) as well as custom accelerator instances.

• device (int | str (default: 'auto')) – The device to use. Can be set to a non-negative index (int or str) or “auto” for automatic selection based on the chosen accelerator. If set to “auto” and accelerator is not determined to be “cpu”, then device will be set to the first available device.

• prefix (str | None (default: None)) – Prefix of saved file names.

• backup_url (str | None (default: None)) – URL to retrieve saved outputs from if not present on disk.

Returns:

Model with loaded state dictionaries.

Examples

>>> model = ModelClass.load(save_path, adata)
>>> model.get_....

static Tangram.load_registry(dir_path, prefix=None)

Return the full registry saved with the model.

Parameters:

• dir_path (str) – Path to saved outputs.

• prefix (str | None (default: None)) – Prefix of saved file names.

Return type:

dict

Returns:

The full registry saved with the model

static Tangram.project_cell_annotations(adata_sc, adata_sp, mapper, labels)

Project cell annotations to spatial data.

Parameters:

• adata_sc (AnnData) – AnnData object with single-cell data.

• adata_sp (AnnData) – AnnData object with spatial data.

• mapper (ndarray) – Mapping from single-cell to spatial data.

• labels (Series) – Cell annotations to project.

Return type:

DataFrame

Returns:

Projected annotations as a pd.DataFrame with shape (n_sp, n_labels).

static Tangram.project_genes(adata_sc, adata_sp, mapper)

Project gene expression to spatial data.

Parameters:

• adata_sc (AnnData) – AnnData object with single-cell data.

• adata_sp (AnnData) – AnnData object with spatial data.

• mapper (ndarray) – Mapping from single-cell to spatial data.

Return type:

AnnData

Returns:

anndata.AnnData object with projected gene expression.

classmethod Tangram.register_manager(adata_manager)

Registers an AnnDataManager instance with this model class.

Stores the AnnDataManager reference in a class-specific manager store. Intended for use in the setup_anndata() class method followed up by retrieval of the AnnDataManager via the _get_most_recent_anndata_manager() method in the model init method.

Notes

Subsequent calls to this method with an AnnDataManager instance referring to the same underlying AnnData object will overwrite the reference to previous AnnDataManager.

Tangram.save(dir_path, prefix=None, overwrite=False, save_anndata=False, save_kwargs=None, legacy_mudata_format=False, **anndata_write_kwargs)

Save the state of the model.

Neither the trainer optimizer state nor the trainer history are saved. Model files are not expected to be reproducibly saved and loaded across versions until we reach version 1.0.

Parameters:

• dir_path (str) – Path to a directory.

• prefix (str | None (default: None)) – Prefix to prepend to saved file names.

• overwrite (bool (default: False)) – Overwrite existing data or not. If False and directory already exists at dir_path, error will be raised.

• save_anndata (bool (default: False)) – If True, also saves the anndata

• save_kwargs (dict | None (default: None)) – Keyword arguments passed into save().

• legacy_mudata_format (bool (default: False)) – If True, saves the model var_names in the legacy format if the model was trained with a MuData object. The legacy format is a flat array with variable names across all modalities concatenated, while the new format is a dictionary with keys corresponding to the modality names and values corresponding to the variable names for each modality.

• anndata_write_kwargs – Kwargs for write()

classmethod Tangram.setup_anndata()

Not implemented, use setup_mudata.

classmethod Tangram.setup_mudata(mdata, density_prior_key='rna_count_based', sc_layer=None, sp_layer=None, modalities=None, **kwargs)

Sets up the AnnData object for this model.

A mapping will be created between data fields used by this model to their respective locations in adata. None of the data in adata are modified. Only adds fields to adata.

Parameters:

• mdata (MuData) – MuData with scRNA and spatial modalities.

• sc_layer (str | None (default: None)) – Layer key in scRNA modality to use for training.

• sp_layer (str | None (default: None)) – Layer key in spatial modality to use for training.

• density_prior_key (Union[str, Literal['rna_count_based', 'uniform'], None] (default: 'rna_count_based')) – Key in spatial modality obs for density prior.

• modalities (dict[str, str] | None (default: None)) – Mapping from setup_mudata param name to modality in mdata.

Tangram.to_device(device)

Move model to device.

Parameters:

device (str | int) – Device to move model to. Options: ‘cpu’ for CPU, integer GPU index (eg. 0), or ‘cuda:X’ where X is the GPU index (eg. ‘cuda:0’). See torch.device for more info.

Examples

>>> adata = scvi.data.synthetic_iid()
>>> model = scvi.model.SCVI(adata)
>>> model.to_device("cpu")  # moves model to CPU
>>> model.to_device("cuda:0")  # moves model to GPU 0
>>> model.to_device(0)  # also moves model to GPU 0

Tangram.train(max_epochs=1000, accelerator='auto', devices='auto', lr=0.1, plan_kwargs=None)

Train the model.

Parameters:

• max_epochs (int (default: 1000)) – Number of passes through the dataset.

• accelerator (str (default: 'auto')) – Supports passing different accelerator types (“cpu”, “gpu”, “tpu”, “ipu”, “hpu”, “mps, “auto”) as well as custom accelerator instances.

• devices (int | list[int] | str (default: 'auto')) – The devices to use. Can be set to a non-negative index (int or str), a sequence of device indices (list or comma-separated str), the value -1 to indicate all available devices, or “auto” for automatic selection based on the chosen accelerator. If set to “auto” and accelerator is not determined to be “cpu”, then devices will be set to the first available device.

• lr (float (default: 0.1)) – Optimiser learning rate (default optimiser is ClippedAdam). Specifying optimiser via plan_kwargs overrides this choice of lr.

• plan_kwargs (dict | None (default: None)) – Keyword args for JaxTrainingPlan. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.

Tangram.view_anndata_setup(adata=None, hide_state_registries=False)

Print summary of the setup for the initial AnnData or a given AnnData object.

Parameters:

• adata (AnnData | MuData | None (default: None)) – AnnData object setup with setup_anndata or transfer_fields().

• hide_state_registries (bool (default: False)) – If True, prints a shortened summary without details of each state registry.

Return type:

None

static Tangram.view_setup_args(dir_path, prefix=None)

Print args used to setup a saved model.

Parameters:

• dir_path (str) – Path to saved outputs.

• prefix (str | None (default: None)) – Prefix of saved file names.

Return type:

None