scviva.external.Tangram

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

Torch 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) – Whether to use the constrained version of Tangram instead of cells mode.

• target_count (int | 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. /tutorials/notebooks/spatial/tangram_scvi_tools

__init__(sc_adata, constrained=False, target_count=None, **model_kwargs)

Methods

__init__(sc_adata[, constrained, target_count])
convert_legacy_save(dir_path, output_dir_path)	Converts a legacy saved model (<v0.15.0) to the updated save format.
data_registry(registry_key)	Returns the object in AnnData associated with the key in the data registry.
deregister_manager([adata])	Deregisters the AnnDataManager instance associated with adata.
differential_abundance(*args, **kwargs)	Not implemented for this model class.
from_spatialdata(sdata[, table_key, region])	Convenience constructor from a SpatialData object.
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_latent_representation([adata, indices, ...])	Return latent representation with optional RAPIDS acceleration.
get_mapper_matrix()	Return the mapping matrix.
get_normalized_expression(*args, **kwargs)	Not implemented for this model class.
get_setup_arg(setup_arg)	Returns the string provided to setup of a specific setup_arg.
get_state_registry(registry_key)	Returns the state registry for the AnnDataField registered with this instance.
get_var_names([legacy_mudata_format])	Variable names of input data.
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.
plot_spatial_embedding([adata, basis, color])	Plot latent embedding overlaid on tissue spatial coordinates.
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.
setup_spatialdata(sdata[, table_key, region])	Register fields from a SpatialData object.
to_device(device)	Move the model to the device.
train([max_epochs, accelerator, devices, ...])	Train the model.
transfer_fields(adata, **kwargs)	Transfer fields from a model to an AnnData object.
update_setup_method_args(setup_method_args)	Update setup method args.
view_anndata_setup([adata, ...])	Print summary of the setup for the initial AnnData or a given AnnData object.
view_registry([hide_state_registries])	Prints summary of the registry.
view_setup_args(dir_path[, prefix])	Print args used to setup a saved model.
view_setup_method_args()	Prints setup kwargs used to produce a given registry.

Attributes

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.
registry	Data attached to model instance.
run_id	Returns the run id of the model.
run_name	Returns the run name of the model.
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.