scvi.external.Tangram#
- class scvi.external.Tangram(sc_adata, constrained=False, target_count=None, **model_kwargs)[source]#
Reimplementation of Tangram [Biancalani et al., 2021] for mapping single-cell RNA-seq data to spatial data.
Currently the “cells” and “constrained” modes are implemented.
Original code: https://github.com/broadinstitute/Tangram.
- 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 (Optional[int]) – The number of cells to be filtered. Necessary when
constrained
is True.**model_kwargs – Keyword args for
TangramMapper
sc_adata (AnnData) –
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:
Attributes table#
Data attached to model instance. |
|
Manager instance associated with self.adata. |
|
The current device that the module's params are on. |
|
Returns computed metrics during training. |
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Whether the model has been trained. |
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Observations that are in test set. |
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Observations that are in train set. |
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Observations that are in validation set. |
Methods table#
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Converts a legacy saved model (<v0.15.0) to the updated save format. |
|
Retrieves the |
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Returns the object in AnnData associated with the key in the data registry. |
Return the mapping matrix. |
|
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Instantiate a model from the saved output. |
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Return the full registry saved with the model. |
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Project cell annotations to spatial data. |
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Project gene expression to spatial data. |
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Registers an |
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Save the state of the model. |
Not implemented, use |
|
|
Sets up the |
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Move model to device. |
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Train the model. |
|
Print summary of the setup for the initial AnnData or a given AnnData object. |
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Print args used to setup a saved model. |
Attributes#
adata
adata_manager
device
history
is_trained
test_indices
train_indices
validation_indices
Methods#
convert_legacy_save
- classmethod Tangram.convert_legacy_save(dir_path, output_dir_path, overwrite=False, prefix=None)[source]#
Converts a legacy saved model (<v0.15.0) to the updated save format.
get_anndata_manager
- Tangram.get_anndata_manager(adata, required=False)[source]#
Retrieves the
AnnDataManager
for a given AnnData object specific to this model instance.Requires
self.id
has been set. Checks for anAnnDataManager
specific to this model instance.
get_from_registry
- Tangram.get_from_registry(adata, registry_key)[source]#
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.
get_mapper_matrix
- Tangram.get_mapper_matrix()[source]#
Return the mapping matrix.
- Returns:
Mapping matrix of shape (n_obs_sp, n_obs_sc)
- Return type:
load
- classmethod Tangram.load(dir_path, adata=None, use_gpu=None, prefix=None, backup_url=None)[source]#
Instantiate a model from the saved output.
- Parameters:
dir_path (str) – Path to saved outputs.
adata (Optional[Union[AnnData, MuData]]) – 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.use_gpu (Optional[Union[str, int, bool]]) – Load model on default GPU if available (if None or True), or index of GPU to use (if int), or name of GPU (if str), or use CPU (if False).
backup_url (Optional[str]) – URL to retrieve saved outputs from if not present on disk.
- Returns:
Model with loaded state dictionaries.
Examples
>>> model = ModelClass.load(save_path, adata) # use the name of the model class used to save >>> model.get_....
load_registry
- static Tangram.load_registry(dir_path, prefix=None)[source]#
Return the full registry saved with the model.
project_cell_annotations
- static Tangram.project_cell_annotations(adata_sc, adata_sp, mapper, labels)[source]#
Project cell annotations to spatial data.
- Parameters:
- Returns:
Projected annotations as a
pd.DataFrame
with shape (n_sp, n_labels).- Return type:
project_genes
- static Tangram.project_genes(adata_sc, adata_sp, mapper)[source]#
Project gene expression to spatial data.
- Parameters:
- Returns:
anndata.AnnData
object with projected gene expression.- Return type:
register_manager
- classmethod Tangram.register_manager(adata_manager)[source]#
Registers an
AnnDataManager
instance with this model class.Stores the
AnnDataManager
reference in a class-specific manager store. Intended for use in thesetup_anndata()
class method followed up by retrieval of theAnnDataManager
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 previousAnnDataManager
.- Parameters:
adata_manager (AnnDataManager) –
save
- Tangram.save(dir_path, prefix=None, overwrite=False, save_anndata=False, **anndata_write_kwargs)[source]#
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 (Optional[str]) – Prefix to prepend to saved file names.
overwrite (bool) – Overwrite existing data or not. If
False
and directory already exists atdir_path
, error will be raised.save_anndata (bool) – If True, also saves the anndata
anndata_write_kwargs – Kwargs for
write()
setup_anndata
setup_mudata
- classmethod Tangram.setup_mudata(mdata, density_prior_key='rna_count_based', sc_layer=None, sp_layer=None, modalities=None, **kwargs)[source]#
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 (Optional[str]) – Layer key in scRNA modality to use for training.
sp_layer (Optional[str]) – Layer key in spatial modality to use for training.
density_prior_key (Optional[Union[str, Literal['rna_count_based', 'uniform']]]) – Key in spatial modality obs for density prior.
modalities (Optional[Dict[str, str]]) – Mapping from
setup_mudata
param name to modality in mdata.
to_device
- Tangram.to_device(device)[source]#
Move model to device.
- Parameters:
device (Union[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
train
- Tangram.train(max_epochs=1000, use_gpu=None, lr=0.1, plan_kwargs=None)[source]#
Train the model.
- Parameters:
max_epochs (int) – Number of passes through the dataset.
use_gpu (Optional[Union[str, int, bool]]) – Use default GPU if available (if None or True), or index of GPU to use (if int), or name of GPU (if str, e.g.,
'cuda:0'
), or use CPU (if False).lr (float) – Optimiser learning rate (default optimiser is
ClippedAdam
). Specifying optimiser via plan_kwargs overrides this choice of lr.plan_kwargs (Optional[dict]) – Keyword args for
JaxTrainingPlan
. Keyword arguments passed totrain()
will overwrite values present inplan_kwargs
, when appropriate.
view_anndata_setup
- Tangram.view_anndata_setup(adata=None, hide_state_registries=False)[source]#
Print summary of the setup for the initial AnnData or a given AnnData object.
view_setup_args