scvi.model.DestVI#

class scvi.model.DestVI(st_adata, cell_type_mapping, decoder_state_dict, px_decoder_state_dict, px_r, n_hidden, n_latent, n_layers, dropout_decoder, l1_reg, **module_kwargs)[source]#

Multi-resolution deconvolution of Spatial Transcriptomics data (DestVI) [Lopez21].. Most users will use the alternate constructor (see example).

Parameters:
st_adata : AnnData

spatial transcriptomics AnnData object that has been registered via setup_anndata().

cell_type_mapping : ndarray

mapping between numerals and cell type labels

decoder_state_dict : OrderedDict

state_dict from the decoder of the CondSCVI model

px_decoder_state_dict : OrderedDict

state_dict from the px_decoder of the CondSCVI model

px_r : ndarray

parameters for the px_r tensor in the CondSCVI model

n_hidden : int

Number of nodes per hidden layer.

n_latent : int

Dimensionality of the latent space.

n_layers : int

Number of hidden layers used for encoder and decoder NNs.

**module_kwargs

Keyword args for MRDeconv

Examples

>>> sc_adata = anndata.read_h5ad(path_to_scRNA_anndata)
>>> scvi.model.CondSCVI.setup_anndata(sc_adata)
>>> sc_model = scvi.model.CondSCVI(sc_adata)
>>> st_adata = anndata.read_h5ad(path_to_ST_anndata)
>>> DestVI.setup_anndata(st_adata)
>>> spatial_model = DestVI.from_rna_model(st_adata, sc_model)
>>> spatial_model.train(max_epochs=2000)
>>> st_adata.obsm["proportions"] = spatial_model.get_proportions(st_adata)
>>> gamma = spatial_model.get_gamma(st_adata)

Notes

See further usage examples in the following tutorials:

  1. Multi-resolution deconvolution of spatial transcriptomics

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.

history

Returns computed metrics during training.

is_trained

Whether the model has been trained.

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.

from_rna_model(st_adata, sc_model[, ...])

Alternate constructor for exploiting a pre-trained model on a RNA-seq dataset.

get_anndata_manager(adata[, required])

Retrieves the AnnDataManager for a given AnnData object specific to this model instance.

get_from_registry(adata, registry_key)

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

get_gamma([indices, batch_size, return_numpy])

Returns the estimated cell-type specific latent space for the spatial data.

get_proportions([keep_noise, indices, ...])

Returns the estimated cell type proportion for the spatial data.

get_scale_for_ct(label[, indices, batch_size])

Return the scaled parameter of the NB for every spot in queried cell types.

load(dir_path[, adata, use_gpu, prefix, ...])

Instantiate a model from the saved output.

load_registry(dir_path[, prefix])

Return the full registry saved with the model.

register_manager(adata_manager)

Registers an AnnDataManager instance with this model class.

save(dir_path[, prefix, overwrite, save_anndata])

Save the state of the model.

setup_anndata(adata[, layer])

Sets up the AnnData object for this model.

to_device(device)

Move model to device.

train([max_epochs, lr, use_gpu, train_size, ...])

Trains the model using MAP inference.

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#

adata#

DestVI.adata[source]#

Data attached to model instance.

Return type:

AnnData | MuDataUnion[AnnData, MuData]

adata_manager#

DestVI.adata_manager[source]#

Manager instance associated with self.adata.

Return type:

AnnDataManager

device#

DestVI.device[source]#

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

Return type:

str

history#

DestVI.history[source]#

Returns computed metrics during training.

is_trained#

DestVI.is_trained[source]#

Whether the model has been trained.

Return type:

bool

test_indices#

DestVI.test_indices[source]#

Observations that are in test set.

Return type:

ndarray

train_indices#

DestVI.train_indices[source]#

Observations that are in train set.

Return type:

ndarray

validation_indices#

DestVI.validation_indices[source]#

Observations that are in validation set.

Return type:

ndarray

Methods#

convert_legacy_save#

classmethod DestVI.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.

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 | NoneOptional[str] (default: None)

Prefix of saved file names.

Return type:

None

from_rna_model#

classmethod DestVI.from_rna_model(st_adata, sc_model, vamp_prior_p=15, l1_reg=0.0, **module_kwargs)[source]#

Alternate constructor for exploiting a pre-trained model on a RNA-seq dataset.

Parameters:
st_adata : AnnData

registered anndata object

sc_model : CondSCVI

trained CondSCVI model

vamp_prior_p : int (default: 15)

number of mixture parameter for VampPrior calculations

l1_reg : float (default: 0.0)

Scalar parameter indicating the strength of L1 regularization on cell type proportions. A value of 50 leads to sparser results.

**model_kwargs

Keyword args for DestVI

get_anndata_manager#

DestVI.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 an AnnDataManager specific to this model instance.

Parameters:
adata : AnnData | MuDataUnion[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 | NoneOptional[AnnDataManager]

get_from_registry#

DestVI.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.

Parameters:
registry_key : str

key of object to get from data registry.

adata : AnnData | MuDataUnion[AnnData, MuData]

AnnData to pull data from.

Return type:

ndarray

Returns:

The requested data as a NumPy array.

get_gamma#

DestVI.get_gamma(indices=None, batch_size=None, return_numpy=False)[source]#

Returns the estimated cell-type specific latent space for the spatial data.

Parameters:
indices : Sequence[int] | NoneOptional[Sequence[int]] (default: None)

Indices of cells in adata to use. Only used if amortization. If None, all cells are used.

batch_size : int | NoneOptional[int] (default: None)

Minibatch size for data loading into model. Only used if amortization. Defaults to scvi.settings.batch_size.

return_numpy : bool (default: False)

if activated, will return a numpy array of shape is n_spots x n_latent x n_labels.

Return type:

ndarray | {str: DataFrame}Union[ndarray, Dict[str, DataFrame]]

get_proportions#

DestVI.get_proportions(keep_noise=False, indices=None, batch_size=None)[source]#

Returns the estimated cell type proportion for the spatial data.

Shape is n_cells x n_labels OR n_cells x (n_labels + 1) if keep_noise.

Parameters:
keep_noise : bool (default: False)

whether to account for the noise term as a standalone cell type in the proportion estimate.

indices : Sequence[int] | NoneOptional[Sequence[int]] (default: None)

Indices of cells in adata to use. Only used if amortization. If None, all cells are used.

batch_size : int | NoneOptional[int] (default: None)

Minibatch size for data loading into model. Only used if amortization. Defaults to scvi.settings.batch_size.

Return type:

DataFrame

get_scale_for_ct#

DestVI.get_scale_for_ct(label, indices=None, batch_size=None)[source]#

Return the scaled parameter of the NB for every spot in queried cell types.

Parameters:
label : str

cell type of interest

indices : Sequence[int] | NoneOptional[Sequence[int]] (default: None)

Indices of cells in self.adata to use. If None, all cells are used.

batch_size : int | NoneOptional[int] (default: None)

Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.

Return type:

DataFrame

Returns:

Pandas dataframe of gene_expression

load#

classmethod DestVI.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 : AnnData | MuData | NoneUnion[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.

use_gpu : str | int | bool | NoneUnion[str, int, bool, None] (default: None)

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).

prefix : str | NoneOptional[str] (default: None)

Prefix of saved file names.

backup_url : str | NoneOptional[str] (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) # use the name of the model class used to save
>>> model.get_....

load_registry#

static DestVI.load_registry(dir_path, prefix=None)[source]#

Return the full registry saved with the model.

Parameters:
dir_path : str

Path to saved outputs.

prefix : str | NoneOptional[str] (default: None)

Prefix of saved file names.

Return type:

dict

Returns:

The full registry saved with the model

register_manager#

classmethod DestVI.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 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.

save#

DestVI.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 : str | NoneOptional[str] (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

anndata_write_kwargs

Kwargs for write()

setup_anndata#

classmethod DestVI.setup_anndata(adata, layer=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:
layer : str | NoneOptional[str] (default: None)

if not None, uses this as the key in adata.layers for raw count data.

to_device#

DestVI.to_device(device)[source]#

Move model to device.

Parameters:
device : str | intUnion[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#

DestVI.train(max_epochs=2000, lr=0.003, use_gpu=None, train_size=1.0, validation_size=None, batch_size=128, n_epochs_kl_warmup=200, plan_kwargs=None, **kwargs)[source]#

Trains the model using MAP inference.

Parameters:
max_epochs : int (default: 2000)

Number of epochs to train for

lr : float (default: 0.003)

Learning rate for optimization.

use_gpu : str | int | bool | NoneUnion[str, int, bool, None] (default: None)

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).

train_size : float (default: 1.0)

Size of training set in the range [0.0, 1.0].

validation_size : float | NoneOptional[float] (default: None)

Size of the test set. If None, defaults to 1 - train_size. If train_size + validation_size < 1, the remaining cells belong to a test set.

batch_size : int (default: 128)

Minibatch size to use during training.

n_epochs_kl_warmup : int (default: 200)

number of epochs needed to reach unit kl weight in the elbo

plan_kwargs : dict | NoneOptional[dict] (default: None)

Keyword args for TrainingPlan. Keyword arguments passed to train() will overwrite values present in plan_kwargs, when appropriate.

**kwargs

Other keyword args for Trainer.

view_anndata_setup#

DestVI.view_anndata_setup(adata=None, hide_state_registries=False)[source]#

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

Parameters:
adata : AnnData | MuData | NoneUnion[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

view_setup_args#

static DestVI.view_setup_args(dir_path, prefix=None)[source]#

Print args used to setup a saved model.

Parameters:
dir_path : str

Path to saved outputs.

prefix : str | NoneOptional[str] (default: None)

Prefix of saved file names.

Return type:

None