pertpy.tools.SCGEN

class pertpy.tools.SCGEN(adata, n_hidden=800, n_latent=100, n_layers=2, dropout_rate=0.2, **model_kwargs)[source]

Jax Implementation of scGen model for batch removal and perturbation prediction.

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.

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

batch_removal([adata])

Removes batch effects.

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 specific to this model instance.

get_decoded_expression([adata, indices, ...])

Get decoded expression.

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 the latent representation for each cell.

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.

predict([ctrl_key, stim_key, ...])

Predicts the cell type provided by the user in stimulated condition.

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(adata[, batch_key, labels_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.

plot_binary_classifier(scgen, adata, delta, ...)

Plots the dot product between delta and latent representation of a linear classifier.

plot_reg_mean_plot(adata, condition_key, ...)

Plots mean matching for a set of specified genes.

plot_reg_var_plot(adata, condition_key, ...)

Plots variance matching for a set of specified genes.

Attributes

adata

SCGEN.adata

Data attached to model instance.

adata_manager

SCGEN.adata_manager

Manager instance associated with self.adata.

device

SCGEN.device

history

SCGEN.history

Returns computed metrics during training.

is_trained

SCGEN.is_trained

Whether the model has been trained.

summary_string

SCGEN.summary_string

Summary string of the model.

test_indices

SCGEN.test_indices

Observations that are in test set.

train_indices

SCGEN.train_indices

Observations that are in train set.

validation_indices

SCGEN.validation_indices

Observations that are in validation set.

Methods

batch_removal

SCGEN.batch_removal(adata=None)[source]

Removes batch effects.

Parameters:

adata (AnnData | None) – AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model. Must have been setup with batch_key and labels_key, corresponding to batch and cell type metadata, respectively.

Returns:

~anndata.AnnData AnnData of corrected gene expression in adata.X and corrected latent space in adata.obsm[“latent”]. A reference to the original AnnData is in corrected.raw if the input adata had no raw attribute.

Return type:

corrected

Examples

>>> import pertpy as pt
>>> data = pt.dt.kang_2018()
>>> pt.tl.SCGEN.setup_anndata(data, batch_key="label", labels_key="cell_type")
>>> model = pt.tl.SCGEN(data)
>>> model.train(max_epochs=10, batch_size=64, early_stopping=True, early_stopping_patience=5)
>>> corrected_adata = model.batch_removal()

convert_legacy_save

classmethod SCGEN.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) – Overwrite existing data or not. If False and directory already exists at output_dir_path, error will be raised.

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

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

Return type:

None

deregister_manager

SCGEN.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.

get_anndata_manager

SCGEN.get_anndata_manager(adata, required=False)

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 (Union[AnnData, MuData]) – AnnData object to find manager instance for.

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

Return type:

AnnDataManager | None

get_decoded_expression

SCGEN.get_decoded_expression(adata=None, indices=None, batch_size=None)[source]

Get decoded expression.

Parameters:

  • adata (AnnData | None) – AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

  • indices (Sequence[int] | None) – Indices of cells in adata to use. If None, all cells are used.

  • batch_size (int | None) – Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.

Return type:

Array

Returns:

Decoded expression for each cell

Examples

>>> import pertpy as pt
>>> data = pt.dt.kang_2018()
>>> pt.tl.SCGEN.setup_anndata(data, batch_key="label", labels_key="cell_type")
>>> model = pt.tl.SCGEN(data)
>>> model.train(max_epochs=10, batch_size=64, early_stopping=True, early_stopping_patience=5)
>>> decoded_X = model.get_decoded_expression()

get_from_registry

SCGEN.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 (Union[AnnData, MuData]) – AnnData to pull data from.

Return type:

The requested data as a NumPy array.

get_latent_representation

SCGEN.get_latent_representation(adata=None, indices=None, give_mean=True, n_samples=1, batch_size=None)[source]

Return the latent representation for each cell.

Parameters:

  • adata (AnnData | None) – AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model.

  • indices (Sequence[int] | None) – Indices of cells in adata to use. If None, all cells are used.

  • batch_size (int | None) – Minibatch size for data loading into model. Defaults to scvi.settings.batch_size.

Return type:

ndarray

Returns:

Low-dimensional representation for each cell

Examples

>>> import pertpy as pt
>>> data = pt.dt.kang_2018()
>>> pt.tl.SCGEN.setup_anndata(data, batch_key="label", labels_key="cell_type")
>>> model = pt.tl.SCGEN(data)
>>> model.train(max_epochs=10, batch_size=64, early_stopping=True, early_stopping_patience=5)
>>> latent_X = model.get_latent_representation()

load

classmethod SCGEN.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 (Union[AnnData, MuData, 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) – Supports passing different accelerator types (“cpu”, “gpu”, “tpu”, “ipu”, “hpu”, “mps, “auto”) as well as custom accelerator instances.

  • device (int | str) – 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) – Prefix of saved file names.

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

Return type:

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 SCGEN.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) – Prefix of saved file names.

Return type:

The full registry saved with the model

plot_binary_classifier

SCGEN.plot_binary_classifier(scgen, adata, delta, ctrl_key, stim_key, show=False, save=None, fontsize=14)[source]

Plots the dot product between delta and latent representation of a linear classifier.

Builds a linear classifier based on the dot product between the difference vector and the latent representation of each cell and plots the dot product results between delta and latent representation.

Parameters:

  • scgen (SCGEN) – ScGen object that was trained.

  • adata (AnnData | None) – AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model. Must have been setup with batch_key and labels_key, corresponding to batch and cell type metadata, respectively.

  • delta (ndarray) – Difference between stimulated and control cells in latent space

  • ctrl_key (str) – Key for control part of the data found in condition_key.

  • stim_key (str) – Key for stimulated part of the data found in condition_key.

  • path_to_save – Path to save the plot.

  • save (str | bool | None) – Specify if the plot should be saved or not.

  • fontsize (float) – Set the font size of the plot.

Return type:

Axes | None

plot_reg_mean_plot

SCGEN.plot_reg_mean_plot(adata, condition_key, axis_keys, labels, save=None, gene_list=None, show=False, top_100_genes=None, verbose=False, legend=True, title=None, x_coeff=0.3, y_coeff=0.8, fontsize=14, **kwargs)[source]

Plots mean matching for a set of specified genes.

Parameters:

  • adata – AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model. Must have been setup with batch_key and labels_key, corresponding to batch and cell type metadata, respectively.

  • condition_key (str) – The key for the condition

  • axis_keys (dict[str, str]) – Dictionary of adata.obs keys that are used by the axes of the plot. Has to be in the following form: {“x”: “Key for x-axis”, “y”: “Key for y-axis”}.

  • labels (dict[str, str]) – Dictionary of axes labels of the form {“x”: “x-axis-name”, “y”: “y-axis name”}.

  • path_to_save – path to save the plot.

  • save (str | bool | None) – Specify if the plot should be saved or not.

  • gene_list (list[str]) – list of gene names to be plotted.

  • show (bool) – if True: will show to the plot after saving it.

  • top_100_genes (list[str]) – List of the top 100 differentially expressed genes. Specify if you want the top 100 DEGs to be assessed extra.

  • verbose (bool) – Specify if you want information to be printed while creating the plot, defaults to False.

  • legend (bool) – if True: plots a legend, defaults to True.

  • title (str) – Set if you want the plot to display a title.

  • x_coeff (float) – Offset to print the R^2 value in x-direction, defaults to 0.3.

  • y_coeff (float) – Offset to print the R^2 value in y-direction, defaults to 0.8.

  • fontsize (float) – Fontsize used for text in the plot, defaults to 14.

  • **kwargs

Return type:

tuple[float, float] | float

Examples

>>> import pertpy as pt
>>> data = pt.dt.kang_2018()
>>> pt.tl.SCGEN.setup_anndata(data, batch_key="label", labels_key="cell_type")
>>> scg = pt.tl.SCGEN(data)
>>> scg.train(max_epochs=10, batch_size=64, early_stopping=True, early_stopping_patience=5)
>>> pred, delta = scg.predict(ctrl_key='ctrl', stim_key='stim', celltype_to_predict='CD4 T cells')
>>> pred.obs['label'] = 'pred'
>>> eval_adata = data[data.obs['cell_type'] == 'CD4 T cells'].copy().concatenate(pred)
>>> r2_value = scg.plot_reg_mean_plot(eval_adata, condition_key='label', axis_keys={"x": "pred", "y": "stim"},                 labels={"x": "predicted", "y": "ground truth"}, save=False, show=True)
Preview:
../../_images/scgen_reg_mean.png

plot_reg_var_plot

SCGEN.plot_reg_var_plot(adata, condition_key, axis_keys, labels, save=None, gene_list=None, top_100_genes=None, show=False, legend=True, title=None, verbose=False, x_coeff=0.3, y_coeff=0.8, fontsize=14, **kwargs)[source]

Plots variance matching for a set of specified genes.

Parameters:

  • adata – AnnData object with equivalent structure to initial AnnData. If None, defaults to the AnnData object used to initialize the model. Must have been setup with batch_key and labels_key, corresponding to batch and cell type metadata, respectively.

  • condition_key (str) – Key of the condition.

  • axis_keys (dict[str, str]) – Dictionary of adata.obs keys that are used by the axes of the plot. Has to be in the following form: {“x”: “Key for x-axis”, “y”: “Key for y-axis”}.

  • labels (dict[str, str]) – Dictionary of axes labels of the form {“x”: “x-axis-name”, “y”: “y-axis name”}.

  • path_to_save – path to save the plot.

  • save (str | bool | None) – Specify if the plot should be saved or not.

  • gene_list (list[str]) – list of gene names to be plotted.

  • show (bool) – if True: will show to the plot after saving it.

  • top_100_genes (list[str]) – List of the top 100 differentially expressed genes. Specify if you want the top 100 DEGs to be assessed extra.

  • legend (bool) – if True: plots a legend, defaults to True.

  • title (str) – Set if you want the plot to display a title.

  • verbose (bool) – Specify if you want information to be printed while creating the plot, defaults to False.

  • x_coeff (float) – Offset to print the R^2 value in x-direction, defaults to 0.3.

  • y_coeff (float) – Offset to print the R^2 value in y-direction, defaults to 0.8.

  • fontsize (float) – Fontsize used for text in the plot, defaults to 14.

Return type:

tuple[float, float] | float

predict

SCGEN.predict(ctrl_key=None, stim_key=None, adata_to_predict=None, celltype_to_predict=None, restrict_arithmetic_to='all')[source]

Predicts the cell type provided by the user in stimulated condition.

Parameters:

  • ctrl_key – Key for control part of the data found in condition_key.

  • stim_key – Key for stimulated part of the data found in condition_key.

  • adata_to_predict – Adata for unperturbed cells you want to be predicted.

  • celltype_to_predict – The cell type you want to be predicted.

  • restrict_arithmetic_to – Dictionary of celltypes you want to be observed for prediction.

Return type:

tuple[AnnData, Any]

Returns:

np nd-array of predicted cells in primary space.

delta: float

Difference between stimulated and control cells in latent space

Examples

>>> import pertpy as pt
>>> data = pt.dt.kang_2018()
>>> pt.tl.SCGEN.setup_anndata(data, batch_key="label", labels_key="cell_type")
>>> model = pt.tl.SCGEN(data)
>>> model.train(max_epochs=10, batch_size=64, early_stopping=True, early_stopping_patience=5)
>>> pred, delta = model.predict(ctrl_key="ctrl", stim_key="stim", celltype_to_predict="CD4 T cells")

register_manager

classmethod SCGEN.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.

save

SCGEN.save(dir_path, prefix=None, overwrite=False, save_anndata=False, save_kwargs=None, **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) – Prefix to prepend to saved file names.

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

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

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

  • anndata_write_kwargs – Kwargs for write()

setup_anndata

classmethod SCGEN.setup_anndata(adata, batch_key=None, labels_key=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.

scGen expects the expression data to come from adata.X

batch_key

key in adata.obs for batch information. Categories will automatically be converted into integer categories and saved to adata.obs[‘_scvi_batch’]. If None, assigns the same batch to all the data.

labels_key

key in adata.obs for label information. Categories will automatically be converted into integer categories and saved to adata.obs[‘_scvi_labels’]. If None, assigns the same label to all the data.

Examples

>>> import pertpy as pt
>>> data = pt.dt.kang_2018()
>>> pt.tl.SCGEN.setup_anndata(data, batch_key="label", labels_key="cell_type")

to_device

SCGEN.to_device(device)[source]

Move model to device.

Parameters:

device – 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

SCGEN.train(max_epochs=None, accelerator='auto', devices='auto', train_size=0.9, validation_size=None, shuffle_set_split=True, batch_size=128, datasplitter_kwargs=None, plan_kwargs=None, **trainer_kwargs)

Train the model.

Parameters:

  • max_epochs (int | None) – Number of passes through the dataset. If None, defaults to np.min([round((20000 / n_cells) * 400), 400])

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

  • devices (int | list[int] | str) – 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.

  • train_size (float) – Size of training set in the range [0.0, 1.0].

  • validation_size (float | 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.

  • shuffle_set_split (bool) – Whether to shuffle indices before splitting. If False, the val, train, and test set are split in the sequential order of the data according to validation_size and train_size percentages.

  • batch_size (int) – Minibatch size to use during training.

  • lr – Learning rate to use during training.

  • datasplitter_kwargs (dict | None) – Additional keyword arguments passed into DataSplitter.

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

  • **trainer_kwargs – Other keyword args for Trainer.

view_anndata_setup

SCGEN.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 (Union[AnnData, MuData, None]) – AnnData object setup with setup_anndata or transfer_fields().

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

Return type:

None

view_setup_args

static SCGEN.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) – Prefix of saved file names.

Return type:

None