Pyro and scvi-tools infrastructure classes¶
Base mixin classes (AutoGuide setup, posterior quantile computation, plotting & export)¶
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cell2location.models.base._pyro_mixin.
init_to_value
(site=None, values={}, init_fn=<function init_to_mean>)[source]¶
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class
cell2location.models.base._pyro_mixin.
AutoGuideMixinModule
[source]¶ Bases:
object
This mixin class provides methods for:
initialising standard AutoNormal guides
initialising amortised guides (AutoNormalEncoder)
initialising amortised guides with special additional inputs
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class
cell2location.models.base._pyro_mixin.
QuantileMixin
[source]¶ Bases:
object
This mixin class provides methods for:
computing median and quantiles of the posterior distribution using both direct and amortised inference
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class
cell2location.models.base._pyro_mixin.
PltExportMixin
[source]¶ Bases:
object
This mixing class provides methods for common plotting tasks and data export.
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static
plot_posterior_mu_vs_data
(mu, data)[source]¶ Plot expected value of the model (e.g. mean of NB distribution) vs observed data
- Parameters
mu – expected value
data – data value
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plot_history
(iter_start=0, iter_end=- 1, ax=None)[source]¶ Plot training history :Parameters: * iter_start – omit initial iterations from the plot
iter_end – omit last iterations from the plot
ax – matplotlib axis
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sample2df_obs
(samples: dict, site_name: str = 'w_sf', summary_name: str = 'means', name_prefix: str = 'cell_abundance', factor_names_key: str = '')[source]¶ Export posterior distribution summary for observation-specific parameters (e.g. spatial cell abundance) as Pandas data frame (means, 5%/95% quantiles or sd of posterior distribution).
- Parameters
samples – dictionary with posterior mean, 5%/95% quantiles, SD, samples, generated by
.sample_posterior()
site_name – name of the model parameter to be exported
summary_name – posterior distribution summary to return [‘means’, ‘stds’, ‘q05’, ‘q95’]
name_prefix – prefix to add to column names (f’{summary_name}{name_prefix}_{site_name}_{self.factor_names_}’)
- Returns
- Return type
Pandas data frame corresponding to either means, 5%/95% quantiles or sd of the posterior distribution
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sample2df_vars
(samples: dict, site_name: str = 'gene_factors', summary_name: str = 'means', name_prefix: str = '', factor_names_key: str = '')[source]¶ Export posterior distribution summary for variable-specific parameters as Pandas data frame (means, 5%/95% quantiles or sd of posterior distribution).
- Parameters
samples – dictionary with posterior mean, 5%/95% quantiles, SD, samples, generated by
.sample_posterior()
site_name – name of the model parameter to be exported
summary_name – posterior distribution summary to return (‘means’, ‘stds’, ‘q05’, ‘q95’)
name_prefix – prefix to add to column names (f’{summary_name}{name_prefix}_{site_name}_{self.factor_names_}’)
- Returns
- Return type
Pandas data frame corresponding to either means, 5%/95% quantiles or sd of the posterior distribution
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plot_QC
(summary_name: str = 'means', use_n_obs: int = 1000)[source]¶ Show quality control plots:
Reconstruction accuracy to assess if there are any issues with model training. The plot should be roughly diagonal, strong deviations signal problems that need to be investigated. Plotting is slow because expected value of mRNA count needs to be computed from model parameters. Random observations are used to speed up computation.
- Parameters
summary_name – posterior distribution summary to use (‘means’, ‘stds’, ‘q05’, ‘q95’)
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static
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class
cell2location.models.base._pyro_mixin.
PyroAggressiveConvergence
(dataloader: scvi.dataloaders._ann_dataloader.AnnDataLoader = None, patience: int = 10, tolerance: float = 0.0001)[source]¶ Bases:
pytorch_lightning.callbacks.callback.Callback
A callback to compute/apply aggressive training convergence criteria for amortised inference. Motivated by this paper: https://arxiv.org/pdf/1901.05534.pdf
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class
cell2location.models.base._pyro_mixin.
PyroTrainingPlan
(pyro_module: scvi.module.base._base_module.PyroBaseModuleClass, loss_fn: Optional[pyro.infer.elbo.ELBO, None] = None, optim: Optional[pyro.optim.optim.PyroOptim, None] = None, optim_kwargs: Optional[dict, None] = None, n_steps_kl_warmup: Optional[int, None] = None, n_epochs_kl_warmup: Optional[int, None] = 400, scale_elbo: float = 1.0)[source]¶ Bases:
scvi.train._trainingplans.PyroTrainingPlan
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class
cell2location.models.base._pyro_mixin.
PyroAggressiveTrainingPlan1
(pyro_module: scvi.module.base._base_module.PyroBaseModuleClass, loss_fn: Optional[pyro.infer.elbo.ELBO, None] = None, optim: Optional[pyro.optim.optim.PyroOptim, None] = None, optim_kwargs: Optional[dict, None] = None, n_aggressive_epochs: int = 1000, n_aggressive_steps: int = 20, n_steps_kl_warmup: Optional[int, None] = None, n_epochs_kl_warmup: Optional[int, None] = 400, aggressive_vars: Optional[list, None] = None, invert_aggressive_selection: bool = False)[source]¶ Bases:
scvi.train._trainingplans.PyroTrainingPlan
Lightning module task to train Pyro scvi-tools modules. :Parameters: * pyro_module – An instance of
PyroBaseModuleClass
. This objectshould have callable model and guide attributes or methods.
loss_fn – A Pyro loss. Should be a subclass of
ELBO
. If None, defaults toTrace_ELBO
.optim – A Pyro optimizer instance, e.g.,
Adam
. If None, defaults topyro.optim.Adam
optimizer with a learning rate of 1e-3.optim_kwargs – Keyword arguments for default optimiser
pyro.optim.Adam
.n_aggressive_epochs – Number of epochs in aggressive optimisation of amortised variables.
n_aggressive_steps – Number of steps to spend optimising amortised variables before one step optimising global variables.
n_steps_kl_warmup – Number of training steps (minibatches) to scale weight on KL divergences from 0 to 1. Only activated when n_epochs_kl_warmup is set to None.
n_epochs_kl_warmup – Number of epochs to scale weight on KL divergences from 0 to 1. Overrides n_steps_kl_warmup when both are not None.
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class
cell2location.models.base._pyro_mixin.
PyroAggressiveTrainingPlan
(scale_elbo: Optional[float, None] = 1.0, **kwargs)[source]¶ Bases:
cell2location.models.base._pyro_mixin.PyroAggressiveTrainingPlan1
Lightning module task to train Pyro scvi-tools modules. :Parameters: * pyro_module – An instance of
PyroBaseModuleClass
. This objectshould have callable model and guide attributes or methods.
loss_fn – A Pyro loss. Should be a subclass of
ELBO
. If None, defaults toTrace_ELBO
.optim – A Pyro optimizer instance, e.g.,
Adam
. If None, defaults topyro.optim.Adam
optimizer with a learning rate of 1e-3.optim_kwargs – Keyword arguments for default optimiser
pyro.optim.Adam
.n_steps_kl_warmup – Number of training steps (minibatches) to scale weight on KL divergences from 0 to 1. Only activated when n_epochs_kl_warmup is set to None.
n_epochs_kl_warmup – Number of epochs to scale weight on KL divergences from 0 to 1. Overrides n_steps_kl_warmup when both are not None.
scvi-tools Module classes (initialising the model and the guide, PyroBaseModuleClass)¶
Cell2location spatial cell abundance estimation¶
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class
cell2location.models.base._pyro_base_loc_module.
Cell2locationBaseModule
(model, amortised: bool = False, encoder_mode: Literal[single, multiple, single - multiple] = 'single', encoder_kwargs: Optional[dict, None] = None, data_transform='log1p', create_autoguide_kwargs: Optional[dict, None] = None, **kwargs)[source]¶ Bases:
scvi.module.base._base_module.PyroBaseModuleClass
,cell2location.models.base._pyro_mixin.AutoGuideMixinModule
Module class which defines AutoGuide given model. Supports multiple model architectures.
- Parameters
amortised – boolean, use a Neural Network to approximate posterior distribution of location-specific (local) parameters?
encoder_mode – Use single encoder for all variables (“single”), one encoder per variable (“multiple”) or a single encoder in the first step and multiple encoders in the second step (“single-multiple”).
encoder_kwargs – arguments for Neural Network construction (scvi.nn.FCLayers)
kwargs – arguments for specific model class - e.g. number of genes, values of the prior distribution
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property
model
¶
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property
guide
¶
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property
list_obs_plate_vars
¶ Create a dictionary with:
“name” - the name of observation/minibatch plate;
“input” - indexes of model args to provide to encoder network when using amortised inference;
“sites” - dictionary with
keys - names of variables that belong to the observation plate (used to recognise and merge posterior samples for minibatch variables)
values - the dimensions in non-plate axis of each variable (used to construct output layer of encoder network when using amortised inference)
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property
is_amortised
¶
Reference signature estimation¶
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class
cell2location.models.reference._reference_model.
RegressionModel
(adata: anndata._core.anndata.AnnData, model_class=None, use_average_as_initial: bool = True, **model_kwargs)[source]¶ Bases:
cell2location.models.base._pyro_mixin.QuantileMixin
,scvi.model.base._pyromixin.PyroSampleMixin
,scvi.model.base._pyromixin.PyroSviTrainMixin
,cell2location.models.base._pyro_mixin.PltExportMixin
,scvi.model.base._base_model.BaseModelClass
Model which estimates per cluster average mRNA count account for batch effects. User-end model class.
https://github.com/BayraktarLab/cell2location
- Parameters
adata – single-cell AnnData object that has been registered via
setup_anndata()
.use_gpu – Use the GPU?
**model_kwargs – Keyword args for
LocationModelLinearDependentWMultiExperimentModel
Examples
TODO add example >>>
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classmethod
setup_anndata
(adata: anndata._core.anndata.AnnData, layer: Optional[str, None] = None, batch_key: Optional[str, None] = None, labels_key: Optional[str, None] = None, categorical_covariate_keys: Optional[List[str], None] = None, continuous_covariate_keys: Optional[List[str], None] = 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 – if not None, uses this as the key in adata.layers for raw count data.
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.
categorical_covariate_keys – keys in adata.obs that correspond to categorical data. These covariates can be added in addition to the batch covariate and are also treated as nuisance factors (i.e., the model tries to minimize their effects on the latent space). Thus, these should not be used for biologically-relevant factors that you do _not_ want to correct for.
continuous_covariate_keys – keys in adata.obs that correspond to continuous data. These covariates can be added in addition to the batch covariate and are also treated as nuisance factors (i.e., the model tries to minimize their effects on the latent space). Thus, these should not be used for biologically-relevant factors that you do _not_ want to correct for.
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train
(max_epochs: Optional[int, None] = None, batch_size: int = 2500, train_size: float = 1, lr: float = 0.002, **kwargs)[source]¶ Train the model with useful defaults
- Parameters
max_epochs – Number of passes through the dataset. If None, defaults to np.min([round((20000 / n_cells) * 400), 400])
train_size – Size of training set in the range [0.0, 1.0].
batch_size – Minibatch size to use during training. If None, no minibatching occurs and all data is copied to device (e.g., GPU).
lr – Optimiser learning rate (default optimiser is
ClippedAdam
). Specifying optimiser via plan_kwargs overrides this choice of lr.kwargs – Other arguments to scvi.model.base.PyroSviTrainMixin().train() method
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export_posterior
(adata, sample_kwargs: Optional[dict, None] = None, export_slot: str = 'mod', add_to_varm: list = ['means', 'stds', 'q05', 'q95'], scale_average_detection: bool = True, use_quantiles: bool = False)[source]¶ Summarise posterior distribution and export results (cell abundance) to anndata object: 1. adata.obsm: Estimated references expression signatures (average mRNA count in each cell type),
as pd.DataFrames for each posterior distribution summary add_to_varm, posterior mean, sd, 5% and 95% quantiles ([‘means’, ‘stds’, ‘q05’, ‘q95’]). If export to adata.varm fails with error, results are saved to adata.var instead.
- adata.uns: Posterior of all parameters, model name, date,
cell type names (‘factor_names’), obs and var names.
- Parameters
adata – anndata object where results should be saved
sample_kwargs –
- arguments for self.sample_posterior (generating and summarising posterior samples), namely:
num_samples - number of samples to use (Default = 1000). batch_size - data batch size (keep low enough to fit on GPU, default 2048). use_gpu - use gpu for generating samples?
export_slot – adata.uns slot where to export results
add_to_varm – posterior distribution summary to export in adata.varm ([‘means’, ‘stds’, ‘q05’, ‘q95’]).
use_quantiles – compute quantiles directly (True, more memory efficient) or use samples (False, default). If True, means and stds cannot be computed so are not exported and returned.
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plot_QC
(summary_name: str = 'means', use_n_obs: int = 1000, scale_average_detection: bool = True)[source]¶ Show quality control plots: 1. Reconstruction accuracy to assess if there are any issues with model training.
The plot should be roughly diagonal, strong deviations signal problems that need to be investigated. Plotting is slow because expected value of mRNA count needs to be computed from model parameters. Random observations are used to speed up computation.
- Estimated reference expression signatures (accounting for batch effect)
compared to average expression in each cluster. We expect the signatures to be different from average when batch effects are present, however, when this plot is very different from a perfect diagonal, such as very low values on Y-axis, non-zero density everywhere) it indicates problems with signature estimation.
- Parameters
summary_name – posterior distribution summary to use (‘means’, ‘stds’, ‘q05’, ‘q95’)