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_model.py
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_model.py
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import logging
from typing import Optional, Tuple, Union
import numpy as np
import pandas as pd
import torch
from anndata import AnnData
from scvi import REGISTRY_KEYS
from scvi._compat import Literal
from scvi.data import AnnDataManager
from scvi.data.fields import CategoricalObsField, LayerField, NumericalObsField
from scvi.external.stereoscope._module import RNADeconv, SpatialDeconv
from scvi.model.base import BaseModelClass, UnsupervisedTrainingMixin
from scvi.utils import setup_anndata_dsp
logger = logging.getLogger(__name__)
class RNAStereoscope(UnsupervisedTrainingMixin, BaseModelClass):
"""
Reimplementation of Stereoscope :cite:p:`Andersson20` for deconvolution of spatial transcriptomics from single-cell transcriptomics.
https://github.com/almaan/stereoscope.
Parameters
----------
sc_adata
single-cell AnnData object that has been registered via :meth:`~scvi.external.RNAStereoscope.setup_anndata`.
**model_kwargs
Keyword args for :class:`~scvi.external.stereoscope.RNADeconv`
Examples
--------
>>> sc_adata = anndata.read_h5ad(path_to_sc_anndata)
>>> scvi.external.RNAStereoscope.setup_anndata(sc_adata, labels_key="labels")
>>> stereo = scvi.external.stereoscope.RNAStereoscope(sc_adata)
>>> stereo.train()
"""
def __init__(
self,
sc_adata: AnnData,
**model_kwargs,
):
super().__init__(sc_adata)
self.n_genes = self.summary_stats.n_vars
self.n_labels = self.summary_stats.n_labels
# first we have the scRNA-seq model
self.module = RNADeconv(
n_genes=self.n_genes,
n_labels=self.n_labels,
**model_kwargs,
)
self._model_summary_string = (
"RNADeconv Model with params: \nn_genes: {}, n_labels: {}"
).format(
self.n_genes,
self.n_labels,
)
self.init_params_ = self._get_init_params(locals())
def train(
self,
max_epochs: int = 400,
lr: float = 0.01,
use_gpu: Optional[Union[str, int, bool]] = None,
train_size: float = 1,
validation_size: Optional[float] = None,
batch_size: int = 128,
plan_kwargs: Optional[dict] = None,
**kwargs,
):
"""
Trains the model using MAP inference.
Parameters
----------
max_epochs
Number of epochs to train for
lr
Learning rate for optimization.
use_gpu
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
Size of training set in the range [0.0, 1.0].
validation_size
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
Minibatch size to use during training.
plan_kwargs
Keyword args for :class:`~scvi.train.TrainingPlan`. Keyword arguments passed to
`train()` will overwrite values present in `plan_kwargs`, when appropriate.
**kwargs
Other keyword args for :class:`~scvi.train.Trainer`.
"""
update_dict = {
"lr": lr,
}
if plan_kwargs is not None:
plan_kwargs.update(update_dict)
else:
plan_kwargs = update_dict
super().train(
max_epochs=max_epochs,
use_gpu=use_gpu,
train_size=train_size,
validation_size=validation_size,
batch_size=batch_size,
plan_kwargs=plan_kwargs,
**kwargs,
)
@classmethod
@setup_anndata_dsp.dedent
def setup_anndata(
cls,
adata: AnnData,
labels_key: Optional[str] = None,
layer: Optional[str] = None,
**kwargs,
):
"""
%(summary)s.
Parameters
----------
%(param_labels_key)s
%(param_layer)s
"""
setup_method_args = cls._get_setup_method_args(**locals())
anndata_fields = [
LayerField(REGISTRY_KEYS.X_KEY, layer, is_count_data=True),
CategoricalObsField(REGISTRY_KEYS.LABELS_KEY, labels_key),
]
adata_manager = AnnDataManager(
fields=anndata_fields, setup_method_args=setup_method_args
)
adata_manager.register_fields(adata, **kwargs)
cls.register_manager(adata_manager)
class SpatialStereoscope(UnsupervisedTrainingMixin, BaseModelClass):
"""
Reimplementation of Stereoscope :cite:p:`Andersson20` for deconvolution of spatial transcriptomics from single-cell transcriptomics.
https://github.com/almaan/stereoscope.
Parameters
----------
st_adata
spatial transcriptomics AnnData object that has been registered via :meth:`~scvi.external.SpatialStereoscope.setup_anndata`.
sc_params
parameters of the model learned from the single-cell RNA seq data for deconvolution.
cell_type_mapping
numpy array mapping for the cell types used in the deconvolution
prior_weight
how to reweight the minibatches for stochastic optimization. "n_obs" is the valid
procedure, "minibatch" is the procedure implemented in Stereoscope.
**model_kwargs
Keyword args for :class:`~scvi.external.stereoscope.SpatialDeconv`
Examples
--------
>>> sc_adata = anndata.read_h5ad(path_to_sc_anndata)
>>> scvi.external.RNAStereoscope.setup_anndata(sc_adata, labels_key="labels")
>>> sc_model = scvi.external.stereoscope.RNAStereoscope(sc_adata)
>>> sc_model.train()
>>> st_adata = anndata.read_h5ad(path_to_st_anndata)
>>> scvi.external.SpatialStereoscope.setup_anndata(st_adata)
>>> stereo = scvi.external.stereoscope.SpatialStereoscope.from_rna_model(st_adata, sc_model)
>>> stereo.train()
>>> st_adata.obsm["deconv"] = stereo.get_proportions()
Notes
-----
See further usage examples in the following tutorials:
1. :doc:`/user_guide/notebooks/stereoscope_heart_LV_tutorial`
"""
def __init__(
self,
st_adata: AnnData,
sc_params: Tuple[np.ndarray],
cell_type_mapping: np.ndarray,
prior_weight: Literal["n_obs", "minibatch"] = "n_obs",
**model_kwargs,
):
super().__init__(st_adata)
self.module = SpatialDeconv(
n_spots=st_adata.n_obs,
sc_params=sc_params,
prior_weight=prior_weight,
**model_kwargs,
)
self._model_summary_string = (
"RNADeconv Model with params: \nn_spots: {}"
).format(
st_adata.n_obs,
)
self.cell_type_mapping = cell_type_mapping
self.init_params_ = self._get_init_params(locals())
@classmethod
def from_rna_model(
cls,
st_adata: AnnData,
sc_model: RNAStereoscope,
prior_weight: Literal["n_obs", "minibatch"] = "n_obs",
layer: Optional[str] = None,
**model_kwargs,
):
"""
Alternate constructor for exploiting a pre-trained model on RNA-seq data.
Parameters
----------
st_adata
registed anndata object
sc_model
trained RNADeconv model
prior_weight
how to reweight the minibatches for stochastic optimization. "n_obs" is the valid
procedure, "minibatch" is the procedure implemented in Stereoscope.
layer
if not `None`, uses this as the key in `adata.layers` for raw count data.
**model_kwargs
Keyword args for :class:`~scvi.external.SpatialDeconv`
"""
cls.setup_anndata(st_adata, layer=layer)
return cls(
st_adata,
sc_model.module.get_params(),
sc_model.adata_manager.get_state_registry(
REGISTRY_KEYS.LABELS_KEY
).categorical_mapping,
prior_weight=prior_weight,
**model_kwargs,
)
def get_proportions(self, keep_noise=False) -> pd.DataFrame:
"""
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
whether to account for the noise term as a standalone cell type in the proportion estimate.
"""
self._check_if_trained()
column_names = self.cell_type_mapping
if keep_noise:
column_names = column_names.append("noise_term")
return pd.DataFrame(
data=self.module.get_proportions(keep_noise),
columns=column_names,
index=self.adata.obs.index,
)
def get_scale_for_ct(
self,
y: np.ndarray,
) -> np.ndarray:
r"""
Calculate the cell type specific expression.
Parameters
----------
y
numpy array containing the list of cell types
Returns
-------
gene_expression
"""
self._check_if_trained()
ind_y = np.array([np.where(ct == self.cell_type_mapping)[0][0] for ct in y])
if ind_y.shape != y.shape:
raise ValueError(
"Incorrect shape after matching cell types to reference mapping. Please check cell type query."
)
px_scale = self.module.get_ct_specific_expression(torch.tensor(ind_y)[:, None])
return np.array(px_scale.cpu())
def train(
self,
max_epochs: int = 400,
lr: float = 0.01,
use_gpu: Optional[Union[str, int, bool]] = None,
batch_size: int = 128,
plan_kwargs: Optional[dict] = None,
**kwargs,
):
"""
Trains the model using MAP inference.
Parameters
----------
max_epochs
Number of epochs to train for
lr
Learning rate for optimization.
use_gpu
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).
batch_size
Minibatch size to use during training.
plan_kwargs
Keyword args for :class:`~scvi.train.TrainingPlan`. Keyword arguments passed to
`train()` will overwrite values present in `plan_kwargs`, when appropriate.
**kwargs
Other keyword args for :class:`~scvi.train.Trainer`.
"""
update_dict = {
"lr": lr,
}
if plan_kwargs is not None:
plan_kwargs.update(update_dict)
else:
plan_kwargs = update_dict
super().train(
max_epochs=max_epochs,
use_gpu=use_gpu,
train_size=1,
validation_size=None,
batch_size=batch_size,
plan_kwargs=plan_kwargs,
**kwargs,
)
@classmethod
@setup_anndata_dsp.dedent
def setup_anndata(
cls,
adata: AnnData,
layer: Optional[str] = None,
**kwargs,
):
"""
%(summary)s.
Parameters
----------
%(param_layer)s
"""
setup_method_args = cls._get_setup_method_args(**locals())
# add index for each cell (provided to pyro plate for correct minibatching)
adata.obs["_indices"] = np.arange(adata.n_obs)
anndata_fields = [
LayerField(REGISTRY_KEYS.X_KEY, layer, is_count_data=True),
NumericalObsField(REGISTRY_KEYS.INDICES_KEY, "_indices"),
]
adata_manager = AnnDataManager(
fields=anndata_fields, setup_method_args=setup_method_args
)
adata_manager.register_fields(adata, **kwargs)
cls.register_manager(adata_manager)