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_condscvi.py
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_condscvi.py
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from __future__ import annotations
import logging
import warnings
import numpy as np
import torch
from anndata import AnnData
from sklearn.cluster import KMeans
from scvi import REGISTRY_KEYS, settings
from scvi.data import AnnDataManager
from scvi.data.fields import CategoricalObsField, LayerField
from scvi.model.base import (
BaseModelClass,
RNASeqMixin,
UnsupervisedTrainingMixin,
VAEMixin,
)
from scvi.module import VAEC
from scvi.utils import setup_anndata_dsp
from scvi.utils._docstrings import devices_dsp
logger = logging.getLogger(__name__)
class CondSCVI(RNASeqMixin, VAEMixin, UnsupervisedTrainingMixin, BaseModelClass):
"""Conditional version of single-cell Variational Inference, used for multi-resolution deconvolution of spatial transcriptomics data :cite:p:`Lopez22`.
Parameters
----------
adata
AnnData object that has been registered via :meth:`~scvi.model.CondSCVI.setup_anndata`.
n_hidden
Number of nodes per hidden layer.
n_latent
Dimensionality of the latent space.
n_layers
Number of hidden layers used for encoder and decoder NNs.
weight_obs
Whether to reweight observations by their inverse proportion (useful for lowly abundant cell types)
dropout_rate
Dropout rate for neural networks.
**module_kwargs
Keyword args for :class:`~scvi.modules.VAEC`
Examples
--------
>>> adata = anndata.read_h5ad(path_to_anndata)
>>> scvi.model.CondSCVI.setup_anndata(adata, "labels")
>>> vae = scvi.model.CondSCVI(adata)
>>> vae.train()
>>> adata.obsm["X_CondSCVI"] = vae.get_latent_representation()
Notes
-----
See further usage examples in the following tutorial:
1. :doc:`/tutorials/notebooks/spatial/DestVI_tutorial`
"""
_module_cls = VAEC
def __init__(
self,
adata: AnnData,
n_hidden: int = 128,
n_latent: int = 5,
n_layers: int = 2,
weight_obs: bool = False,
dropout_rate: float = 0.05,
**module_kwargs,
):
super().__init__(adata)
n_labels = self.summary_stats.n_labels
n_vars = self.summary_stats.n_vars
if weight_obs:
ct_counts = np.unique(
self.get_from_registry(adata, REGISTRY_KEYS.LABELS_KEY),
return_counts=True,
)[1]
ct_prop = ct_counts / np.sum(ct_counts)
ct_prop[ct_prop < 0.05] = 0.05
ct_prop = ct_prop / np.sum(ct_prop)
ct_weight = 1.0 / ct_prop
module_kwargs.update({"ct_weight": ct_weight})
self.module = self._module_cls(
n_input=n_vars,
n_labels=n_labels,
n_hidden=n_hidden,
n_latent=n_latent,
n_layers=n_layers,
dropout_rate=dropout_rate,
**module_kwargs,
)
self._model_summary_string = (
"Conditional SCVI Model with the following params: \nn_hidden: {}, n_latent: {}, n_layers: {}, dropout_rate: {}, weight_obs: {}"
).format(n_hidden, n_latent, n_layers, dropout_rate, weight_obs)
self.init_params_ = self._get_init_params(locals())
@torch.inference_mode()
def get_vamp_prior(self, adata: AnnData | None = None, p: int = 10) -> np.ndarray:
r"""Return an empirical prior over the cell-type specific latent space (vamp prior) that may be used for deconvolution.
Parameters
----------
adata
AnnData object with equivalent structure to initial AnnData. If `None`, defaults to the
AnnData object used to initialize the model.
p
number of clusters in kmeans clustering for cell-type sub-clustering for empirical prior
Returns
-------
mean_vprior: np.ndarray
(n_labels, p, D) array
var_vprior
(n_labels, p, D) array
"""
if self.is_trained_ is False:
warnings.warn(
"Trying to query inferred values from an untrained model. Please train "
"the model first.",
UserWarning,
stacklevel=settings.warnings_stacklevel,
)
adata = self._validate_anndata(adata)
# Extracting latent representation of adata including variances.
mean_vprior = np.zeros((self.summary_stats.n_labels, p, self.module.n_latent))
var_vprior = np.ones((self.summary_stats.n_labels, p, self.module.n_latent))
mp_vprior = np.zeros((self.summary_stats.n_labels, p))
labels_state_registry = self.adata_manager.get_state_registry(REGISTRY_KEYS.LABELS_KEY)
key = labels_state_registry.original_key
mapping = labels_state_registry.categorical_mapping
scdl = self._make_data_loader(adata=adata, batch_size=p)
mean = []
var = []
for tensors in scdl:
x = tensors[REGISTRY_KEYS.X_KEY]
y = tensors[REGISTRY_KEYS.LABELS_KEY]
out = self.module.inference(x, y)
mean_, var_ = out["qz"].loc, (out["qz"].scale ** 2)
mean += [mean_.cpu()]
var += [var_.cpu()]
mean_cat, var_cat = torch.cat(mean).numpy(), torch.cat(var).numpy()
for ct in range(self.summary_stats["n_labels"]):
local_indices = np.where(adata.obs[key] == mapping[ct])[0]
n_local_indices = len(local_indices)
if "overclustering_vamp" not in adata.obs.columns:
if p < n_local_indices and p > 0:
overclustering_vamp = KMeans(n_clusters=p, n_init=30).fit_predict(
mean_cat[local_indices]
)
else:
# Every cell is its own cluster
overclustering_vamp = np.arange(n_local_indices)
else:
overclustering_vamp = adata[local_indices, :].obs["overclustering_vamp"]
keys, counts = np.unique(overclustering_vamp, return_counts=True)
n_labels_overclustering = len(keys)
if n_labels_overclustering > p:
error_mess = """
Given cell type specific clustering contains more clusters than vamp_prior_p.
Increase value of vamp_prior_p to largest number of cell type specific clusters."""
raise ValueError(error_mess)
var_cluster = np.ones(
[
n_labels_overclustering,
self.module.n_latent,
]
)
mean_cluster = np.zeros_like(var_cluster)
for index, cluster in enumerate(keys):
indices_curr = local_indices[np.where(overclustering_vamp == cluster)[0]]
var_cluster[index, :] = np.mean(var_cat[indices_curr], axis=0) + np.var(
mean_cat[indices_curr], axis=0
)
mean_cluster[index, :] = np.mean(mean_cat[indices_curr], axis=0)
slicing = slice(n_labels_overclustering)
mean_vprior[ct, slicing, :] = mean_cluster
var_vprior[ct, slicing, :] = var_cluster
mp_vprior[ct, slicing] = counts / sum(counts)
return mean_vprior, var_vprior, mp_vprior
@devices_dsp.dedent
def train(
self,
max_epochs: int = 300,
lr: float = 0.001,
accelerator: str = "auto",
devices: int | list[int] | str = "auto",
train_size: float = 1,
validation_size: float | None = None,
shuffle_set_split: bool = True,
batch_size: int = 128,
datasplitter_kwargs: dict | None = None,
plan_kwargs: dict | None = None,
**kwargs,
):
"""Trains the model using MAP inference.
Parameters
----------
max_epochs
Number of epochs to train for
lr
Learning rate for optimization.
%(param_accelerator)s
%(param_devices)s
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.
shuffle_set_split
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
Minibatch size to use during training.
datasplitter_kwargs
Additional keyword arguments passed into :class:`~scvi.dataloaders.DataSplitter`.
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,
accelerator=accelerator,
devices=devices,
train_size=train_size,
validation_size=validation_size,
shuffle_set_split=shuffle_set_split,
batch_size=batch_size,
datasplitter_kwargs=datasplitter_kwargs,
plan_kwargs=plan_kwargs,
**kwargs,
)
@classmethod
@setup_anndata_dsp.dedent
def setup_anndata(
cls,
adata: AnnData,
labels_key: str | None = None,
layer: str | None = None,
**kwargs,
):
"""%(summary)s.
Parameters
----------
%(param_adata)s
%(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)