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_model.py
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_model.py
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from __future__ import annotations
import logging
import numpy as np
import pandas as pd
import torch
from anndata import AnnData
from lightning.pytorch.callbacks import Callback
from scvi import REGISTRY_KEYS
from scvi.data import AnnDataManager
from scvi.data.fields import (
CategoricalJointObsField,
CategoricalObsField,
LayerField,
NumericalJointObsField,
NumericalObsField,
)
from scvi.dataloaders import DataSplitter
from scvi.external.cellassign._module import CellAssignModule
from scvi.model._utils import get_max_epochs_heuristic
from scvi.model.base import BaseModelClass, UnsupervisedTrainingMixin
from scvi.train import LoudEarlyStopping, TrainingPlan, TrainRunner
from scvi.utils import setup_anndata_dsp
from scvi.utils._docstrings import devices_dsp
logger = logging.getLogger(__name__)
B = 10
class CellAssign(UnsupervisedTrainingMixin, BaseModelClass):
"""Reimplementation of CellAssign for reference-based annotation :cite:p:`Zhang19`.
Original implementation: https://github.com/irrationone/cellassign.
Parameters
----------
adata
single-cell AnnData object that has been registered via :meth:`~scvi.external.CellAssign.setup_anndata`.
The object should be subset to contain the same genes as the cell type marker dataframe.
cell_type_markers
Binary marker gene DataFrame of genes by cell types. Gene names corresponding to `adata.var_names`
should be in DataFrame index, and cell type labels should be the columns.
**model_kwargs
Keyword args for :class:`~scvi.external.cellassign.CellAssignModule`
Examples
--------
>>> adata = scvi.data.read_h5ad(path_to_anndata)
>>> library_size = adata.X.sum(1)
>>> adata.obs["size_factor"] = library_size / np.mean(library_size)
>>> marker_gene_mat = pd.read_csv(path_to_marker_gene_csv)
>>> bdata = adata[:, adata.var.index.isin(marker_gene_mat.index)].copy()
>>> CellAssign.setup_anndata(bdata, size_factor_key="size_factor")
>>> model = CellAssign(bdata, marker_gene_mat)
>>> model.train()
>>> predictions = model.predict(bdata)
Notes
-----
Size factors in the R implementation of CellAssign are computed using scran. An approximate approach
computes the sum of UMI counts (library size) over all genes and divides by the mean library size.
See further usage examples in the following tutorial:
1. :doc:`/tutorials/notebooks/scrna/cellassign_tutorial`
"""
def __init__(
self,
adata: AnnData,
cell_type_markers: pd.DataFrame,
**model_kwargs,
):
try:
cell_type_markers = cell_type_markers.loc[adata.var_names]
except KeyError as err:
raise KeyError("Anndata and cell type markers do not contain the same genes.") from err
super().__init__(adata)
self.n_genes = self.summary_stats.n_vars
self.cell_type_markers = cell_type_markers
rho = torch.Tensor(cell_type_markers.to_numpy())
n_cats_per_cov = (
self.adata_manager.get_state_registry(REGISTRY_KEYS.CAT_COVS_KEY).n_cats_per_key
if REGISTRY_KEYS.CAT_COVS_KEY in self.adata_manager.data_registry
else None
)
adata = self._validate_anndata(adata)
x = self.get_from_registry(adata, REGISTRY_KEYS.X_KEY)
col_means = np.asarray(np.mean(x, 0)).ravel() # (g)
col_means_mu, col_means_std = np.mean(col_means), np.std(col_means)
col_means_normalized = torch.Tensor((col_means - col_means_mu) / col_means_std)
# compute basis means for phi - shape (B)
basis_means = np.linspace(np.min(x), np.max(x), B) # (B)
self.module = CellAssignModule(
n_genes=self.n_genes,
rho=rho,
basis_means=basis_means,
b_g_0=col_means_normalized,
n_batch=self.summary_stats.n_batch,
n_cats_per_cov=n_cats_per_cov,
n_continuous_cov=self.summary_stats.get("n_extra_continuous_covs", 0),
**model_kwargs,
)
self._model_summary_string = (
f"CellAssign Model with params: \nn_genes: {self.n_genes}, n_labels: {rho.shape[1]}"
)
self.init_params_ = self._get_init_params(locals())
@torch.inference_mode()
def predict(self) -> pd.DataFrame:
"""Predict soft cell type assignment probability for each cell."""
adata = self._validate_anndata(None)
scdl = self._make_data_loader(adata=adata)
predictions = []
for tensors in scdl:
generative_inputs = self.module._get_generative_input(tensors, None)
outputs = self.module.generative(**generative_inputs)
gamma = outputs["gamma"]
predictions += [gamma.cpu()]
return pd.DataFrame(torch.cat(predictions).numpy(), columns=self.cell_type_markers.columns)
@devices_dsp.dedent
def train(
self,
max_epochs: int = 400,
lr: float = 3e-3,
accelerator: str = "auto",
devices: int | list[int] | str = "auto",
train_size: float = 0.9,
validation_size: float | None = None,
shuffle_set_split: bool = True,
batch_size: int = 1024,
datasplitter_kwargs: dict | None = None,
plan_kwargs: dict | None = None,
early_stopping: bool = True,
early_stopping_patience: int = 15,
early_stopping_min_delta: float = 0.0,
**kwargs,
):
"""Trains the model.
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`.
early_stopping
Adds callback for early stopping on validation_loss
early_stopping_patience
Number of times early stopping metric can not improve over early_stopping_min_delta
early_stopping_min_delta
Threshold for counting an epoch torwards patience
`train()` will overwrite values present in `plan_kwargs`, when appropriate.
**kwargs
Other keyword args for :class:`~scvi.train.Trainer`.
"""
update_dict = {"lr": lr, "weight_decay": 1e-10}
if plan_kwargs is not None:
plan_kwargs.update(update_dict)
else:
plan_kwargs = update_dict
datasplitter_kwargs = datasplitter_kwargs or {}
if "callbacks" in kwargs:
kwargs["callbacks"] += [ClampCallback()]
else:
kwargs["callbacks"] = [ClampCallback()]
if early_stopping:
early_stopping_callback = [
LoudEarlyStopping(
monitor="elbo_validation",
min_delta=early_stopping_min_delta,
patience=early_stopping_patience,
mode="min",
)
]
if "callbacks" in kwargs:
kwargs["callbacks"] += early_stopping_callback
else:
kwargs["callbacks"] = early_stopping_callback
kwargs["check_val_every_n_epoch"] = 1
if max_epochs is None:
max_epochs = get_max_epochs_heuristic(self.adata.n_obs)
plan_kwargs = plan_kwargs if isinstance(plan_kwargs, dict) else {}
data_splitter = DataSplitter(
self.adata_manager,
train_size=train_size,
validation_size=validation_size,
batch_size=batch_size,
shuffle_set_split=shuffle_set_split,
**datasplitter_kwargs,
)
training_plan = TrainingPlan(self.module, **plan_kwargs)
runner = TrainRunner(
self,
training_plan=training_plan,
data_splitter=data_splitter,
max_epochs=max_epochs,
accelerator=accelerator,
devices=devices,
**kwargs,
)
return runner()
@classmethod
@setup_anndata_dsp.dedent
def setup_anndata(
cls,
adata: AnnData,
size_factor_key: str,
batch_key: str | None = None,
categorical_covariate_keys: list[str] | None = None,
continuous_covariate_keys: list[str] | None = None,
layer: str | None = None,
**kwargs,
):
"""%(summary)s.
Parameters
----------
%(param_adata)s
size_factor_key
key in `adata.obs` with continuous valued size factors.
%(param_batch_key)s
%(param_layer)s
%(param_cat_cov_keys)s
%(param_cont_cov_keys)s
"""
setup_method_args = cls._get_setup_method_args(**locals())
anndata_fields = [
LayerField(REGISTRY_KEYS.X_KEY, layer, is_count_data=True),
NumericalObsField(REGISTRY_KEYS.SIZE_FACTOR_KEY, size_factor_key),
CategoricalObsField(REGISTRY_KEYS.BATCH_KEY, batch_key),
CategoricalJointObsField(REGISTRY_KEYS.CAT_COVS_KEY, categorical_covariate_keys),
NumericalJointObsField(REGISTRY_KEYS.CONT_COVS_KEY, continuous_covariate_keys),
]
adata_manager = AnnDataManager(fields=anndata_fields, setup_method_args=setup_method_args)
adata_manager.register_fields(adata, **kwargs)
cls.register_manager(adata_manager)
class ClampCallback(Callback):
"""Clamp callback."""
def __init__(self):
super().__init__()
def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx):
"""Clamp parameters."""
with torch.inference_mode():
pl_module.module.delta_log.clamp_(np.log(pl_module.module.min_delta))
super().on_train_batch_end(trainer, pl_module, outputs, batch, batch_idx)