import logging
from typing import Literal
import numpy as np
import pandas as pd
import scipy
import torch
from anndata import AnnData
from mudata import MuData
from scvi.data import AnnDataManager, AnnDataManagerValidationCheck, fields
from scvi.model._utils import parse_device_args
from scvi.train import TrainingPlan
from scvi.utils import setup_anndata_dsp, track
from scvi.utils._docstrings import devices_dsp
from scviva.external.tangram._module import TANGRAM_REGISTRY_KEYS, TangramMapper
from scviva.model.base._spatial_base import SpatialBaseModel
logger = logging.getLogger(__name__)
[docs]
class Tangram(SpatialBaseModel):
"""Torch reimplementation of Tangram :cite:p:`Biancalani21`.
Maps single-cell RNA-seq data to spatial data. Original implementation:
https://github.com/broadinstitute/Tangram.
Currently the "cells" and "constrained" modes are implemented.
Parameters
----------
mdata
MuData object that has been registered via :meth:`~scviva.external.Tangram.setup_mudata`.
constrained
Whether to use the constrained version of Tangram instead of cells mode.
target_count
The number of cells to be filtered. Necessary when `constrained` is True.
**model_kwargs
Keyword args for :class:`~scviva.external.tangram.TangramMapper`
Examples
--------
>>> from scviva.external import Tangram
>>> ad_sc = anndata.read_h5ad(path_to_sc_anndata)
>>> ad_sp = anndata.read_h5ad(path_to_sp_anndata)
>>> markers = pd.read_csv(path_to_markers, index_col=0) # genes to use for mapping
>>> mdata = mudata.MuData(
{
"sp_full": ad_sp,
"sc_full": ad_sc,
"sp": ad_sp[:, markers].copy(),
"sc": ad_sc[:, markers].copy()
}
)
>>> modalities = {"density_prior_key": "sp", "sc_layer": "sc", "sp_layer": "sp"}
>>> Tangram.setup_mudata(
mdata, density_prior_key="rna_count_based_density", modalities=modalities
)
>>> tangram = Tangram(sc_adata)
>>> tangram.train()
>>> ad_sc.obsm["tangram_mapper"] = tangram.get_mapper_matrix()
>>> ad_sp.obsm["tangram_cts"] = tangram.project_cell_annotations(
ad_sc, ad_sp, ad_sc.obsm["tangram_mapper"], ad_sc.obs["labels"]
)
>>> projected_ad_sp = tangram.project_genes(ad_sc, ad_sp, ad_sc.obsm["tangram_mapper"])
Notes
-----
See further usage examples in the following tutorials:
1. :doc:`/tutorials/notebooks/spatial/tangram_scvi_tools`
"""
[docs]
def __init__(
self,
sc_adata: AnnData,
constrained: bool = False,
target_count: int | None = None,
**model_kwargs,
):
super().__init__(sc_adata)
self.n_obs_sc = self.adata_manager.get_from_registry(TANGRAM_REGISTRY_KEYS.SC_KEY).shape[0]
self.n_obs_sp = self.adata_manager.get_from_registry(TANGRAM_REGISTRY_KEYS.SP_KEY).shape[0]
if constrained and target_count is None:
raise ValueError("Please specify `target_count` when using constrained Tangram.")
has_density_prior = not self.adata_manager.fields[-1].is_empty
if has_density_prior:
prior = self.adata_manager.get_from_registry(TANGRAM_REGISTRY_KEYS.DENSITY_KEY)
if np.abs(prior.ravel().sum() - 1) > 1e-3:
raise ValueError("Density prior must sum to 1. Please normalize the prior.")
self.module = TangramMapper(
n_obs_sc=self.n_obs_sc,
n_obs_sp=self.n_obs_sp,
lambda_d=1.0 if has_density_prior else 0.0,
constrained=constrained,
target_count=target_count,
**model_kwargs,
)
self._model_summary_string = (
f"TangramMapper Model with params: \nn_obs_sc: {self.n_obs_sc}, "
f"n_obs_sp: {self.n_obs_sp}"
)
self.init_params_ = self._get_init_params(locals())
def get_mapper_matrix(self) -> np.ndarray:
"""Return the mapping matrix.
Returns
-------
Mapping matrix of shape (n_obs_sc, n_obs_sp)
"""
return torch.softmax(self.module.mapper_unconstrained, dim=1).detach().cpu().numpy()
@devices_dsp.dedent
def train(
self,
max_epochs: int = 1000,
accelerator: str = "auto",
devices: int | list[int] | str = "auto",
lr: float = 0.1,
plan_kwargs: dict | None = None,
):
"""Train the model.
Parameters
----------
max_epochs
Number of passes through the dataset.
%(param_accelerator)s
%(param_devices)s
lr
Optimizer learning rate (default optimizer is :class:`~torch.optim.Adam`).
Specifying optimizer via plan_kwargs overrides this choice of lr.
plan_kwargs
Keyword args for :class:`~scvi.train.TrainingPlan`. Keyword arguments passed to
`train()` will overwrite values present in `plan_kwargs`, when appropriate.
"""
update_dict = {
"lr": lr,
"eps": 1e-8,
"weight_decay": 0,
}
if plan_kwargs is not None:
if "optim_kwargs" in plan_kwargs:
plan_kwargs = plan_kwargs.copy()
optim_kwargs = {
"lr" if key == "learning_rate" else key: value
for key, value in plan_kwargs.pop("optim_kwargs").items()
}
plan_kwargs.update(optim_kwargs)
plan_kwargs.update(update_dict)
else:
plan_kwargs = update_dict
_, _, device = parse_device_args(
accelerator,
devices,
return_device="torch",
validate_single_device=True,
)
self.module.to(device)
logger.info(f"Tangram torch module moved to {device}.")
tensor_dict = self._get_tensor_dict(device=device)
training_plan = TrainingPlan(self.module, **plan_kwargs)
self._training_plan = training_plan
optimizer = training_plan.configure_optimizers()["optimizer"]
pbar = track(range(max_epochs), style="tqdm", description="Training")
history = pd.DataFrame(index=np.arange(max_epochs), columns=["loss"])
for i in pbar:
training_plan.train()
optimizer.zero_grad()
_, _, loss_output = training_plan(tensor_dict)
loss_output.loss.backward()
optimizer.step()
loss = loss_output.loss.detach().cpu().item()
history.iloc[i] = loss
pbar.set_description(f"Training... Loss: {loss}")
self.history_ = {}
self.history_["loss"] = history
self.module.eval()
@classmethod
@setup_anndata_dsp.dedent
def setup_mudata(
cls,
mdata: MuData,
density_prior_key: str | Literal["rna_count_based", "uniform"] | None = "rna_count_based",
sc_layer: str | None = None,
sp_layer: str | None = None,
modalities: dict[str, str] | None = None,
**kwargs,
):
"""%(summary)s.
Parameters
----------
mdata
MuData with scRNA and spatial modalities.
sc_layer
Layer key in scRNA modality to use for training.
sp_layer
Layer key in spatial modality to use for training.
density_prior_key
Key in spatial modality obs for density prior.
modalities
Mapping from `setup_mudata` param name to modality in mdata.
"""
setup_method_args = cls._get_setup_method_args(**locals())
if modalities is None:
raise ValueError("Modalities cannot be None.")
modalities = cls._create_modalities_attr_dict(modalities, setup_method_args)
mudata_fields = [
fields.MuDataLayerField(
TANGRAM_REGISTRY_KEYS.SC_KEY,
sc_layer,
mod_key=modalities.sc_layer,
is_count_data=False,
mod_required=True,
),
fields.MuDataLayerField(
TANGRAM_REGISTRY_KEYS.SP_KEY,
sp_layer,
mod_key=modalities.sp_layer,
is_count_data=False,
mod_required=True,
),
fields.MuDataNumericalObsField(
TANGRAM_REGISTRY_KEYS.DENSITY_KEY,
density_prior_key,
mod_key=modalities.density_prior_key,
required=False,
mod_required=True,
),
]
adata_manager = AnnDataManager(
fields=mudata_fields,
setup_method_args=setup_method_args,
validation_checks=AnnDataManagerValidationCheck(check_fully_paired_mudata=False),
)
adata_manager.register_fields(mdata, **kwargs)
sc_state = adata_manager.get_state_registry(TANGRAM_REGISTRY_KEYS.SC_KEY)
sp_state = adata_manager.get_state_registry(TANGRAM_REGISTRY_KEYS.SP_KEY)
# Need to access the underlying AnnData field to get these attributes
if not (
pd.Index(sc_state[fields.LayerField.COLUMN_NAMES_KEY]).equals(
sp_state[fields.LayerField.COLUMN_NAMES_KEY]
),
):
raise ValueError(
"The column names of the spatial and single-cell layers must be the same."
)
cls.register_manager(adata_manager)
@classmethod
def setup_anndata(cls):
"""Not implemented, use `setup_mudata`."""
raise NotImplementedError("Use `setup_mudata` to setup a MuData object for training.")
def _get_tensor_dict(
self,
device: torch.device,
) -> dict[str, torch.Tensor]:
"""Get training data for Tangram model.
Tangram does not minibatch, so we just make a dictionary of
torch tensors here.
"""
tensor_dict = {}
for key in TANGRAM_REGISTRY_KEYS:
try:
tensor_dict[key] = self.adata_manager.get_from_registry(key)
# When density is missing
except KeyError:
continue
if scipy.sparse.issparse(tensor_dict[key]):
tensor_dict[key] = tensor_dict[key].toarray()
elif isinstance(tensor_dict[key], pd.DataFrame):
tensor_dict[key] = tensor_dict[key].values
else:
tensor_dict[key] = tensor_dict[key]
tensor_dict[key] = torch.as_tensor(
tensor_dict[key], dtype=torch.float32, device=device
)
return tensor_dict
@staticmethod
def project_cell_annotations(
adata_sc: AnnData, adata_sp: AnnData, mapper: np.ndarray, labels: pd.Series
) -> pd.DataFrame:
"""Project cell annotations to spatial data.
Parameters
----------
adata_sc
AnnData object with single-cell data.
adata_sp
AnnData object with spatial data.
mapper
Mapping from single-cell to spatial data.
labels
Cell annotations to project.
Returns
-------
Projected annotations as a :class:`pd.DataFrame` with shape (n_sp, n_labels).
"""
if len(labels) != adata_sc.shape[0]:
raise ValueError(
"The number of labels must match the number of cells in the sc AnnData object."
)
cell_type_df = pd.get_dummies(labels)
projection = mapper.T @ cell_type_df.values
return pd.DataFrame(
index=adata_sp.obs_names, columns=cell_type_df.columns, data=projection
)
@staticmethod
def project_genes(adata_sc: AnnData, adata_sp: AnnData, mapper: np.ndarray) -> AnnData:
"""Project gene expression to spatial data.
Parameters
----------
adata_sc
AnnData object with single-cell data.
adata_sp
AnnData object with spatial data.
mapper
Mapping from single-cell to spatial data.
Returns
-------
:class:`anndata.AnnData` object with projected gene expression.
"""
adata_ge = AnnData(
X=mapper.T @ adata_sc.X,
obs=adata_sp.obs,
var=adata_sc.var,
uns=adata_sc.uns,
)
return adata_ge
