update backbone clustering

spateo/tdr/models/__init__.py

@@ -2,6 +2,7 @@
     ElPiGraph_method,
     PrinCurve_method,
     SimplePPT_method,
+    backbone_scc,
     construct_backbone,
     map_gene_to_backbone,
     map_points_to_backbone,

spateo/tdr/models/models_backbone/__init__.py

@@ -1,3 +1,3 @@
-from .backbone import construct_backbone, update_backbone
+from .backbone import backbone_scc, construct_backbone, update_backbone
 from .backbone_methods import ElPiGraph_method, PrinCurve_method, SimplePPT_method
 from .backbone_utils import map_gene_to_backbone, map_points_to_backbone

spateo/tdr/models/models_backbone/backbone.py

@@ -5,9 +5,12 @@
 except ImportError:
     from typing_extensions import Literal
 
+import anndata as ad
 import numpy as np
+import pandas as pd
 from anndata import AnnData
 from pyvista import PolyData, UnstructuredGrid
+from scipy.sparse import issparse
 from scipy.spatial.distance import cdist
 
 
@@ -149,3 +152,82 @@ def update_backbone(
 
     updated_backbone.point_data[key_added] = np.arange(0, updated_backbone.n_points, 1)
     return updated_backbone
+
+
+def backbone_scc(
+    adata: AnnData,
+    backbone: PolyData,
+    genes: Optional[list] = None,
+    adata_nodes_key: str = "backbone_nodes",
+    backbone_nodes_key: str = "updated_nodes",
+    key_added: Optional[str] = "backbone_scc",
+    layer: Optional[str] = None,
+    e_neigh: int = 10,
+    s_neigh: int = 6,
+    cluster_method: Literal["leiden", "louvain"] = "leiden",
+    resolution: Optional[float] = None,
+    inplace: bool = True,
+) -> Optional[AnnData]:
+    """
+    Spatially constrained clustering (scc) along the backbone.
+
+    Args:
+        adata: The anndata object.
+        backbone: The backbone model.
+        genes: The list of genes that will be used to subset the data for clustering. If ``genes = None``, all genes will be used.
+        adata_nodes_key: The key that corresponds to the nodes in the adata.
+        backbone_nodes_key: The key that corresponds to the nodes in the backbone.
+        key_added: adata.obs key under which to add the cluster labels.
+        layer: The layer that will be used to retrieve data for dimension reduction and clustering. If ``layer = None``, ``.X`` is used.
+        e_neigh: the number of nearest neighbor in gene expression space.
+        s_neigh: the number of nearest neighbor in physical space.
+        cluster_method: the method that will be used to cluster the cells.
+        resolution: the resolution parameter of the louvain clustering algorithm.
+        inplace: Whether to copy adata or modify it inplace.
+
+    Returns:
+        An ``AnnData`` object is updated/copied with the ``key_added`` in the ``.obs`` attribute, storing the clustering results.
+    """
+    import dynamo as dyn
+    from dynamo.tools.utils import fetch_X_data
+
+    from ....tools import scc
+
+    adata = adata if inplace else adata.copy()
+    if "pp" not in adata.uns.keys():
+        adata.uns["pp"] = {}
+    genes, X_data = fetch_X_data(adata, genes, layer)
+    X_data = X_data.A if issparse(X_data) else X_data
+    X_data = pd.DataFrame(X_data, columns=genes)
+    X_data[adata_nodes_key] = adata.obs[adata_nodes_key].values
+    X_data = pd.DataFrame(X_data.groupby(by=adata_nodes_key).mean())
+    backbone_nodes = X_data.index
+
+    X_spatial = pd.DataFrame(backbone.points, index=backbone.point_data[backbone_nodes_key])
+    X_spatial = X_spatial.loc[backbone_nodes, :].values
+
+    backbone_adata = ad.AnnData(
+        X=X_data.values,
+        var=pd.DataFrame(index=X_data.columns),
+        obs=pd.DataFrame(backbone_nodes, columns=[adata_nodes_key]),
+        obsm={"spatial": X_spatial},
+        uns={"__type": "UMI", "pp": {}},
+    )
+
+    dyn.pp.normalize(backbone_adata)
+    dyn.pp.log1p(backbone_adata)
+    backbone_adata.obsm["X_backbone"] = backbone_adata.X
+    scc(
+        backbone_adata,
+        spatial_key="spatial",
+        pca_key="X_backbone",
+        e_neigh=e_neigh,
+        s_neigh=s_neigh,
+        resolution=resolution,
+        key_added="scc",
+        cluster_method=cluster_method,
+    )
+
+    cluster_dict = {i: c for i, c in zip(backbone_adata.obs[adata_nodes_key], backbone_adata.obs["scc"])}
+    adata.obs[key_added] = adata.obs[adata_nodes_key].map(lambda x: cluster_dict[x])
+    return None if inplace else adata