Switch dendrogram implementation to aggregate

src/scanpy/_compat.py

@@ -4,10 +4,14 @@
 from dataclasses import dataclass, field
 from functools import cache, partial
 from pathlib import Path
+from typing import TYPE_CHECKING
 
 from legacy_api_wrap import legacy_api
 from packaging.version import Version
 
+if TYPE_CHECKING:
+    from importlib.metadata import PackageMetadata
+
 try:
     from dask.array import Array as DaskArray
 except ImportError:
@@ -60,14 +64,14 @@ def __exit__(self, *_excinfo) -> None:
             os.chdir(self._old_cwd.pop())
 
 
-def pkg_metadata(package):
+def pkg_metadata(package) -> PackageMetadata:
     from importlib.metadata import metadata
 
     return metadata(package)
 
 
 @cache
-def pkg_version(package):
+def pkg_version(package) -> Version:
     from importlib.metadata import version
 
     return Version(version(package))

src/scanpy/tools/_dendrogram.py

@@ -6,20 +6,23 @@
 
 from typing import TYPE_CHECKING
 
-import pandas as pd
+from anndata import AnnData
 from pandas.api.types import CategoricalDtype
 
+from scanpy.get._aggregated import aggregate
+
 from .. import logging as logg
 from .._compat import old_positionals
-from .._utils import _doc_params, raise_not_implemented_error_if_backed_type
+from .._utils import _doc_params
 from ..neighbors._doc import doc_n_pcs, doc_use_rep
 from ._utils import _choose_representation
 
 if TYPE_CHECKING:
     from collections.abc import Sequence
     from typing import Any
 
-    from anndata import AnnData
+    import numpy as np
+    import pandas as pd
 
 
 @old_positionals(
@@ -118,7 +121,6 @@
     >>> sc.pl.dotplot(adata, markers, groupby='bulk_labels', dendrogram=True)
     """
 
-    raise_not_implemented_error_if_backed_type(adata.X, "dendrogram")
     if isinstance(groupby, str):
         # if not a list, turn into a list
         groupby = [groupby]
@@ -135,10 +137,11 @@
             )
 
     if var_names is None:
-        rep_df = pd.DataFrame(
-            _choose_representation(adata, use_rep=use_rep, n_pcs=n_pcs)
+        rep = _choose_representation(adata, use_rep=use_rep, n_pcs=n_pcs)
+        rep_adata = AnnData(rep)
+        categorical: pd.Series[pd.CategoricalDtype] = adata.obs[groupby[0]].astype(
+            "category"
         )
-        categorical = adata.obs[groupby[0]]
         if len(groupby) > 1:
             for group in groupby[1:]:
                 # create new category by merging the given groupby categories
@@ -147,27 +150,25 @@
                 ).astype("category")
         categorical.name = "_".join(groupby)
 
-        rep_df.set_index(categorical, inplace=True)
-        categories: pd.Index = rep_df.index.categories
+        rep_adata.obs["cat"] = categorical
+        categories: pd.Index = categorical.cat.categories
     else:
         gene_names = adata.raw.var_names if use_raw else adata.var_names
         from ..plotting._anndata import _prepare_dataframe
 
         categories, rep_df = _prepare_dataframe(
             adata, gene_names, groupby, use_raw=use_raw
         )
+        rep_adata = AnnData(rep_df)
+        rep_adata.obs["cat"] = rep_df.index
 
     # aggregate values within categories using 'mean'
-    mean_df = (
-        rep_df.groupby(level=0, observed=True)
-        .mean()
-        .loc[categories]  # Fixed ordering for pandas < 2
-    )
+    means: np.ndarray = aggregate(rep_adata, by="cat", func="mean").layers["mean"]
 
     import scipy.cluster.hierarchy as sch
     from scipy.spatial import distance
 
-    corr_matrix = mean_df.T.corr(method=cor_method).clip(-1, 1)
+    corr_matrix = means.T.corr(method=cor_method).clip(-1, 1)
     corr_condensed = distance.squareform(1 - corr_matrix)
     z_var = sch.linkage(
         corr_condensed, method=linkage_method, optimal_ordering=optimal_ordering

tests/test_backed.py

@@ -2,10 +2,13 @@
 
 from functools import partial
 
+import h5py
 import pytest
 from anndata import read_h5ad
+from packaging.version import Version
 
 import scanpy as sc
+from scanpy._compat import pkg_version
 
 
 @pytest.mark.parametrize(
@@ -94,5 +97,11 @@ def test_scatter_backed(backed_adata):
     sc.pl.scatter(backed_adata, color="0", basis="pca")
 
 
-def test_dotplot_backed(backed_adata):
-    sc.pl.dotplot(backed_adata, ["0", "1", "2", "3"], groupby="cat")
+def test_dotplot_backed(request: pytest.FixtureRequest, backed_adata):
+    if isinstance(backed_adata.X, h5py.Dataset) and pkg_version("anndata") < Version(
+        "0.11.0.dev100"
+    ):
+        reason = "anndata bug when setting X to h5py.Dataset"
+        request.applymarker(pytest.mark.xfail(reason=reason))
+    sc.tl.dendrogram(backed_adata, "cat")
+    sc.pl.dotplot(backed_adata, ["0", "1", "2", "3"], groupby="cat", dendrogram=True)