Backport PR #4201 on branch 1.12.x (chore: stale AnnData.concatenate references)

docs/release-notes/0.3.0.md

@@ -1,7 +1,7 @@
 (v0.3.0)=
 ### 0.3.0 {small}`2017-11-16`
 
-- {class}`~anndata.AnnData` gains method {meth}`~anndata.AnnData.concatenate` {smaller}`A Wolf`
+- {class}`~anndata.AnnData` gains method `AnnData.concatenate` {smaller}`A Wolf`
 - {class}`~anndata.AnnData` is available as the separate [anndata] package {smaller}`P Angerer, A Wolf`
 - results of [PAGA](https://github.com/theislab/paga) simplified {smaller}`A Wolf`
 

src/scanpy/external/pp/_mnn_correct.py

@@ -68,13 +68,13 @@ def mnn_correct(  # noqa: PLR0913
         correction. Typically, a list of highly variable genes (HVGs).
         When set to `None`, uses all vars.
     batch_key
-        The `batch_key` for :meth:`~anndata.AnnData.concatenate`.
+        The `batch_key` for ``anndata.AnnData.concatenate``.
         Only valid when `do_concatenate` and supplying `AnnData` objects.
     index_unique
-        The `index_unique` for :meth:`~anndata.AnnData.concatenate`.
+        The `index_unique` for ``anndata.AnnData.concatenate``.
         Only valid when `do_concatenate` and supplying `AnnData` objects.
     batch_categories
-        The `batch_categories` for :meth:`~anndata.AnnData.concatenate`.
+        The `batch_categories` for ``anndata.AnnData.concatenate``.
         Only valid when `do_concatenate` and supplying AnnData objects.
     k
         Number of mutual nearest neighbors.

src/scanpy/external/tl/_harmony_timeseries.py

@@ -85,7 +85,7 @@ def harmony_timeseries(
 
     >>> from itertools import product
     >>> import pandas as pd
-    >>> from anndata import AnnData
+    >>> from anndata import AnnData, concat
     >>> import scanpy as sc
     >>> import scanpy.external as sce
 
@@ -99,11 +99,12 @@ def harmony_timeseries(
 
     >>> adata_ref = sc.datasets.pbmc3k()
     >>> start = [596, 615, 1682, 1663, 1409, 1432]
-    >>> adata = AnnData.concatenate(
-    ...     *(adata_ref[i : i + 1000] for i in start),
+    >>> adata = concat(
+    ...     [adata_ref[i : i + 1000] for i in start],
     ...     join="outer",
-    ...     batch_key="sample",
-    ...     batch_categories=[f"sa{i}_Rep{j}" for i, j in product((1, 2, 3), (1, 2))],
+    ...     label="sample",
+    ...     keys=[f"sa{i}_Rep{j}" for i, j in product((1, 2, 3), (1, 2))],
+    ...     index_unique="-",
     ... )
     >>> time_points = adata.obs["sample"].str.split("_", expand=True)[0]
     >>> adata.obs["time_points"] = pd.Categorical(

tests/external/test_harmony_timeseries.py

@@ -2,7 +2,7 @@
 
 from itertools import product
 
-from anndata import AnnData
+from anndata import concat
 
 import scanpy as sc
 import scanpy.external as sce
@@ -15,11 +15,12 @@
 def test_load_timepoints_from_anndata_list():
     adata_ref = pbmc3k()
     start = [596, 615, 1682, 1663, 1409, 1432]
-    adata = AnnData.concatenate(
-        *(adata_ref[i : i + 1000] for i in start),
+    adata = concat(
+        [adata_ref[i : i + 1000] for i in start],
         join="outer",
-        batch_key="sample",
-        batch_categories=[f"sa{i}_Rep{j}" for i, j in product((1, 2, 3), (1, 2))],
+        label="sample",
+        keys=[f"sa{i}_Rep{j}" for i, j in product((1, 2, 3), (1, 2))],
+        index_unique="-",
     )
     adata.obs["time_points"] = adata.obs["sample"].str.split("_", expand=True)[0]
     adata.obs["time_points"] = adata.obs["time_points"].astype("category")