Doc fixes for 1.11

docs/release-notes/1.11.0.md

@@ -6,14 +6,14 @@
 - {func}`~scanpy.pp.sample` supports both upsampling and downsampling of observations and variables. {func}`~scanpy.pp.subsample` is now deprecated. {smaller}`G Eraslan & P Angerer` ({pr}`943`)
 - Add `layer` argument to {func}`scanpy.tl.score_genes` and {func}`scanpy.tl.score_genes_cell_cycle` {smaller}`L Zappia` ({pr}`2921`)
 - Prevent `raw` conflict with `layer` in {func}`~scanpy.tl.score_genes` {smaller}`S Dicks` ({pr}`3155`)
-- Add support for `median` as an aggregation function to the `Aggregation` class in `scanpy.get._aggregated.py`. This allows for median-based aggregation of data (e.g., pseudobulk), complementing existing methods like mean- and sum-based aggregation {smaller}`M Dehkordi (Farhad)` ({pr}`3180`)
+- Add support for `median` as an aggregation function to {func}`~scanpy.get.aggregate`. This allows for median-based aggregation of data (e.g., pseudobulk), complementing existing methods like mean- and sum-based aggregation {smaller}`M Dehkordi (Farhad)` ({pr}`3180`)
 - Add `key_added` argument to {func}`~scanpy.pp.pca`, {func}`~scanpy.tl.tsne` and {func}`~scanpy.tl.umap` {smaller}`P Angerer` ({pr}`3184`)
 - Support running {func}`scanpy.pp.pca` on sparse Dask arrays with the `'covariance_eigh'` solver {smaller}`P Angerer` ({pr}`3263`)
-- Use upstreamed {class}`~sklearn.decomposition.PCA` implementation for {class}`~scipy.sparse.csr_array` and {class}`~scipy.sparse.csr_matrix` (see {ref}`sklearn:changes_1_4`) {smaller}`P Angerer` ({pr}`3267`)
+- Use upstreamed {class}`~sklearn.decomposition.PCA` implementation for {class}`~scipy.sparse.csr_array` and {class}`~scipy.sparse.csr_matrix` (see scikit-learn {ref}`sklearn:changes_1_4`) {smaller}`P Angerer` ({pr}`3267`)
 - Add explicit support to {func}`scanpy.pp.pca` for `svd_solver='covariance_eigh'` {smaller}`P Angerer` ({pr}`3296`)
-- Add support {class}`dask.array.Array` to {func}`scanpy.pp.calculate_qc_metrics` {smaller}`I Gold` ({pr}`3307`)
+- Add support for {class}`dask.array.Array` to {func}`scanpy.pp.calculate_qc_metrics` {smaller}`I Gold` ({pr}`3307`)
 - Support `layer` parameter in {func}`scanpy.pl.highest_expr_genes` {smaller}`P Angerer` ({pr}`3324`)
-- Run numba functions single-threaded when called from inside of a ThreadPool {smaller}`P Angerer` ({pr}`3335`)
+- Run numba functions single-threaded when called from inside of a {class}`~multiprocessing.pool.ThreadPool` {smaller}`P Angerer` ({pr}`3335`)
 - Switch {func}`~scanpy.logging.print_header` and {func}`~scanpy.logging.print_versions` to {mod}`session_info2` {smaller}`P Angerer` ({pr}`3384`)
 - Add sampling probabilities/mask parameter `p` to {func}`~scanpy.pp.sample` {smaller}`P Angerer` ({pr}`3410`)
 

src/scanpy/preprocessing/_simple.py

@@ -885,7 +885,7 @@ def sample(
         Rows correspond to cells and columns to genes.
     fraction
         Sample to this `fraction` of the number of observations or variables.
-        (All of them, even if there are `0`s/`False`s in `p`.)
+        (All of them, even if there are `0`\\ s/`False`\\ s in `p`.)
         This can be larger than 1.0, if `replace=True`.
         See `axis` and `replace`.
     n