pp.highly_variable_genes handles subset and inplace consistently (scv…

…erse#2757)

Co-authored-by: Philipp A <[email protected]>

docs/release-notes/1.9.7.md

@@ -5,3 +5,4 @@
 - Fix handling of numpy array palettes (e.g. after write-read cycle) {pr}`2734` {smaller}`P Angerer`
 - Specify correct version of `matplotlib` dependency {pr}`2733` {smaller}`P Fisher`
 - Fix {func}`scanpy.pl.violin` usage of `seaborn.catplot` {pr}`2739` {smaller}`E Roellin`
+- Fix {func}`scanpy.pp.highly_variable_genes` to handle the combinations of `inplace` and `subset` consistently {pr}`2757` {smaller}`E Roellin`

scanpy/preprocessing/_highly_variable_genes.py

@@ -146,7 +146,7 @@ def _highly_variable_genes_seurat_v3(
  df["highly_variable"] = False
  df.loc[sorted_index[: int(n_top_genes)], "highly_variable"] = True
 
- if inplace or subset:
+ if inplace:
  adata.uns["hvg"] = {"flavor": "seurat_v3"}
  logg.hint(
  "added\n"
@@ -172,6 +172,9 @@ def _highly_variable_genes_seurat_v3(
  else:
  if batch_key is None:
  df = df.drop(["highly_variable_nbatches"], axis=1)
+ if subset:
+ df = df.iloc[df.highly_variable.values, :]
+
  return df
 
 
@@ -544,7 +547,7 @@ def highly_variable_genes(
 
  logg.info(" finished", time=start)
 
- if inplace or subset:
+ if inplace:
  adata.uns["hvg"] = {"flavor": flavor}
  logg.hint(
  "added\n"
@@ -559,6 +562,7 @@ def highly_variable_genes(
  adata.var["dispersions_norm"] = df["dispersions_norm"].values.astype(
  "float32", copy=False
  )
+
  if batch_key is not None:
  adata.var["highly_variable_nbatches"] = df[
  "highly_variable_nbatches"
@@ -568,5 +572,9 @@ def highly_variable_genes(
  ].values
  if subset:
  adata._inplace_subset_var(df["highly_variable"].values)
+
  else:
+ if subset:
+ df = df.iloc[df.highly_variable.values, :]
+
  return df

scanpy/tests/test_highly_variable_genes.py

@@ -516,3 +516,38 @@ def test_cellranger_n_top_genes_warning():
  match="`n_top_genes` > number of normalized dispersions, returning all genes with normalized dispersions.",
  ):
  sc.pp.highly_variable_genes(adata, n_top_genes=1000, flavor="cell_ranger")
+
+
+@pytest.mark.parametrize("flavor", ["seurat", "cell_ranger"])
+@pytest.mark.parametrize("subset", [True, False])
+@pytest.mark.parametrize("inplace", [True, False])
+def test_highly_variable_genes_subset_inplace_consistency(
+ flavor,
+ subset,
+ inplace,
+):
+ adata = sc.datasets.blobs(n_observations=20, n_variables=80, random_state=0)
+ adata.X = np.abs(adata.X).astype(int)
+
+ if flavor == "seurat" or flavor == "cell_ranger":
+ sc.pp.normalize_total(adata, target_sum=1e4)
+ sc.pp.log1p(adata)
+
+ elif flavor == "seurat_v3":
+ pass
+
+ else:
+ raise ValueError(f"Unknown flavor {flavor}")
+
+ n_genes = adata.shape[1]
+
+ output_df = sc.pp.highly_variable_genes(
+ adata,
+ flavor=flavor,
+ n_top_genes=15,
+ subset=subset,
+ inplace=inplace,
+ )
+
+ assert (output_df is None) == inplace
+ assert len(adata.var if inplace else output_df) == (15 if subset else n_genes)