PR Reviews

pertpy/preprocessing/_guide_rna.py

@@ -13,7 +13,7 @@
 
 if TYPE_CHECKING:
     from anndata import AnnData
-    from matplotlib.axes import Axes
+    from matplotlib.pyplot import Figure
 
 
 class GuideAssignment:
@@ -113,13 +113,14 @@ def assign_to_max_guide(
     def plot_heatmap(
         self,
         adata: AnnData,
+        *,
         layer: str | None = None,
         order_by: np.ndarray | str | None = None,
         key_to_save_order: str = None,
         show: bool = True,
         return_fig: bool = False,
         **kwargs,
-    ) -> list[Axes]:
+    ) -> Figure | None:
         """Heatmap plotting of guide RNA expression matrix.
 
         Assuming guides have sparse expression, this function reorders cells
@@ -141,8 +142,8 @@ def plot_heatmap(
             kwargs: Are passed to sc.pl.heatmap.
 
         Returns:
-            If return_fig is True, returns a list of Axes. Alternatively you can pass save or show parameters as they will be passed to sc.pl.heatmap.
-            Order of cells in the y-axis will be saved on adata.obs[key_to_save_order] if provided.
+            If `return_fig` is `True`, returns the figure, otherwise `None`.
+            Order of cells in the y-axis will be saved on `adata.obs[key_to_save_order]` if provided.
 
         Examples:
             Each cell is assigned to gRNA that occurs at least 5 times in the respective cell, which is then
@@ -179,7 +180,7 @@ def plot_heatmap(
             adata.obs[key_to_save_order] = pd.Categorical(order)
 
         try:
-            axis_group = sc.pl.heatmap(
+            fig = sc.pl.heatmap(
                 adata[order, :],
                 var_names=adata.var.index.tolist(),
                 groupby=temp_col_name,
@@ -196,5 +197,5 @@ def plot_heatmap(
         if show:
             plt.show()
         if return_fig:
-            return axis_group
+            return fig
         return None

pertpy/tools/_augur.py

@@ -979,6 +979,7 @@ def predict_differential_prioritization(
     def plot_dp_scatter(
         self,
         results: pd.DataFrame,
+        *,
         top_n: int = None,
         ax: Axes = None,
         show: bool = True,
@@ -1050,6 +1051,7 @@ def plot_dp_scatter(
     def plot_important_features(
         self,
         data: dict[str, Any],
+        *,
         key: str = "augurpy_results",
         top_n: int = 10,
         ax: Axes = None,
@@ -1117,11 +1119,12 @@ def plot_important_features(
     def plot_lollipop(
         self,
         data: dict[str, Any] | AnnData,
+        *,
         key: str = "augurpy_results",
         ax: Axes = None,
         show: bool = True,
         return_fig: bool = False,
-    ) -> Axes | Figure | None:
+    ) -> Figure | None:
         """Plot a lollipop plot of the mean augur values.
 
         Args:
@@ -1180,6 +1183,7 @@ def plot_scatterplot(
         self,
         results1: dict[str, Any],
         results2: dict[str, Any],
+        *,
         top_n: int = None,
         show: bool = True,
         return_fig: bool = False,

pertpy/tools/_cinemaot.py

@@ -651,6 +651,7 @@ def plot_vis_matching(
         control: str,
         de_label: str,
         source_label: str,
+        *,
         matching_rep: str = "ot",
         resolution: float = 0.5,
         normalize: str = "col",
@@ -677,6 +678,9 @@ def plot_vis_matching(
             {common_plot_args}
             **kwargs: Other parameters to input for seaborn.heatmap.
 
+        Returns:
+            If `return_fig` is `True`, returns the figure, otherwise `None`.
+
         Examples:
             >>> import pertpy as pt
             >>> adata = pt.dt.cinemaot_example()
@@ -716,10 +720,7 @@ def plot_vis_matching(
         if show:
             plt.show()
         if return_fig:
-            if ax is not None:
-                return ax
-            else:
-                return g
+            return g
         return None
 
 

pertpy/tools/_coda/_base_coda.py

@@ -1192,6 +1192,7 @@ def plot_stacked_barplot(  # pragma: no cover
         self,
         data: AnnData | MuData,
         feature_name: str,
+        *,
         modality_key: str = "coda",
         palette: ListedColormap | None = cm.tab20,
         show_legend: bool | None = True,
@@ -1200,7 +1201,7 @@ def plot_stacked_barplot(  # pragma: no cover
         dpi: int | None = 100,
         show: bool = True,
         return_fig: bool = False,
-    ) -> plt.Axes | Figure | None:
+    ) -> Figure | None:
         """Plots a stacked barplot for all levels of a covariate or all samples (if feature_name=="samples").
 
         Args:
@@ -1287,6 +1288,7 @@ def plot_stacked_barplot(  # pragma: no cover
     def plot_effects_barplot(  # pragma: no cover
         self,
         data: AnnData | MuData,
+        *,
         modality_key: str = "coda",
         covariates: str | list | None = None,
         parameter: Literal["log2-fold change", "Final Parameter", "Expected Sample"] = "log2-fold change",
@@ -1300,7 +1302,7 @@ def plot_effects_barplot(  # pragma: no cover
         dpi: int | None = 100,
         show: bool = True,
         return_fig: bool = False,
-    ) -> plt.Axes | plt.Figure | sns.axisgrid.FacetGrid | None:
+    ) -> Figure | None:
         """Barplot visualization for effects.
 
         The effect results for each covariate are shown as a group of barplots, with intra--group separation by cell types.
@@ -1322,8 +1324,7 @@ def plot_effects_barplot(  # pragma: no cover
             {common_plot_args}
 
         Returns:
-            Depending on `plot_facets`, returns a :class:`~matplotlib.axes.Axes` (`plot_facets = False`)
-            or :class:`~sns.axisgrid.FacetGrid` (`plot_facets = True`) object
+            If `return_fig` is `True`, returns the figure, otherwise `None`.
 
         Examples:
             >>> import pertpy as pt
@@ -1476,6 +1477,7 @@ def plot_boxplots(  # pragma: no cover
         self,
         data: AnnData | MuData,
         feature_name: str,
+        *,
         modality_key: str = "coda",
         y_scale: Literal["relative", "log", "log10", "count"] = "relative",
         plot_facets: bool = False,
@@ -1490,7 +1492,7 @@ def plot_boxplots(  # pragma: no cover
         dpi: int | None = 100,
         show: bool = True,
         return_fig: bool = False,
-    ) -> plt.Axes | plt.Figure | sns.axisgrid.FacetGrid | None:
+    ) -> Figure | None:
         """Grouped boxplot visualization.
 
          The cell counts for each cell type are shown as a group of boxplots
@@ -1515,8 +1517,7 @@ def plot_boxplots(  # pragma: no cover
             {common_plot_args}
 
         Returns:
-            Depending on `plot_facets`, returns a :class:`~matplotlib.axes.Axes` (`plot_facets = False`)
-            or :class:`~sns.axisgrid.FacetGrid` (`plot_facets = True`) object
+            If `return_fig` is `True`, returns the figure, otherwise `None`.
 
         Examples:
             >>> import pertpy as pt
@@ -1707,6 +1708,7 @@ def plot_boxplots(  # pragma: no cover
     def plot_rel_abundance_dispersion_plot(  # pragma: no cover
         self,
         data: AnnData | MuData,
+        *,
         modality_key: str = "coda",
         abundant_threshold: float | None = 0.9,
         default_color: str | None = "Grey",
@@ -1717,7 +1719,7 @@ def plot_rel_abundance_dispersion_plot(  # pragma: no cover
         ax: plt.Axes | None = None,
         show: bool = True,
         return_fig: bool = False,
-    ) -> plt.Axes | plt.Figure | None:
+    ) -> Figure | None:
         """Plots total variance of relative abundance versus minimum relative abundance of all cell types for determination of a reference cell type.
 
         If the count of the cell type is larger than 0 in more than abundant_threshold percent of all samples, the cell type will be marked in a different color.
@@ -1735,7 +1737,7 @@ def plot_rel_abundance_dispersion_plot(  # pragma: no cover
             {common_plot_args}
 
         Returns:
-            A :class:`~matplotlib.axes.Axes` object
+            If `return_fig` is `True`, returns the figure, otherwise `None`.
 
         Examples:
             >>> import pertpy as pt
@@ -1829,6 +1831,7 @@ def label_point(x, y, val, ax):
     def plot_draw_tree(  # pragma: no cover
         self,
         data: AnnData | MuData,
+        *,
         modality_key: str = "coda",
         tree: str = "tree",  # Also type ete3.Tree. Omitted due to import errors
         tight_text: bool | None = False,
@@ -1912,6 +1915,7 @@ def plot_draw_effects(  # pragma: no cover
         self,
         data: AnnData | MuData,
         covariate: str,
+        *,
         modality_key: str = "coda",
         tree: str = "tree",  # Also type ete3.Tree. Omitted due to import errors
         show_legend: bool | None = None,
@@ -2106,6 +2110,7 @@ def plot_effects_umap(  # pragma: no cover
         mdata: MuData,
         effect_name: str | list | None,
         cluster_key: str,
+        *,
         modality_key_1: str = "rna",
         modality_key_2: str = "coda",
         color_map: Colormap | str | None = None,
@@ -2114,7 +2119,7 @@ def plot_effects_umap(  # pragma: no cover
         show: bool = True,
         return_fig: bool = False,
         **kwargs,
-    ) -> plt.Axes | plt.Figure | None:
+    ) -> Figure | None:
         """Plot a UMAP visualization colored by effect strength.
 
         Effect results in .varm of aggregated sample-level AnnData (default is data['coda']) are assigned to cell-level AnnData
@@ -2134,7 +2139,7 @@ def plot_effects_umap(  # pragma: no cover
             **kwargs: All other keyword arguments are passed to `scanpy.plot.umap()`
 
         Returns:
-            If `return_fig==True` a :class:`~matplotlib.axes.Axes` or a list of it.
+            If `return_fig` is `True`, returns the figure, otherwise `None`.
 
         Examples:
             >>> import pertpy as pt

pertpy/tools/_dialogue.py

@@ -1067,11 +1067,12 @@ def plot_split_violins(
         adata: AnnData,
         split_key: str,
         celltype_key: str,
+        *,
         split_which: tuple[str, str] = None,
         mcp: str = "mcp_0",
         show: bool = True,
         return_fig: bool = False,
-    ) -> Axes | Figure | None:
+    ) -> Figure | None:
         """Plots split violin plots for a given MCP and split variable.
 
         Any cells with a value for split_key not in split_which are removed from the plot.
@@ -1122,10 +1123,11 @@ def plot_pairplot(
         celltype_key: str,
         color: str,
         sample_id: str,
+        *,
         mcp: str = "mcp_0",
         show: bool = True,
         return_fig: bool = False,
-    ) -> PairGrid | Figure | None:
+    ) -> Figure | None:
         """Generate a pairplot visualization for multi-cell perturbation (MCP) data.
 
         Computes the mean of a specified MCP feature (mcp) for each combination of sample and cell type,

pertpy/tools/_differential_gene_expression/_base.py

@@ -529,7 +529,7 @@ def plot_paired(
         show_legend: bool = True,
         size: int = 10,
         y_label: str = "expression",
-        pvalue_template=lambda x: f"unadj. p={x:.2e}, t-test",
+        pvalue_template=lambda x: f"p={x:.2e}",
         boxplot_properties=None,
         palette=None,
         show: bool = True,
@@ -594,7 +594,7 @@ def plot_paired(
             raise ValueError("The number of groups in the group_by column must be exactly 2 to enable paired testing")
 
         if var_names is None:
-            var_names = results_df.sort_values(pvalue_col, ascending=True).head(n_top_vars)[symbol_col].tolist()
+            var_names = results_df.head(n_top_vars)[symbol_col].tolist()
 
         adata = adata[:, var_names]
 

pertpy/tools/_enrichment.py

@@ -296,6 +296,7 @@ def gsea(
     def plot_dotplot(
         self,
         adata: AnnData,
+        *,
         targets: dict[str, dict[str, list[str]]] = None,
         source: Literal["chembl", "dgidb", "pharmgkb"] = "chembl",
         category_name: str = "interaction_type",
@@ -426,6 +427,7 @@ def plot_gsea(
         self,
         adata: AnnData,
         enrichment: dict[str, pd.DataFrame],
+        *,
         n: int = 10,
         key: str = "pertpy_enrichment_gsea",
         interactive_plot: bool = False,

pertpy/tools/_milo.py

@@ -718,6 +718,7 @@ def _graph_spatial_fdr(
     def plot_nhood_graph(
         self,
         mdata: MuData,
+        *,
         alpha: float = 0.1,
         min_logFC: float = 0,
         min_size: int = 10,
@@ -813,6 +814,7 @@ def plot_nhood(
         self,
         mdata: MuData,
         ix: int,
+        *,
         feature_key: str | None = "rna",
         basis: str = "X_umap",
         color_map: Colormap | str | None = None,
@@ -874,14 +876,15 @@ def plot_nhood(
     def plot_da_beeswarm(
         self,
         mdata: MuData,
+        *,
         feature_key: str | None = "rna",
         anno_col: str = "nhood_annotation",
         alpha: float = 0.1,
         subset_nhoods: list[str] = None,
         palette: str | Sequence[str] | dict[str, str] | None = None,
         show: bool = True,
         return_fig: bool = False,
-    ) -> Figure | Axes | None:
+    ) -> Figure | None:
         """Plot beeswarm plot of logFC against nhood labels
 
         Args:
@@ -894,6 +897,9 @@ def plot_da_beeswarm(
                      Defaults to pre-defined category colors for violinplots.
             {common_plot_args}
 
+        Returns:
+            If `return_fig` is `True`, returns the figure, otherwise `None`.
+
         Examples:
             >>> import pertpy as pt
             >>> import scanpy as sc
@@ -999,11 +1005,12 @@ def plot_nhood_counts_by_cond(
         self,
         mdata: MuData,
         test_var: str,
+        *,
         subset_nhoods: list[str] = None,
         log_counts: bool = False,
         show: bool = True,
         return_fig: bool = False,
-    ) -> Figure | Axes | None:
+    ) -> Figure | None:
         """Plot boxplot of cell numbers vs condition of interest.
 
         Args:
@@ -1012,6 +1019,9 @@ def plot_nhood_counts_by_cond(
             subset_nhoods: List of obs_names for neighbourhoods to include in plot. If None, plot all nhoods.
             log_counts: Whether to plot log1p of cell counts.
             {common_plot_args}
+
+        Returns:
+            If `return_fig` is `True`, returns the figure, otherwise `None`.
         """
         try:
             nhood_adata = mdata["milo"].T.copy()