Added kw args to all plots

CHANGELOG.md

@@ -16,7 +16,7 @@ and this project adheres to [Semantic Versioning][].
 ### Changes
 
 - `tl.rankby_obsm` now accepts `AnnData.obs` column names specified in the `obs_keys` argument
-- `pl.source_targets` now accepts extra arguments through `kw_scatter` and its `top` can be `None`, not displaying any label
+- Most plotting functions now accept extra arguments through `kw_` arguments.
 - p-values now are corrected using a custom numba-optimized version of `scipy.stats.false_discovery_control` called `_fdr_bh_axis1_numba`
 
 ## 2.1.1

src/decoupler/pl/_barplot.py

@@ -39,6 +39,7 @@ def barplot(
     vmin: float | None = None,
     vcenter: float | None = 0,
     vmax: float | None = None,
+    kw_barplot: dict | None = None,
     **kwargs,
 ) -> None | Figure:
     """
@@ -57,6 +58,8 @@ def barplot(
     %(vmin)s
     %(vcenter)s
     %(vmax)s
+    kw_barplot
+        Keyword arguments passed to ``seaborn.barplot``.
     %(plot)s
 
     Example
@@ -78,6 +81,8 @@ def barplot(
     assert isinstance(name, str) and name in data.index, "name must be str and in data.index"
     assert isinstance(top, int) and top > 0, "top must be int and > 0"
     assert isinstance(vertical, bool), "vertical must be bool"
+    if kw_barplot is None:
+        kw_barplot = {}
     # Process df
     df = data.loc[[name]]
     df.index.name = None
@@ -93,7 +98,7 @@ def barplot(
     # Instance
     bp = Plotter(**kwargs)
     # Plot
-    sns.barplot(data=df, x=x, y=y, ax=bp.ax)
+    sns.barplot(data=df, x=x, y=y, ax=bp.ax, **kw_barplot)
     if not vertical:
         sizes = np.array([bar.get_width() for bar in bp.ax.containers[0]])
         bp.ax.set_xlabel("Score")

src/decoupler/pl/_dotplot.py

@@ -19,6 +19,7 @@ def dotplot(
     scale: int | float = 0.15,
     cmap: str = "RdBu_r",
     vcenter: int | float | None = None,
+    kw_scatter: dict | None = None,
     **kwargs,
 ) -> None | Figure:
     """
@@ -41,6 +42,8 @@ def dotplot(
         Scale of the dots.
     %(cmap)s
     %(vcenter)s
+    kw_scatter
+        Keyword arguments passed to ``matplotlib.pyplot.scatter``.
     %(plot)s
 
     Example
@@ -77,6 +80,8 @@ def dotplot(
     assert isinstance(top, int | float) and top > 0, "top must be numerical and > 0"
     assert isinstance(scale, int | float), "scale must be numerical"
     assert isinstance(vcenter, int | float) or vcenter is None, "vcenter must be numeric or None"
+    if kw_scatter is None:
+        kw_scatter = {}
     # Filter by top
     df = df.copy()
     df["abs_x_col"] = df[x].abs()
@@ -101,14 +106,7 @@ def dotplot(
         norm = TwoSlopeNorm(vmin=None, vcenter=vcenter, vmax=None)
     else:
         norm = None
-    scatter = bp.ax.scatter(
-        x=x_vals,
-        y=y_vals,
-        c=c_vals,
-        s=ns,
-        cmap=cmap,
-        norm=norm,
-    )
+    scatter = bp.ax.scatter(x=x_vals, y=y_vals, c=c_vals, s=ns, cmap=cmap, norm=norm, **kw_scatter)
     bp.ax.set_axisbelow(True)
     bp.ax.set_xlabel(x)
     # Add legend

src/decoupler/pl/_filter_by_expr.py

@@ -19,6 +19,7 @@ def filter_by_expr(
     large_n: int = 10,
     min_prop: float = 0.7,
     cmap: str = "viridis",
+    kw_histplot: dict | None = None,
     **kwargs,
 ) -> None | Figure:
     """
@@ -34,6 +35,8 @@ def filter_by_expr(
     %(min_total_count)s
     %(large_n)s
     %(min_prop_expr)s
+    kw_histplot
+        Keyword arguments passed to ``seaborn.histplot``.
     %(plot)s
 
     Example
@@ -50,6 +53,8 @@ def filter_by_expr(
     X, _, _ = extract(adata, empty=False)
     isbacked = isinstance(X, tuple)
     assert not isbacked, "adata is in backed mode, reload adata without backed='r'"
+    if kw_histplot is None:
+        kw_histplot = {}
     obs = adata.obs
     # Minimum sample size cutoff
     min_sample_size = _min_sample_size(
@@ -77,6 +82,7 @@ def filter_by_expr(
         cbar_kws={"shrink": 0.75, "label": "Number of genes"},
         discrete=(False, True),
         ax=bp.ax,
+        **kw_histplot,
     )
     bp.ax.axhline(y=min_sample_size - 0.5, c="gray", ls="--")
     bp.ax.axvline(x=np.log10(min_total_count), c="gray", ls="--")

src/decoupler/pl/_filter_by_prop.py

@@ -9,7 +9,7 @@
 
 @docs.dedent
 def filter_by_prop(
-    adata: AnnData, min_prop: float = 0.1, min_smpls: int = 2, log: bool = True, color="gray", **kwargs
+    adata: AnnData, min_prop: float = 0.1, min_smpls: int = 2, kw_hist: dict | None = None, **kwargs
 ) -> None | Figure:
     """
     Plot to help determining the thresholds of the ``decoupler.pp.filter_by_prop`` function.
@@ -19,10 +19,8 @@ def filter_by_prop(
     %(adata)s
     %(min_prop_prop)s
     %(min_smpls)s
-    log
-        Whether to log-scale the y axis.
-    color
-        Color to use in ``matplotlib.pyplot.hist``.
+    kw_hist
+        Keyword arguments passed to ``matplotlib.pyplot.hist``.
     %(plot)s
 
     Example
@@ -40,21 +38,20 @@ def filter_by_prop(
     assert "psbulk_props" in adata.layers.keys(), (
         "psbulk_props must be in adata.layers, use this function afer running decoupler.pp.pseudobulk"
     )
+    if kw_hist is None:
+        kw_hist = {}
+    kw_hist.setdefault("color", "gray")
+    kw_hist.setdefault("align", "left")
+    kw_hist.setdefault("rwidth", 0.95)
+    kw_hist.setdefault("log", True)
     props = adata.layers["psbulk_props"]
     if isinstance(props, pd.DataFrame):
         props = props.values
     nsmpls = np.sum(props >= min_prop, axis=0)
     # Instance
     bp = Plotter(**kwargs)
     # Plot
-    _ = bp.ax.hist(
-        nsmpls,
-        bins=range(min(nsmpls), max(nsmpls) + 2),
-        log=log,
-        color=color,
-        align="left",
-        rwidth=0.95,
-    )
+    _ = bp.ax.hist(nsmpls, bins=range(min(nsmpls), max(nsmpls) + 2), **kw_hist)
     bp.ax.axvline(x=min_smpls - 0.5, c="black", ls="--")
     bp.ax.set_xlabel("Samples (≥ min_prop)")
     bp.ax.set_ylabel("Number of genes")

src/decoupler/pl/_filter_samples.py

@@ -16,6 +16,7 @@ def filter_samples(
     log: bool = True,
     min_cells: int | float = 10,
     min_counts: int | float = 1000,
+    kw_scatterplot: dict | None = None,
     **kwargs,
 ) -> None | Figure:
     """
@@ -30,6 +31,8 @@ def filter_samples(
         If set, log10 transform the ``psbulk_n_cells`` and ``psbulk_counts`` columns during visualization.
     %(min_cells)s
     %(min_counts)s
+    kw_scatterplot
+        Keyword arguments passed to ``seaborn.scatterplot``.
     %(plot)s
 
     Example
@@ -56,6 +59,8 @@ def filter_samples(
         groupby = [groupby]
     if groupby:
         assert all(col in adata.obs for col in groupby), "columns in groupby must be in adata.obs"
+    if kw_scatterplot is None:
+        kw_scatterplot = {}
     # Extract obs
     df = adata.obs.copy()
     # Transform to log10
@@ -78,7 +83,7 @@ def filter_samples(
         for ax, grp in zip(axes, groupby, strict=False):
             ax.grid(zorder=0)
             ax.set_axisbelow(True)
-            sns.scatterplot(x="psbulk_cells", y="psbulk_counts", hue=grp, ax=ax, data=df, zorder=1)
+            sns.scatterplot(x="psbulk_cells", y="psbulk_counts", hue=grp, ax=ax, data=df, zorder=1, **kw_scatterplot)
             ax.legend(loc="center left", bbox_to_anchor=(1, 0.5), frameon=False, title=grp)
             ax.set_xlabel(label_x)
             ax.set_ylabel(label_y)
@@ -91,7 +96,7 @@ def filter_samples(
         bp = Plotter(**kwargs)
         bp.ax.grid(zorder=0)
         bp.ax.set_axisbelow(True)
-        sns.scatterplot(x="psbulk_cells", y="psbulk_counts", hue=groupby, ax=bp.ax, data=df, zorder=1)
+        sns.scatterplot(x="psbulk_cells", y="psbulk_counts", hue=groupby, ax=bp.ax, data=df, zorder=1, **kw_scatterplot)
         if groupby:
             bp.ax.legend(loc="center left", bbox_to_anchor=(1, 0.5), frameon=False, title=groupby)
         bp.ax.set_xlabel(label_x)

src/decoupler/pl/_network.py

@@ -179,17 +179,18 @@ def network(
     score: pd.DataFrame = None,
     sources: int | list | str = 5,
     targets: int | list | str = 10,
-    by_abs=True,
-    size_node=5,
-    size_label=2.5,
-    s_cmap="RdBu_r",
-    t_cmap="viridis",
-    vcenter=False,
-    c_pos_w="darkgreen",
-    c_neg_w="darkred",
-    s_label="Enrichment\nscore",
-    t_label="Gene\nexpression",
-    layout="kk",
+    by_abs: bool = True,
+    size_node: int = 5,
+    size_label: float | int = 2.5,
+    s_cmap: str = "RdBu_r",
+    t_cmap: str = "viridis",
+    vcenter: bool = False,
+    c_pos_w: str = "darkgreen",
+    c_neg_w: str = "darkred",
+    s_label: str = "Enrichment\nscore",
+    t_label: str = "Gene\nexpression",
+    layout: str = "kk",
+    kw_igraph: dict | None = None,
     **kwargs,
 ):
     """
@@ -228,6 +229,8 @@ def network(
         Label to place in the target colorbar.
     layout
         Layout to use to order the nodes. Check ``igraph`` documentation for more options.
+    kw_igraph
+        Keyword arguments passed to ``igraph.plot``.
     %(plot)s
 
     Example
@@ -249,6 +252,8 @@ def network(
         data = pd.DataFrame(np.ones((1, trgs.size)), index=["0"], columns=trgs)
         s_cmap = "white"
         t_cmap = "white"
+    if kw_igraph is None:
+        kw_igraph = {}
     # Filter
     fdata, fscore, fnet = _filter(
         data=data,
@@ -283,14 +288,9 @@ def network(
     ax2 = bp.fig.add_subplot(gs[-1, 0])
     ax3 = bp.fig.add_subplot(gs[-1, 1])
     ax4 = bp.fig.add_subplot(gs[-1, -1])
-    ig.plot(
-        g,
-        target=ax1,
-        layout=layout,
-        vertex_size=(size_node * bp.dpi) / (bp.figsize[0] * bp.figsize[0]),
-        vertex_size_label=(size_label * bp.dpi) / (bp.figsize[0] * bp.figsize[0]),
-        bbox_inches="tight",
-    )
+    kw_igraph.setdefault("layout", layout)
+    kw_igraph.setdefault("vertex_size", (size_node * bp.dpi) / (bp.figsize[0] * bp.figsize[0]))
+    ig.plot(g, target=ax1, bbox_inches="tight", **kw_igraph)
     if is_cmap:
         sm = matplotlib.cm.ScalarMappable(norm=s_norm, cmap=s_cmap)
         bp.fig.colorbar(sm, cax=ax2, orientation="horizontal", label=s_label)

src/decoupler/pl/_volcano.py

@@ -23,6 +23,7 @@ def volcano(
     color_pos: str = "#D62728",
     color_neg: str = "#1F77B4",
     color_null: str = "gray",
+    kw_scatter: dict | None = None,
     **kwargs,
 ) -> None | Figure:
     """
@@ -54,6 +55,8 @@ def volcano(
         Color to plot significant negative features.
     color_null
         Color to plot rest of the genes.
+    kw_scatter
+        Keyword arguments passed to ``matplotlib.pyplot.scatter``.
     %(plot)s
 
     Example
@@ -85,6 +88,8 @@ def volcano(
     assert isinstance(color_pos, str), "color_pos must be str"
     assert isinstance(color_neg, str), "color_neg must be str"
     assert isinstance(color_null, str), "color_null must be str"
+    if kw_scatter is None:
+        kw_scatter = {}
     # Instance
     bp = Plotter(**kwargs)
     # Transform thr_sign
@@ -111,7 +116,7 @@ def volcano(
     df.loc[up_msk, "weight"] = color_pos
     df.loc[dw_msk, "weight"] = color_neg
     # Plot
-    df.plot.scatter(x="stat", y="pval", c="weight", sharex=False, ax=bp.ax)
+    df.plot.scatter(x="stat", y="pval", c="weight", sharex=False, ax=bp.ax, **kw_scatter)
     bp.ax.set_axisbelow(True)
     # Draw thr lines
     bp.ax.axvline(x=thr_stat, linestyle="--", color="black")

tests/pl/test_barplot.py

@@ -34,6 +34,6 @@ def test_barplot(
     vertical,
     vcenter,
 ):
-    fig = dc.pl.barplot(data=df, name=name, top=top, vertical=vertical, return_fig=True)
+    fig = dc.pl.barplot(data=df, name=name, top=top, vertical=vertical, return_fig=True, kw_barplot={"alpha": 0.5})
     assert isinstance(fig, Figure)
     plt.close(fig)

tests/pl/test_dotplot.py

@@ -25,6 +25,15 @@ def test_dotplot(
     df,
     vcenter,
 ):
-    fig = dc.pl.dotplot(df=df, x="x", y="y", c="c", s="s", vcenter=vcenter, return_fig=True)
+    fig = dc.pl.dotplot(
+        df=df,
+        x="x",
+        y="y",
+        c="c",
+        s="s",
+        vcenter=vcenter,
+        return_fig=True,
+        kw_scatter={"alpha": 0.5},
+    )
     assert isinstance(fig, Figure)
     plt.close(fig)

tests/pl/test_filter_by_expr.py

@@ -7,6 +7,6 @@
 def test_filter_by_expr(
     pdata,
 ):
-    fig = dc.pl.filter_by_expr(adata=pdata, return_fig=True)
+    fig = dc.pl.filter_by_expr(adata=pdata, return_fig=True, kw_histplot={"alpha": 0.5})
     assert isinstance(fig, Figure)
     plt.close(fig)

tests/pl/test_filter_by_prop.py

@@ -7,6 +7,6 @@
 def test_filter_by_prop(
     pdata,
 ):
-    fig = dc.pl.filter_by_prop(adata=pdata, return_fig=True)
+    fig = dc.pl.filter_by_prop(adata=pdata, return_fig=True, kw_hist={"alpha": 0.5})
     assert isinstance(fig, Figure)
     plt.close(fig)

tests/pl/test_filter_samples.py

@@ -18,6 +18,6 @@ def test_filter_samples(
     groupby,
     log,
 ):
-    fig = dc.pl.filter_samples(adata=pdata, groupby=groupby, log=log, return_fig=True)
+    fig = dc.pl.filter_samples(adata=pdata, groupby=groupby, log=log, return_fig=True, kw_scatterplot={"alpha": 0.5})
     assert isinstance(fig, Figure)
     plt.close(fig)

tests/pl/test_network.py

@@ -70,6 +70,7 @@ def test_network(
         vcenter=vcenter,
         s_cmap=s_cmap,
         figsize=(5, 5),
+        kw_igraph={"vertex_color": "green"},
         return_fig=True,
     )
     assert isinstance(fig, Figure)