added support for multiple targets in plot_colocalization_diff_heatmap

src/pixelator/plot/spatial_analysis_plots.py

@@ -117,8 +117,8 @@ def _get_top_marker_pairs(
 
 def plot_colocalization_diff_heatmap(
     colocalization_data: pd.DataFrame,
-    target: str,
     reference: str,
+    targets: str | list[str] | None = None,
     contrast_column: str = "sample",
     markers: Union[list, None] = None,
     n_top_marker_pairs: Union[int, None] = None,
@@ -130,8 +130,8 @@ def plot_colocalization_diff_heatmap(
     Example usage: plot_colocalization_diff_heatmap(pxl.colocalization, target:"stimulated", reference:"control", contrast_column="sample").
 
     :param colocalization_data: The colocalization data frame that can be found in a pixel variable "pxl" through pxl.colocalization. The data frame should contain the columns "marker_1", "marker_2", "pearson", "pearson_z", and the contrast_column.
-    :param target: The label for target components in the contrast_column.
     :param reference: The label for reference components in the contrast_column.
+    :param targets: label or list of labels for target components in the contrast_column.
     :param contrast_column: The column to use for the contrast. Defaults to "sample".
     :param markers: The markers to include in the heatmap. Defaults to None. At most only one of n_top_marker_pairs or markers should be provided.
     :param n_top_marker_pairs: The number of top marker pairs to include in the heatmap. Defaults to None. At most only one of n_top_marker_pairs or markers should be provided.
@@ -150,6 +150,18 @@ def plot_colocalization_diff_heatmap(
     else:
         value_col = "pearson"
 
+    if isinstance(targets, str):
+        targets = [targets]
+    elif targets is None:
+        targets = colocalization_data[contrast_column].unique()
+        targets = list(set(targets) - {reference})
+
+    if len(targets) > 5:
+        raise ValueError(
+            "Only up to 5 target components can be visualized. "
+            "Number of requested targets is {len(targets)}"
+        )
+
     if markers is not None:
         filter_mask = (colocalization_data["marker_1"].isin(markers)) & (
             colocalization_data["marker_2"].isin(markers)
@@ -158,45 +170,49 @@ def plot_colocalization_diff_heatmap(
 
     differential_colocalization = get_differential_colocalization(
         colocalization_data,
-        target=target,
+        targets=targets,
         reference=reference,
         contrast_column=contrast_column,
         use_z_score=use_z_score,
     )
-
-    differential_colocalization = differential_colocalization.fillna(0).reset_index()
-
-    if n_top_marker_pairs is not None:
-        top_markers = _get_top_marker_pairs(
-            differential_colocalization, n_top_marker_pairs, "median_difference"
+    if len(targets) == 1:
+        differential_colocalization = {targets[0]: differential_colocalization}
+    fig, axes = plt.subplots(1, len(targets), figsize=(5 * len(targets), 5))
+    for ind, target in enumerate(targets):
+        ax = axes if len(targets) == 1 else axes[ind]
+        target_diff = differential_colocalization[target]
+        target_diff = target_diff.fillna(0).reset_index()
+
+        if n_top_marker_pairs is not None:
+            top_markers = _get_top_marker_pairs(
+                target_diff, n_top_marker_pairs, "median_difference"
+            )
+        else:
+            top_markers = None
+
+        # Making the differential colocalization symmetric
+        target_diff = _make_colocalization_symmetric(target_diff, "median_difference")
+
+        pivoted_target_diff = _pivot_colocalization_data(
+            target_diff,
+            "median_difference",
+            markers=top_markers,
         )
-    else:
-        top_markers = None
 
-    # Making the differential colocalization symmetric
-    differential_colocalization = _make_colocalization_symmetric(
-        differential_colocalization, "median_difference"
-    )
-
-    pivoted_differential_colocalization = _pivot_colocalization_data(
-        differential_colocalization,
-        "median_difference",
-        markers=top_markers,
-    )
-
-    max_value = np.max(np.abs(pivoted_differential_colocalization.to_numpy().flatten()))
-    sns.clustermap(
-        pivoted_differential_colocalization,
-        yticklabels=True,
-        xticklabels=True,
-        method="complete",
-        linewidths=0.1,
-        vmin=-max_value,
-        vmax=max_value,
-        cmap=cmap,
-    )
+        max_value = np.max(np.abs(pivoted_target_diff.to_numpy().flatten()))
+        sns.clustermap(
+            pivoted_target_diff,
+            yticklabels=True,
+            xticklabels=True,
+            method="complete",
+            linewidths=0.1,
+            vmin=-max_value,
+            vmax=max_value,
+            cmap=cmap,
+            ax=ax,
+        )
 
-    return plt.gcf(), plt.gca()
+    return fig, axes
 
 
 def _add_top_marker_labels(
@@ -286,7 +302,7 @@ def plot_colocalization_diff_volcano(
 
     differential_colocalization = get_differential_colocalization(
         colocalization_data,
-        target=target,
+        targets=target,
         reference=reference,
         contrast_column=contrast_column,
         use_z_score=use_z_score,