Update app.py

app.py

@@ -328,106 +328,35 @@ def compute_shap_difference(shap1_norm, shap2_norm):
     """Compute the SHAP difference between normalized sequences"""
     return shap2_norm - shap1_norm
 
-def plot_comparative_heatmap(shap_diff, seq1_length, seq2_length, title="SHAP Difference Heatmap"):
+def plot_comparative_heatmap(shap_diff, title="SHAP Difference Heatmap"):
     """
-    Plots a comparative heatmap of SHAP differences between two sequences.
-
-    Parameters
-    ----------
-    shap_diff     : 1D array of differences (Seq2 SHAP - Seq1 SHAP).
-                    Negative = Seq1 more human-like, Positive = Seq2 more human-like.
-    seq1_length   : int, length of sequence 1 (for labeling).
-    seq2_length   : int, length of sequence 2 (for labeling).
-    title         : str, figure title.
-
-    Figure Layout
-    -------------
-    - Bottom X-Axis: Relative positions in percent (0% to 100%).
-    - Top X-Axis   : Actual positions for both sequences (S1 and S2).
-    - Y-Axis       : Hidden (this is effectively a 1D heatmap).
-    - Colorbar     : Vertical, placed on the right.
-    
-    The final layout uses `tight_layout` with a rectangular constraint to ensure 
-    nothing overlaps while still providing clear labeling.
+    Plot heatmap using relative positions (0-100%)
     """
-    # Reshape the 1D differences into a 1 x N image
     heatmap_data = shap_diff.reshape(1, -1)
-    # Determine the symmetric range around zero
-    extent = max(abs(shap_diff.min()), abs(shap_diff.max()))
-    
-    # Create the figure (width x height in inches)
-    fig, ax = plt.subplots(figsize=(10, 3))
+    extent = max(abs(np.min(shap_diff)), abs(np.max(shap_diff)))
     
-    # Main heatmap
+    fig, ax = plt.subplots(figsize=(12, 1.8))
     cmap = get_zero_centered_cmap()
-    cax = ax.imshow(
-        heatmap_data, 
-        aspect='auto', 
-        cmap=cmap, 
-        vmin=-extent, 
-        vmax=extent
-    )
+    cax = ax.imshow(heatmap_data, aspect='auto', cmap=cmap, vmin=-extent, vmax=extent)
     
-    # Number of major ticks along x-axis
+    # Create percentage-based x-axis ticks
     num_ticks = 5
-    tick_positions = np.linspace(0, shap_diff.shape[0] - 1, num_ticks)
-    
-    # ----------------- Bottom Axis: Percentage ----------------- #
+    tick_positions = np.linspace(0, shap_diff.shape[0]-1, num_ticks)
+    tick_labels = [f"{int(x*100)}%" for x in np.linspace(0, 1, num_ticks)]
     ax.set_xticks(tick_positions)
-    ax.set_xticklabels(
-        [f"{int(x * 100)}%" for x in np.linspace(0, 1, num_ticks)], 
-        fontsize=9
-    )
-    ax.set_xlabel("Relative Position (%)", fontsize=10, labelpad=10)
-    
-    # ----------------- Top Axis: Actual Positions ----------------- #
-    ax_top = ax.twiny()
-    ax_top.set_xlim(ax.get_xlim())  # Match the bottom axis
-    
-    # Create position arrays for both sequences
-    seq1_positions = np.linspace(0, seq1_length, num_ticks)
-    seq2_positions = np.linspace(0, seq2_length, num_ticks)
-    
-    # Helper function to format large positions nicely
-    def format_position(x):
-        if x >= 1e6:
-            return f"{x / 1e6:.1f}M"
-        elif x >= 1e3:
-            return f"{int(x / 1e3)}K"
-        else:
-            return f"{int(x)}"
+    ax.set_xticklabels(tick_labels)
     
-    seq1_labels = [format_position(x) for x in seq1_positions]
-    seq2_labels = [format_position(x) for x in seq2_positions]
-    
-    ax_top.set_xticks(tick_positions)
-    # Each tick label shows the corresponding position in Seq1 and Seq2
-    ax_top.set_xticklabels(
-        [f"S1: {s1}\nS2: {s2}" for s1, s2 in zip(seq1_labels, seq2_labels)],
-        fontsize=9
-    )
-    ax_top.set_xlabel("Sequence Positions", fontsize=10, labelpad=15)
-    
-    # ----------------- Colorbar (Vertical, on the right) ----------------- #
-    # 'fraction' = thickness of colorbar, 'pad' = gap from the right edge
-    cbar = fig.colorbar(
-        cax, ax=ax, orientation='vertical', fraction=0.03, pad=0.07
-    )
-    cbar.set_label("SHAP Difference\n(Seq2 - Seq1)", fontsize=10, labelpad=5)
+    cbar = plt.colorbar(cax, orientation='horizontal', pad=0.25, aspect=40, shrink=0.8)
+    cbar.ax.tick_params(labelsize=8)
+    cbar.set_label('SHAP Difference (Seq2 - Seq1)', fontsize=9, labelpad=5)
     
-    # Hide the y-axis (not needed in a 1D heatmap)
     ax.set_yticks([])
-    
-    # Set a descriptive title
-    ax.set_title(title, fontsize=12, pad=10)
-    
-    # Adjust layout so everything fits without overlapping
-    # The rect parameter leaves space on the right for the colorbar
-    fig.tight_layout(rect=[0, 0, 0.9, 1])  # Adjust as necessary
+    ax.set_xlabel('Relative Position in Sequence', fontsize=10)
+    ax.set_title(title, pad=10)
+    plt.subplots_adjust(bottom=0.25, left=0.05, right=0.95)
     
     return fig
 
-
 def plot_shap_histogram(shap_array, title="SHAP Distribution", num_bins=30):
     """
     Plot histogram of SHAP values with configurable number of bins
@@ -608,8 +537,6 @@ def analyze_sequence_comparison(file1, file2, fasta1="", fasta2=""):
         # Generate visualizations
         heatmap_fig = plot_comparative_heatmap(
             shap_diff,
-            seq1_length=len1,
-            seq2_length=len2,
             title=f"SHAP Difference Heatmap (window: {smooth_window})"
         )
         heatmap_img = fig_to_image(heatmap_fig)