Create omni_genomics.py

omni_genomics.py

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+import plotly.express as px
+import plotly.graph_objects as go
+import pandas as pd
+import numpy as np
+
+class OMNIGenomics:
+    def __init__(self):
+        pass
+
+    def plot_variant_density(self, vcf_df):
+        """
+        Creates a high-fidelity visualization of variant distribution 
+        across the chromosomal landscape.
+        """
+        if vcf_df.empty:
+            return go.Figure()
+
+        # Sort by Chromosome and Position
+        vcf_df = vcf_df.sort_values(["Chromosome", "Position"])
+
+        # Create the Density Map
+        fig = px.density_heatmap(
+            vcf_df,
+            x="Position",
+            y="Chromosome",
+            z="Risk_Score",
+            histfunc="avg",
+            color_continuous_scale="Viridis",
+            title="Chromosomal Variant Density & Risk Distribution",
+            labels={'Risk_Score': 'Impact Level'},
+            nbinsx=100,
+            nbinsy=22
+        )
+
+        # Apply Stealth/V9 Styling
+        fig.update_layout(
+            paper_bgcolor='rgba(0,0,0,0)',
+            plot_bgcolor='rgba(0,0,0,0)',
+            font_color="#e0e0e0",
+            xaxis=dict(showgrid=False, zeroline=False),
+            yaxis=dict(showgrid=False, zeroline=False, autorange="reversed"),
+            coloraxis_colorbar=dict(title="Risk Score")
+        )
+        
+        return fig
+
+    def plot_pharmacogenomic_markers(self, markers):
+        """
+        Visualizes specific drug-gene interaction alerts.
+        """
+        # Simulated data if none provided
+        if not markers:
+            markers = {'CYP2C19': 0.9, 'VKORC1': 0.7, 'SLCO1B1': 0.4}
+            
+        fig = px.bar(
+            x=list(markers.keys()),
+            y=list(markers.values()),
+            color=list(markers.values()),
+            color_continuous_scale="Reds",
+            title="Pharmacogenomic Toxicity/Efficacy Markers",
+            labels={'x': 'Gene', 'y': 'Alert Intensity'}
+        )
+        
+        fig.update_layout(
+            paper_bgcolor='rgba(0,0,0,0)',
+            plot_bgcolor='rgba(0,0,0,0)',
+            font_color="#e0e0e0"
+        )
+        
+        return fig