559 lines
22 KiB
Python
559 lines
22 KiB
Python
import streamlit as st
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import pandas as pd
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import plotly.express as px
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import plotly.graph_objects as go
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import networkx as nx
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from pathlib import Path
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st.set_page_config(page_title="Hetionet Dashboard", layout="wide")
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# Custom CSS
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st.markdown("""
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<style>
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.stTabs [data-baseweb="tab-list"] {
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gap: 24px;
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}
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.stTabs [data-baseweb="tab"] {
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height: 50px;
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padding-left: 20px;
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padding-right: 20px;
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}
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.stat-box {
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background: grey;
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padding: 20px;
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border-radius: 10px;
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color: white;
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text-align: center;
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margin: 10px 0;
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}
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</style>
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""", unsafe_allow_html=True)
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st.title("Hetionet Drug Analysis Dashboard")
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# Sidebar
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st.sidebar.header("Global Search")
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search_type = st.sidebar.selectbox("Search for:", ["Gene", "Disease", "Drug"])
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search_term = st.sidebar.text_input(f"Enter {search_type} name:")
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st.sidebar.markdown("---")
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st.sidebar.header("Navigation")
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page = st.sidebar.radio("Select Analysis:", [
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"Overview",
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"Hotspot Genes",
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"Drug Repurposing",
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"Polypharmacy Risk",
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"Symptom Triangle",
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"Super Drugs",
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"Drug Conflicts",
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"Network Graph",
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"Compare Drugs"
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])
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# Load data
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try:
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data_dir = Path("neo4j_csv")
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with st.spinner("Loading data..."):
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genes = pd.read_csv(data_dir / "nodes_Gene.csv")
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diseases = pd.read_csv(data_dir / "nodes_Disease.csv")
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repurposing = pd.read_csv(data_dir / "analysis_drug_repurposing.csv")
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polypharmacy = pd.read_csv(data_dir / "analysis_polypharmacy_risk.csv")
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symptom_triangle = pd.read_csv(data_dir / "analysis_symptom_triangle.csv")
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super_drugs = pd.read_csv(data_dir / "analysis_super_drugs.csv")
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# Try loading new files
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try:
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drug_conflicts = pd.read_csv(data_dir / "analysis_drug_conflicts.csv")
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network_nodes = pd.read_csv(data_dir / "network_nodes.csv")
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network_edges = pd.read_csv(data_dir / "network_edges.csv")
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except:
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drug_conflicts = None
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network_nodes = None
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network_edges = None
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# Global Search Results
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if search_term:
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st.sidebar.markdown("---")
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st.sidebar.subheader("Search Results")
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if search_type == "Gene":
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results = genes[genes['name'].str.contains(search_term, case=False, na=False)]
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if len(results) > 0:
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st.sidebar.success(f"Found {len(results)} genes")
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for _, row in results.head(5).iterrows():
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st.sidebar.write(f"**{row['name']}**: {row['num_diseases']} diseases")
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else:
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st.sidebar.warning("No genes found")
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elif search_type == "Disease":
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results = diseases[diseases['name'].str.contains(search_term, case=False, na=False)]
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if len(results) > 0:
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st.sidebar.success(f"Found {len(results)} diseases")
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for _, row in results.head(5).iterrows():
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st.sidebar.write(f"**{row['name']}**: {row['num_symptoms']} symptoms")
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else:
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st.sidebar.warning("No diseases found")
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elif search_type == "Drug":
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results = super_drugs[super_drugs['name'].str.contains(search_term, case=False, na=False)]
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if len(results) > 0:
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st.sidebar.success(f"Found {len(results)} drugs")
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for _, row in results.head(5).iterrows():
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st.sidebar.write(f"**{row['name']}**: Score {row['super_score']:.2f}")
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else:
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st.sidebar.warning("No drugs found")
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# OVERVIEW PAGE
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if page == "Overview":
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st.header("Dataset Overview")
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col1, col2, col3, col4 = st.columns(4)
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col1.metric("Total Genes", f"{len(genes):,}")
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col2.metric("Total Diseases", f"{len(diseases):,}")
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col3.metric("Repurposing Opportunities", f"{len(repurposing):,}")
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col4.metric("Analyzed Drugs", f"{len(super_drugs):,}")
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# STATISTICS BOXES
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st.markdown("---")
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st.subheader("Key Statistics")
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col1, col2, col3 = st.columns(3)
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with col1:
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avg_diseases_per_gene = genes[genes['num_diseases'] > 0]['num_diseases'].mean()
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st.markdown(f"""
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<div class="stat-box">
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<h3>avg. Diseases per Gene</h3>
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<h1>{avg_diseases_per_gene:.1f}</h1>
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<p>For genes with disease associations</p>
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</div>
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""", unsafe_allow_html=True)
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with col2:
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avg_symptoms_per_disease = diseases[diseases['num_symptoms'] > 0]['num_symptoms'].mean()
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st.markdown(f"""
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<div class="stat-box">
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<h3>avg. symptoms per disease</h3>
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<h1>{avg_symptoms_per_disease:.1f}</h1>
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<p>For diseases with documented symptoms</p>
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</div>
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""", unsafe_allow_html=True)
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with col3:
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avg_side_effects = polypharmacy['num_side_effects'].mean()
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st.markdown(f"""
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<div class="stat-box">
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<h3>avg. side effect per drug</h3>
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<h1>{avg_side_effects:.1f}</h1>
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<p>Across all analyzed compounds</p>
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</div>
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""", unsafe_allow_html=True)
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st.markdown("---")
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col1, col2 = st.columns(2)
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with col1:
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st.subheader("Top Genes by Disease Count")
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top_genes = genes.nlargest(10, 'num_diseases')[['name', 'num_diseases']]
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fig = px.bar(top_genes, x='name', y='num_diseases', color='num_diseases')
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fig.update_layout(
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showlegend=False,
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xaxis_title="Gene",
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yaxis_title="Number of Diseases"
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)
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# Enable chart export
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config = {'displayModeBar': True, 'displaylogo': False}
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st.plotly_chart(fig, use_container_width=True, config=config)
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csv = top_genes.to_csv(index=False).encode('utf-8')
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st.download_button("Download Data", csv, "top_genes.csv", "text/csv")
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with col2:
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st.subheader("Top Diseases by Symptom Count")
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top_diseases = diseases.nlargest(10, 'num_symptoms')[['name', 'num_symptoms']]
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fig = px.bar(top_diseases, x='name', y='num_symptoms', color='num_symptoms', color_continuous_scale='Reds')
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fig.update_layout(
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showlegend=False,
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xaxis_title="Disease",
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yaxis_title="Number of Symptoms"
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)
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config = {'displayModeBar': True, 'displaylogo': False}
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st.plotly_chart(fig, use_container_width=True, config=config)
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csv = top_diseases.to_csv(index=False).encode('utf-8')
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st.download_button("Download Data", csv, "top_diseases.csv", "text/csv")
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# HOTSPOT GENES PAGE
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elif page == "Hotspot Genes":
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st.header("🧬 Hotspot Genes - Most Disease Associations")
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col1, col2 = st.columns([3, 1])
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with col2:
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n_genes = st.slider("Number of genes:", 10, 50, 20)
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min_diseases = st.slider("Min diseases:", 0, 50, 0)
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with col1:
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filtered_genes = genes[genes['num_diseases'] >= min_diseases].nlargest(n_genes, 'num_diseases')
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fig = px.bar(
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filtered_genes,
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x='name',
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y='num_diseases',
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title=f'Top {n_genes} Genes (min {min_diseases} diseases)',
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color='num_diseases',
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color_continuous_scale='Viridis'
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)
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fig.update_layout(height=600, showlegend=False)
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config = {'displayModeBar': True, 'displaylogo': False}
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st.plotly_chart(fig, use_container_width=True, config=config)
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st.dataframe(filtered_genes[['name', 'num_diseases']].reset_index(drop=True), use_container_width=True)
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csv = filtered_genes.to_csv(index=False).encode('utf-8')
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st.download_button("Download Filtered Data", csv, "hotspot_genes.csv", "text/csv")
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# DRUG REPURPOSING PAGE
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elif page == "Drug Repurposing":
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st.header("Drug Repurposing Opportunities")
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col1, col2 = st.columns([2, 1])
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with col1:
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top_n = st.slider("Show top N opportunities:", 10, 50, 20)
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top_repurpose = repurposing.nlargest(top_n, 'shared_genes')
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fig = px.scatter(
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top_repurpose,
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x='disease',
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y='candidate_drug',
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size='shared_genes',
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color='shared_genes',
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title='Drug Repurposing Candidates',
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color_continuous_scale='Reds',
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height=600
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)
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fig.update_xaxes(tickangle=-45)
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config = {'displayModeBar': True, 'displaylogo': False}
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st.plotly_chart(fig, use_container_width=True, config=config)
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with col2:
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st.subheader("Filter by Disease")
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diseases_list = sorted(repurposing['disease'].unique())
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selected = st.selectbox("Select disease:", diseases_list)
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filtered = repurposing[repurposing['disease'] == selected].nlargest(10, 'shared_genes')
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st.dataframe(filtered[['candidate_drug', 'shared_genes']].reset_index(drop=True), height=400)
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csv = filtered.to_csv(index=False).encode('utf-8')
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st.download_button("Download", csv, f"repurposing_{selected}.csv", "text/csv")
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# POLYPHARMACY RISK PAGE
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elif page == "Polypharmacy Risk":
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st.header("Polypharmacy Risk Analysis")
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col1, col2 = st.columns([3, 1])
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with col2:
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top_n = st.slider("Number of drugs:", 10, 30, 20)
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min_risk = st.slider("Min risk score:", 0, 100, 0)
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with col1:
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filtered_risk = polypharmacy[polypharmacy['risk_score'] >= min_risk].nlargest(top_n, 'num_side_effects')
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fig = px.scatter(
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filtered_risk,
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x='num_diseases_treated',
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y='num_side_effects',
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size='risk_score',
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color='risk_score',
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hover_data=['name'],
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title='Drugs: Side Effects vs Diseases Treated',
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color_continuous_scale='Reds'
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)
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fig.update_layout(height=600)
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config = {'displayModeBar': True, 'displaylogo': False}
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st.plotly_chart(fig, use_container_width=True, config=config)
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st.dataframe(
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filtered_risk[['name', 'num_diseases_treated', 'num_side_effects', 'risk_score']].reset_index(drop=True),
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use_container_width=True
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)
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csv = filtered_risk.to_csv(index=False).encode('utf-8')
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st.download_button("Download Risk Data", csv, "polypharmacy_risk.csv", "text/csv")
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# SYMPTOM TRIANGLE PAGE
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elif page == "Symptom Triangle":
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st.header("Symptom-Disease-Drug Connections")
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top_n = st.slider("Number of symptoms:", 10, 30, 20)
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top_symptoms = symptom_triangle.nlargest(top_n, 'impact_score')
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fig = px.scatter(
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top_symptoms,
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x='num_diseases',
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y='num_treating_drugs',
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size='impact_score',
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color='drugs_with_side_effects',
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hover_data=['symptom'],
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title='Symptom Impact Analysis',
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color_continuous_scale='RdYlGn_r'
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)
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fig.update_layout(height=600)
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config = {'displayModeBar': True, 'displaylogo': False}
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st.plotly_chart(fig, use_container_width=True, config=config)
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st.dataframe(
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top_symptoms[['symptom', 'num_diseases', 'num_treating_drugs', 'drugs_with_side_effects', 'impact_score']].reset_index(drop=True),
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use_container_width=True
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)
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csv = top_symptoms.to_csv(index=False).encode('utf-8')
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st.download_button("Download Symptom Data", csv, "symptom_triangle.csv", "text/csv")
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# SUPER DRUGS PAGE
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elif page == "Super Drugs":
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st.header("Super-Drug Score (Best Benefit/Risk Ratio)")
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col1, col2 = st.columns([3, 1])
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with col2:
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top_n = st.slider("Number of drugs:", 10, 30, 20)
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min_score = st.slider("Min super score:", 0.0, 5.0, 0.0, 0.1)
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with col1:
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filtered_super = super_drugs[super_drugs['super_score'] >= min_score].nlargest(top_n, 'super_score')
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fig = px.scatter(
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filtered_super,
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x='num_diseases_treated',
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y='num_side_effects',
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size='super_score',
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color='super_score',
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hover_data=['name'],
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title='Super Drugs Analysis',
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color_continuous_scale='Viridis_r'
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)
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fig.update_layout(height=600)
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config = {'displayModeBar': True, 'displaylogo': False}
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st.plotly_chart(fig, use_container_width=True, config=config)
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st.dataframe(
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filtered_super[['name', 'num_diseases_treated', 'num_side_effects', 'super_score']].reset_index(drop=True),
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use_container_width=True
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)
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perfect = super_drugs[(super_drugs['num_side_effects'] == 0) & (super_drugs['num_diseases_treated'] > 0)]
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st.info(f"💎 Found {len(perfect)} drugs with ZERO documented side effects!")
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csv = filtered_super.to_csv(index=False).encode('utf-8')
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st.download_button("Download Super Drugs", csv, "super_drugs.csv", "text/csv")
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# DRUG CONFLICTS PAGE
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elif page == "Drug Conflicts":
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st.header("Drug Conflicts - Overlapping Side Effects")
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if drug_conflicts is not None and len(drug_conflicts) > 0:
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col1, col2 = st.columns([3, 1])
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with col2:
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top_n = st.slider("Number of conflicts:", 10, 50, 20)
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min_overlap = st.slider("Min shared side effects:", 0, 100, 10)
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with col1:
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filtered_conflicts = drug_conflicts[
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drug_conflicts['shared_side_effects'] >= min_overlap
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].nlargest(top_n, 'shared_side_effects')
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fig = px.scatter(
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filtered_conflicts,
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x='drug1_total_se',
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y='drug2_total_se',
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size='shared_side_effects',
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color='overlap_percentage',
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hover_data=['drug1', 'drug2', 'shared_side_effects'],
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title='Drug Pairs with Overlapping Side Effects',
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labels={'drug1_total_se': 'Drug 1 Total SE', 'drug2_total_se': 'Drug 2 Total SE'},
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color_continuous_scale='Reds'
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)
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fig.update_layout(height=600)
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config = {'displayModeBar': True, 'displaylogo': False}
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st.plotly_chart(fig, use_container_width=True, config=config)
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st.warning("These drug combinations may have compounded side effects!")
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st.dataframe(
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filtered_conflicts[['drug1', 'drug2', 'shared_side_effects', 'overlap_percentage']].reset_index(drop=True),
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use_container_width=True
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)
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csv = filtered_conflicts.to_csv(index=False).encode('utf-8')
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st.download_button("Download Conflicts", csv, "drug_conflicts.csv", "text/csv")
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else:
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st.warning("Drug conflicts data not available. Run the ETL script to generate this analysis.")
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# NETWORK GRAPH PAGE
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elif page == "Network Graph":
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st.header("Disease-Gene-Drug Network")
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if network_nodes is not None and network_edges is not None:
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st.info("Interactive network visualization showing connections between diseases, genes, and drugs")
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# Create networkx graph
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G = nx.Graph()
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# Add nodes
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for _, row in network_nodes.iterrows():
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G.add_node(row['id'], label=row['label'], type=row['type'])
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# Add edges
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for _, row in network_edges.iterrows():
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G.add_edge(row['source'], row['target'])
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# Create layout
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pos = nx.spring_layout(G, k=0.5, iterations=50)
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# Create edge trace
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edge_x = []
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edge_y = []
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for edge in G.edges():
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x0, y0 = pos[edge[0]]
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x1, y1 = pos[edge[1]]
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edge_x.extend([x0, x1, None])
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edge_y.extend([y0, y1, None])
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edge_trace = go.Scatter(
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x=edge_x, y=edge_y,
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line=dict(width=0.5, color='#888'),
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hoverinfo='none',
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mode='lines'
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)
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# Create node traces (separate by type for legend)
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node_traces = []
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color_map = {
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'Disease': '#ff4444',
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'Gene': '#4444ff',
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'Compound': '#44ff44'
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}
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for node_type, color in color_map.items():
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node_x = []
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node_y = []
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node_text = []
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for node in G.nodes():
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if G.nodes[node]['type'] == node_type:
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x, y = pos[node]
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node_x.append(x)
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node_y.append(y)
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node_text.append(f"{node_type}: {G.nodes[node]['label']}")
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if node_x:
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node_trace = go.Scatter(
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x=node_x, y=node_y,
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mode='markers',
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name=node_type,
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hoverinfo='text',
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text=node_text,
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marker=dict(
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color=color,
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size=12,
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line=dict(width=2, color='white')
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)
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)
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node_traces.append(node_trace)
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# Create figure
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fig = go.Figure(data=[edge_trace] + node_traces,
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layout=go.Layout(
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title='Disease-Gene-Drug Network',
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showlegend=True,
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hovermode='closest',
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margin=dict(b=0,l=0,r=0,t=40),
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xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
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yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
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|
height=700
|
|
))
|
|
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config = {'displayModeBar': True, 'displaylogo': False}
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st.plotly_chart(fig, use_container_width=True, config=config)
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|
|
|
col1, col2, col3 = st.columns(3)
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col1.metric("🔴 Diseases", len([n for n in G.nodes() if G.nodes[n]['type'] == 'Disease']))
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|
col2.metric("🔵 Genes", len([n for n in G.nodes() if G.nodes[n]['type'] == 'Gene']))
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|
col3.metric("🟢 Drugs", len([n for n in G.nodes() if G.nodes[n]['type'] == 'Compound']))
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else:
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|
st.warning("Network data not available. Run the ETL script to generate this visualization.")
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|
|
|
# COMPARE DRUGS PAGE
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elif page == "Compare Drugs 🆕":
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|
st.header("⚖️ Compare Drugs Side-by-Side")
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|
|
|
drug_names = sorted(super_drugs['name'].unique())
|
|
|
|
col1, col2 = st.columns(2)
|
|
|
|
with col1:
|
|
drug1 = st.selectbox("Select Drug 1:", drug_names, key='drug1')
|
|
|
|
with col2:
|
|
drug2 = st.selectbox("Select Drug 2:", drug_names, key='drug2')
|
|
|
|
if drug1 and drug2:
|
|
drug1_data = super_drugs[super_drugs['name'] == drug1].iloc[0]
|
|
drug2_data = super_drugs[super_drugs['name'] == drug2].iloc[0]
|
|
|
|
st.markdown("---")
|
|
|
|
col1, col2 = st.columns(2)
|
|
|
|
with col1:
|
|
st.subheader(f"{drug1}")
|
|
st.metric("Diseases Treated", int(drug1_data['num_diseases_treated']))
|
|
st.metric("Side Effects", int(drug1_data['num_side_effects']))
|
|
st.metric("Super Score", f"{drug1_data['super_score']:.2f}")
|
|
|
|
with col2:
|
|
st.subheader(f"{drug2}")
|
|
st.metric("Diseases Treated", int(drug2_data['num_diseases_treated']))
|
|
st.metric("Side Effects", int(drug2_data['num_side_effects']))
|
|
st.metric("Super Score", f"{drug2_data['super_score']:.2f}")
|
|
|
|
# Comparison chart
|
|
comparison_df = pd.DataFrame({
|
|
'Metric': ['Diseases Treated', 'Side Effects', 'Super Score'],
|
|
drug1: [drug1_data['num_diseases_treated'], drug1_data['num_side_effects'], drug1_data['super_score']],
|
|
drug2: [drug2_data['num_diseases_treated'], drug2_data['num_side_effects'], drug2_data['super_score']]
|
|
})
|
|
|
|
fig = px.bar(
|
|
comparison_df,
|
|
x='Metric',
|
|
y=[drug1, drug2],
|
|
barmode='group',
|
|
title='Side-by-Side Comparison'
|
|
)
|
|
config = {'displayModeBar': True, 'displaylogo': False}
|
|
st.plotly_chart(fig, use_container_width=True, config=config)
|
|
|
|
# Winner determination
|
|
st.markdown("---")
|
|
st.subheader("🏆 Recommendation")
|
|
|
|
if drug1_data['super_score'] > drug2_data['super_score']:
|
|
st.success(f"**{drug1}** has a better benefit/risk ratio (Super Score: {drug1_data['super_score']:.2f})")
|
|
elif drug2_data['super_score'] > drug1_data['super_score']:
|
|
st.success(f"**{drug2}** has a better benefit/risk ratio (Super Score: {drug2_data['super_score']:.2f})")
|
|
else:
|
|
st.info("Both drugs have the same Super Score")
|
|
|
|
except FileNotFoundError as e:
|
|
st.error(f"Could not find data files")
|
|
st.write("Please ensure you're running the dashboard from the correct directory with access to `neo4j_csv/` folder")
|
|
|
|
except Exception as e:
|
|
st.error(f"Error: {str(e)}")
|
|
st.exception(e) |