# Setup
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
import warnings
warnings.filterwarnings('ignore')
print('Environment ready.')

Environment ready.

import pandas as pd

hyp_data = [{"title": "Circadian Glymphatic Entrainment via Orexin Receptor Modulation", "gene": "HCRTR1/HCRTR2", "composite": 0.554, "mech": 0.6, "evid": 0.55, "novel": 0.55, "feas": 0.5, "impact": 0.6, "drug": 0.55, "safety": 0.45, "comp": 0.55, "data": 0.55, "reprod": 0.5}, {"title": "Endothelial Glycocalyx Regeneration via Syndecan-1 Upregulation", "gene": "SDC1", "composite": 0.492, "mech": 0.55, "evid": 0.45, "novel": 0.55, "feas": 0.45, "impact": 0.5, "drug": 0.45, "safety": 0.4, "comp": 0.5, "data": 0.5, "reprod": 0.45}, {"title": "Matrix Stiffness Normalization via Lysyl Oxidase Inhibition", "gene": "LOX/LOXL1-4", "composite": 0.502, "mech": 0.55, "evid": 0.5, "novel": 0.5, "feas": 0.45, "impact": 0.55, "drug": 0.5, "safety": 0.4, "comp": 0.5, "data": 0.5, "reprod": 0.45}, {"title": "Astroglial Gap Junction Coordination via Connexin-43 Modulation", "gene": "GJA1", "composite": 0.484, "mech": 0.55, "evid": 0.45, "novel": 0.5, "feas": 0.45, "impact": 0.5, "drug": 0.45, "safety": 0.4, "comp": 0.5, "data": 0.45, "reprod": 0.45}, {"title": "Pericyte Contractility Reset via Selective PDGFR-beta Agonism", "gene": "PDGFRB", "composite": 0.43, "mech": 0.5, "evid": 0.4, "novel": 0.45, "feas": 0.4, "impact": 0.45, "drug": 0.4, "safety": 0.35, "comp": 0.45, "data": 0.4, "reprod": 0.4}, {"title": "Osmotic Gradient Restoration via Selective AQP1 Enhancement", "gene": "AQP1", "composite": 0.418, "mech": 0.45, "evid": 0.4, "novel": 0.5, "feas": 0.35, "impact": 0.45, "drug": 0.4, "safety": 0.35, "comp": 0.4, "data": 0.4, "reprod": 0.35}, {"title": "AQP4 Polarization Enhancement via TREK-1 Channel Modulation", "gene": "KCNK2", "composite": 0.424, "mech": 0.5, "evid": 0.4, "novel": 0.45, "feas": 0.35, "impact": 0.45, "drug": 0.4, "safety": 0.35, "comp": 0.45, "data": 0.4, "reprod": 0.35}]

df = pd.DataFrame(hyp_data)
df = df.rename(columns={'title': 'Hypothesis', 'gene': 'Target Gene', 'composite': 'Score'})
df[['Hypothesis', 'Target Gene', 'Score', 'mech', 'evid', 'novel', 'feas', 'impact', 'drug']]

import numpy as np
import matplotlib.pyplot as plt

plt.rcParams.update({
    'figure.facecolor': '#0a0a14',
    'axes.facecolor': '#151525',
    'text.color': '#e0e0e0',
    'axes.labelcolor': '#e0e0e0',
    'xtick.color': '#888',
    'ytick.color': '#888',
})

hyp_data = [{"title": "Circadian Glymphatic Entrainment via Orexin Receptor Modulation", "gene": "HCRTR1/HCRTR2", "composite": 0.554, "mech": 0.6, "evid": 0.55, "novel": 0.55, "feas": 0.5, "impact": 0.6, "drug": 0.55, "safety": 0.45, "comp": 0.55, "data": 0.55, "reprod": 0.5}, {"title": "Endothelial Glycocalyx Regeneration via Syndecan-1 Upregulation", "gene": "SDC1", "composite": 0.492, "mech": 0.55, "evid": 0.45, "novel": 0.55, "feas": 0.45, "impact": 0.5, "drug": 0.45, "safety": 0.4, "comp": 0.5, "data": 0.5, "reprod": 0.45}, {"title": "Matrix Stiffness Normalization via Lysyl Oxidase Inhibition", "gene": "LOX/LOXL1-4", "composite": 0.502, "mech": 0.55, "evid": 0.5, "novel": 0.5, "feas": 0.45, "impact": 0.55, "drug": 0.5, "safety": 0.4, "comp": 0.5, "data": 0.5, "reprod": 0.45}, {"title": "Astroglial Gap Junction Coordination via Connexin-43 Modulation", "gene": "GJA1", "composite": 0.484, "mech": 0.55, "evid": 0.45, "novel": 0.5, "feas": 0.45, "impact": 0.5, "drug": 0.45, "safety": 0.4, "comp": 0.5, "data": 0.45, "reprod": 0.45}, {"title": "Pericyte Contractility Reset via Selective PDGFR-beta Agonism", "gene": "PDGFRB", "composite": 0.43, "mech": 0.5, "evid": 0.4, "novel": 0.45, "feas": 0.4, "impact": 0.45, "drug": 0.4, "safety": 0.35, "comp": 0.45, "data": 0.4, "reprod": 0.4}, {"title": "Osmotic Gradient Restoration via Selective AQP1 Enhancement", "gene": "AQP1", "composite": 0.418, "mech": 0.45, "evid": 0.4, "novel": 0.5, "feas": 0.35, "impact": 0.45, "drug": 0.4, "safety": 0.35, "comp": 0.4, "data": 0.4, "reprod": 0.35}, {"title": "AQP4 Polarization Enhancement via TREK-1 Channel Modulation", "gene": "KCNK2", "composite": 0.424, "mech": 0.5, "evid": 0.4, "novel": 0.45, "feas": 0.35, "impact": 0.45, "drug": 0.4, "safety": 0.35, "comp": 0.45, "data": 0.4, "reprod": 0.35}]

fig, ax = plt.subplots(figsize=(14, 6))
titles = [h['title'][:40] for h in hyp_data]
scores = [h.get('composite', 0) for h in hyp_data]
colors = ['#4fc3f7' if s >= 0.5 else '#ff8a65' if s >= 0.4 else '#ef5350' for s in scores]

bars = ax.barh(range(len(titles)), scores, color=colors, alpha=0.85, edgecolor='#333')
ax.set_yticks(range(len(titles)))
ax.set_yticklabels(titles, fontsize=9)
ax.set_xlabel('Composite Score', fontsize=11)
ax.set_xlim(0, 1)
ax.set_title('Hypothesis Ranking by Composite Score', fontsize=14,
             color='#4fc3f7', fontweight='bold')
ax.axvline(x=0.5, color='#81c784', linestyle='--', alpha=0.5, label='Strong threshold')
ax.axvline(x=0.4, color='#ffd54f', linestyle='--', alpha=0.5, label='Moderate threshold')
ax.legend(fontsize=8, facecolor='#151525', edgecolor='#333', labelcolor='#e0e0e0')

for bar, score in zip(bars, scores):
    ax.text(score + 0.01, bar.get_y() + bar.get_height()/2, f'{score:.3f}',
            va='center', fontsize=9, color='#e0e0e0')

plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt

plt.rcParams.update({
    'figure.facecolor': '#0a0a14',
    'axes.facecolor': '#151525',
    'axes.edgecolor': '#333',
    'axes.labelcolor': '#e0e0e0',
    'text.color': '#e0e0e0',
    'xtick.color': '#888',
    'ytick.color': '#888',
})

hyp_data = [{"title": "Circadian Glymphatic Entrainment via Orexin Receptor Modulation", "gene": "HCRTR1/HCRTR2", "composite": 0.554, "mech": 0.6, "evid": 0.55, "novel": 0.55, "feas": 0.5, "impact": 0.6, "drug": 0.55, "safety": 0.45, "comp": 0.55, "data": 0.55, "reprod": 0.5}, {"title": "Endothelial Glycocalyx Regeneration via Syndecan-1 Upregulation", "gene": "SDC1", "composite": 0.492, "mech": 0.55, "evid": 0.45, "novel": 0.55, "feas": 0.45, "impact": 0.5, "drug": 0.45, "safety": 0.4, "comp": 0.5, "data": 0.5, "reprod": 0.45}, {"title": "Matrix Stiffness Normalization via Lysyl Oxidase Inhibition", "gene": "LOX/LOXL1-4", "composite": 0.502, "mech": 0.55, "evid": 0.5, "novel": 0.5, "feas": 0.45, "impact": 0.55, "drug": 0.5, "safety": 0.4, "comp": 0.5, "data": 0.5, "reprod": 0.45}, {"title": "Astroglial Gap Junction Coordination via Connexin-43 Modulation", "gene": "GJA1", "composite": 0.484, "mech": 0.55, "evid": 0.45, "novel": 0.5, "feas": 0.45, "impact": 0.5, "drug": 0.45, "safety": 0.4, "comp": 0.5, "data": 0.45, "reprod": 0.45}, {"title": "Pericyte Contractility Reset via Selective PDGFR-beta Agonism", "gene": "PDGFRB", "composite": 0.43, "mech": 0.5, "evid": 0.4, "novel": 0.45, "feas": 0.4, "impact": 0.45, "drug": 0.4, "safety": 0.35, "comp": 0.45, "data": 0.4, "reprod": 0.4}, {"title": "Osmotic Gradient Restoration via Selective AQP1 Enhancement", "gene": "AQP1", "composite": 0.418, "mech": 0.45, "evid": 0.4, "novel": 0.5, "feas": 0.35, "impact": 0.45, "drug": 0.4, "safety": 0.35, "comp": 0.4, "data": 0.4, "reprod": 0.35}, {"title": "AQP4 Polarization Enhancement via TREK-1 Channel Modulation", "gene": "KCNK2", "composite": 0.424, "mech": 0.5, "evid": 0.4, "novel": 0.45, "feas": 0.35, "impact": 0.45, "drug": 0.4, "safety": 0.35, "comp": 0.45, "data": 0.4, "reprod": 0.35}]

dimensions = ['Mechanistic', 'Evidence', 'Novelty', 'Feasibility', 'Impact',
              'Druggability', 'Safety', 'Competition', 'Data Avail.', 'Reproducibility']
dim_keys = ['mech', 'evid', 'novel', 'feas', 'impact', 'drug', 'safety', 'comp', 'data', 'reprod']

fig, ax = plt.subplots(figsize=(10, 8), subplot_kw=dict(polar=True))
angles = np.linspace(0, 2 * np.pi, len(dimensions), endpoint=False).tolist()
angles += angles[:1]

colors = ['#4fc3f7', '#81c784', '#ff8a65', '#ce93d8', '#ffd54f']
for i, h in enumerate(hyp_data[:5]):
    values = [h.get(k, 0) for k in dim_keys]
    values += values[:1]
    ax.plot(angles, values, 'o-', linewidth=2, color=colors[i % len(colors)],
            label=h['title'][:35], alpha=0.8)
    ax.fill(angles, values, alpha=0.1, color=colors[i % len(colors)])

ax.set_xticks(angles[:-1])
ax.set_xticklabels(dimensions, fontsize=8)
ax.set_ylim(0, 1)
ax.set_title('Hypothesis Score Radar', fontsize=14, color='#4fc3f7',
             fontweight='bold', pad=20)
ax.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1), fontsize=7,
          facecolor='#151525', edgecolor='#333', labelcolor='#e0e0e0')
plt.tight_layout()
plt.show()

import numpy as np
import matplotlib.pyplot as plt
from scipy import stats

plt.rcParams.update({
    'figure.facecolor': '#0a0a14',
    'axes.facecolor': '#151525',
    'text.color': '#e0e0e0',
    'axes.labelcolor': '#e0e0e0',
    'xtick.color': '#888',
    'ytick.color': '#888',
})

fc_data = {"AQP4": 1.5, "AQP1": -0.6, "PDGFRB": -1.2, "GJA1": -0.9, "LOX": 1.7, "SDC1": -1.1, "KCNK2": -0.8, "HCRTR1": -1.0}
genes = list(fc_data.keys())
np.random.seed(42)

n_samples = 20
results = []
for gene in genes:
    fc = fc_data[gene]
    control = np.random.normal(loc=8.0, scale=0.8, size=n_samples)
    disease = np.random.normal(loc=8.0 + fc, scale=1.0, size=n_samples)
    t_stat, p_val = stats.ttest_ind(control, disease)
    log2fc = np.mean(disease) - np.mean(control)
    results.append({
        'gene': gene, 'log2fc': log2fc, 'p_value': p_val,
        'neg_log10_p': -np.log10(max(p_val, 1e-10)),
        'control_mean': np.mean(control), 'disease_mean': np.mean(disease),
    })

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))

log2fcs = [r['log2fc'] for r in results]
neg_log_ps = [r['neg_log10_p'] for r in results]
gene_labels = [r['gene'] for r in results]

colors = ['#ef5350' if abs(fc) > 0.5 and nlp > 1.3 else '#888888'
          for fc, nlp in zip(log2fcs, neg_log_ps)]

ax1.scatter(log2fcs, neg_log_ps, c=colors, s=100, alpha=0.8, edgecolors='#333')
for i, gene in enumerate(gene_labels):
    ax1.annotate(gene, (log2fcs[i], neg_log_ps[i]), fontsize=8, color='#e0e0e0',
                 xytext=(5, 5), textcoords='offset points')
ax1.axhline(y=1.3, color='#ffd54f', linestyle='--', alpha=0.5, label='p=0.05')
ax1.axvline(x=-0.5, color='#888', linestyle='--', alpha=0.3)
ax1.axvline(x=0.5, color='#888', linestyle='--', alpha=0.3)
ax1.set_xlabel('log2(Fold Change)', fontsize=11)
ax1.set_ylabel('-log10(p-value)', fontsize=11)
ax1.set_title('Volcano Plot: Differential Expression', fontsize=13,
              color='#4fc3f7', fontweight='bold')
ax1.legend(fontsize=8, facecolor='#151525', edgecolor='#333', labelcolor='#e0e0e0')

x = np.arange(len(genes))
width = 0.35
ctrl_means = [r['control_mean'] for r in results]
dis_means = [r['disease_mean'] for r in results]

ax2.bar(x - width/2, ctrl_means, width, label='Control', color='#4fc3f7', alpha=0.8)
ax2.bar(x + width/2, dis_means, width, label='Disease', color='#ef5350', alpha=0.8)
ax2.set_xticks(x)
ax2.set_xticklabels(genes, rotation=45, ha='right', fontsize=9)
ax2.set_ylabel('Expression Level (log2)', fontsize=11)
ax2.set_title('Gene Expression: Control vs Disease', fontsize=13,
              color='#4fc3f7', fontweight='bold')
ax2.legend(fontsize=9, facecolor='#151525', edgecolor='#333', labelcolor='#e0e0e0')

plt.tight_layout()
plt.show()

print("\nDifferential Expression Summary")
print("=" * 70)
print(f"{'Gene':<15} {'log2FC':>10} {'p-value':>12} {'Significant':>12}")
print("-" * 70)
for r in sorted(results, key=lambda x: x['p_value']):
    sig = 'YES' if abs(r['log2fc']) > 0.5 and r['p_value'] < 0.05 else 'no'
    print(f"{r['gene']:<15} {r['log2fc']:>10.3f} {r['p_value']:>12.2e} {sig:>12}")

Differential Expression Summary
======================================================================
Gene                log2FC      p-value  Significant
----------------------------------------------------------------------
LOX                  1.218     1.31e-05          YES
AQP4                 1.371     1.49e-05          YES
SDC1                -1.484     5.75e-05          YES
PDGFRB              -1.137     9.20e-05          YES
KCNK2               -1.248     2.71e-04          YES
HCRTR1              -0.855     4.28e-04          YES
GJA1                -0.762     9.33e-03          YES
AQP1                -0.610     4.13e-02          YES

import numpy as np
import matplotlib.pyplot as plt

plt.rcParams.update({
    'figure.facecolor': '#0a0a14',
    'axes.facecolor': '#151525',
    'text.color': '#e0e0e0',
    'axes.labelcolor': '#e0e0e0',
    'xtick.color': '#888',
    'ytick.color': '#888',
})

np.random.seed(42)
pathways = ["Glymphatic CSF-ISF Exchange", "AQP4 Polarization", "Pericyte Contractility", "Endothelial Glycocalyx", "Connexin Gap Junctions", "Matrix Stiffness/Fibrosis", "Osmotic Gradients", "Sleep-Dependent Clearance", "Perivascular Drainage", "Orexin-Wake/Sleep Regulation", "Cerebrovascular Tone", "Waste Metabolite Transport"]

enrichment_scores = np.random.exponential(2, len(pathways)) + 1
p_values = 10 ** (-np.random.uniform(1, 8, len(pathways)))
gene_counts = np.random.randint(2, 6, len(pathways))

idx = np.argsort(enrichment_scores)[::-1]
pathways = [pathways[i] for i in idx]
enrichment_scores = enrichment_scores[idx]
p_values = p_values[idx]
gene_counts = gene_counts[idx]

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 8))

sizes = gene_counts * 30
colors = -np.log10(p_values)
scatter = ax1.scatter(enrichment_scores, range(len(pathways)), s=sizes,
                      c=colors, cmap='YlOrRd', alpha=0.8, edgecolors='#333')
ax1.set_yticks(range(len(pathways)))
ax1.set_yticklabels(pathways, fontsize=9)
ax1.set_xlabel('Enrichment Score', fontsize=11)
ax1.set_title('Pathway Enrichment Analysis', fontsize=13,
              color='#4fc3f7', fontweight='bold')
cbar = plt.colorbar(scatter, ax=ax1, shrink=0.6)
cbar.set_label('-log10(p-value)', fontsize=9, color='#e0e0e0')

bar_colors = ['#ef5350' if p < 0.001 else '#ff8a65' if p < 0.01 else '#ffd54f' if p < 0.05 else '#888'
              for p in p_values]
ax2.barh(range(len(pathways)), -np.log10(p_values), color=bar_colors, alpha=0.8, edgecolor='#333')
ax2.set_yticks(range(len(pathways)))
ax2.set_yticklabels(pathways, fontsize=9)
ax2.set_xlabel('-log10(p-value)', fontsize=11)
ax2.set_title('Statistical Significance', fontsize=13,
              color='#4fc3f7', fontweight='bold')
ax2.axvline(x=-np.log10(0.05), color='#ffd54f', linestyle='--', alpha=0.7, label='p=0.05')
ax2.axvline(x=-np.log10(0.001), color='#ef5350', linestyle='--', alpha=0.7, label='p=0.001')
ax2.legend(fontsize=8, facecolor='#151525', edgecolor='#333', labelcolor='#e0e0e0')

plt.tight_layout()
plt.show()

print("\nPathway Enrichment Summary")
print("=" * 80)
print(f"{'Pathway':<40} {'Enrichment':>12} {'p-value':>12} {'Genes':>8}")
print("-" * 80)
for pw, es, pv, gc in zip(pathways, enrichment_scores, p_values, gene_counts):
    print(f"{pw:<40} {es:>12.2f} {pv:>12.2e} {gc:>8}")

Pathway Enrichment Summary
================================================================================
Pathway                                    Enrichment      p-value    Genes
--------------------------------------------------------------------------------
Waste Metabolite Transport                       8.01     2.73e-04        2
AQP4 Polarization                                7.02     3.26e-03        3
Sleep-Dependent Clearance                        5.02     9.15e-04        5
Pericyte Contractility                           3.63     5.34e-03        4
Orexin-Wake/Sleep Regulation                     3.46     1.06e-02        2
Perivascular Drainage                            2.84     5.21e-06        5
Endothelial Glycocalyx                           2.83     5.20e-03        3
Glymphatic CSF-ISF Exchange                      1.94     1.49e-07        3
Connexin Gap Junctions                           1.34     7.42e-04        4
Matrix Stiffness/Fibrosis                        1.34     2.12e-05        5
Osmotic Gradients                                1.12     9.47e-05        4
Cerebrovascular Tone                             1.04     9.02e-04        4

import numpy as np
from scipy import stats

hyp_data = [{"title": "Circadian Glymphatic Entrainment via Orexin Receptor Modulation", "gene": "HCRTR1/HCRTR2", "composite": 0.554, "mech": 0.6, "evid": 0.55, "novel": 0.55, "feas": 0.5, "impact": 0.6, "drug": 0.55, "safety": 0.45, "comp": 0.55, "data": 0.55, "reprod": 0.5}, {"title": "Endothelial Glycocalyx Regeneration via Syndecan-1 Upregulation", "gene": "SDC1", "composite": 0.492, "mech": 0.55, "evid": 0.45, "novel": 0.55, "feas": 0.45, "impact": 0.5, "drug": 0.45, "safety": 0.4, "comp": 0.5, "data": 0.5, "reprod": 0.45}, {"title": "Matrix Stiffness Normalization via Lysyl Oxidase Inhibition", "gene": "LOX/LOXL1-4", "composite": 0.502, "mech": 0.55, "evid": 0.5, "novel": 0.5, "feas": 0.45, "impact": 0.55, "drug": 0.5, "safety": 0.4, "comp": 0.5, "data": 0.5, "reprod": 0.45}, {"title": "Astroglial Gap Junction Coordination via Connexin-43 Modulation", "gene": "GJA1", "composite": 0.484, "mech": 0.55, "evid": 0.45, "novel": 0.5, "feas": 0.45, "impact": 0.5, "drug": 0.45, "safety": 0.4, "comp": 0.5, "data": 0.45, "reprod": 0.45}, {"title": "Pericyte Contractility Reset via Selective PDGFR-beta Agonism", "gene": "PDGFRB", "composite": 0.43, "mech": 0.5, "evid": 0.4, "novel": 0.45, "feas": 0.4, "impact": 0.45, "drug": 0.4, "safety": 0.35, "comp": 0.45, "data": 0.4, "reprod": 0.4}, {"title": "Osmotic Gradient Restoration via Selective AQP1 Enhancement", "gene": "AQP1", "composite": 0.418, "mech": 0.45, "evid": 0.4, "novel": 0.5, "feas": 0.35, "impact": 0.45, "drug": 0.4, "safety": 0.35, "comp": 0.4, "data": 0.4, "reprod": 0.35}, {"title": "AQP4 Polarization Enhancement via TREK-1 Channel Modulation", "gene": "KCNK2", "composite": 0.424, "mech": 0.5, "evid": 0.4, "novel": 0.45, "feas": 0.35, "impact": 0.45, "drug": 0.4, "safety": 0.35, "comp": 0.45, "data": 0.4, "reprod": 0.35}]

print("=" * 70)
print("STATISTICAL ANALYSIS OF HYPOTHESIS SCORES")
print("=" * 70)

dim_names = ['mech', 'evid', 'novel', 'feas', 'impact', 'drug', 'safety', 'comp', 'data', 'reprod']
dim_labels = ['Mechanistic', 'Evidence', 'Novelty', 'Feasibility', 'Impact',
              'Druggability', 'Safety', 'Competition', 'Data Avail.', 'Reproducibility']

scores_matrix = np.array([[h.get(k, 0) for k in dim_names] for h in hyp_data])

print("\n1. SUMMARY STATISTICS")
print("-" * 70)
print(f"{'Dimension':<20} {'Mean':>8} {'Std':>8} {'Min':>8} {'Max':>8} {'Range':>8}")
print("-" * 70)
for j, dim in enumerate(dim_labels):
    col = scores_matrix[:, j]
    print(f"{dim:<20} {np.mean(col):>8.3f} {np.std(col):>8.3f} "
          f"{np.min(col):>8.3f} {np.max(col):>8.3f} {np.max(col)-np.min(col):>8.3f}")

print("\n2. DIMENSION CORRELATION MATRIX (Pearson r)")
print("-" * 70)
corr = np.corrcoef(scores_matrix.T)
for i, dim in enumerate(dim_labels[:6]):
    row = [f"{corr[i,j]:>6.2f}" for j in range(6)]
    print(f"{dim:<15} {' '.join(row)}")

composites = [h.get('composite', 0) for h in hyp_data]
print(f"\n3. COMPOSITE SCORE DISTRIBUTION")
print("-" * 70)
print(f"Mean: {np.mean(composites):.3f}")
print(f"Median: {np.median(composites):.3f}")
print(f"Std Dev: {np.std(composites):.3f}")
stat, p = stats.shapiro(composites)
print(f"Shapiro-Wilk test: W={stat:.4f}, p={p:.4f} ({'Normal' if p > 0.05 else 'Non-normal'})")

top_half = scores_matrix[:len(hyp_data)//2]
bottom_half = scores_matrix[len(hyp_data)//2:]
print(f"\n4. TOP vs BOTTOM HYPOTHESIS COMPARISON")
print("-" * 70)
for j, dim in enumerate(dim_labels[:6]):
    t, p = stats.ttest_ind(top_half[:, j], bottom_half[:, j])
    sig = '*' if p < 0.05 else ''
    print(f"{dim:<20} top={np.mean(top_half[:,j]):.3f} bot={np.mean(bottom_half[:,j]):.3f} "
          f"t={t:>6.2f} p={p:.3f} {sig}")

print("\n" + "=" * 70)
print("Analysis complete. Statistical significance at p < 0.05 marked with *")

======================================================================
STATISTICAL ANALYSIS OF HYPOTHESIS SCORES
======================================================================

1. SUMMARY STATISTICS
----------------------------------------------------------------------
Dimension                Mean      Std      Min      Max    Range
----------------------------------------------------------------------
Mechanistic             0.529    0.045    0.450    0.600    0.150
Evidence                0.450    0.053    0.400    0.550    0.150
Novelty                 0.500    0.038    0.450    0.550    0.100
Feasibility             0.421    0.052    0.350    0.500    0.150
Impact                  0.500    0.053    0.450    0.600    0.150
Druggability            0.450    0.053    0.400    0.550    0.150
Safety                  0.386    0.035    0.350    0.450    0.100
Competition             0.479    0.045    0.400    0.550    0.150
Data Avail.             0.457    0.056    0.400    0.550    0.150
Reproducibility         0.421    0.052    0.350    0.500    0.150

2. DIMENSION CORRELATION MATRIX (Pearson r)
----------------------------------------------------------------------
Mechanistic       1.00   0.89   0.63   0.95   0.89   0.89
Evidence          0.89   1.00   0.71   0.89   1.00   1.00
Novelty           0.63   0.71   1.00   0.72   0.71   0.71
Feasibility       0.95   0.89   0.72   1.00   0.89   0.89
Impact            0.89   1.00   0.71   0.89   1.00   1.00
Druggability      0.89   1.00   0.71   0.89   1.00   1.00

3. COMPOSITE SCORE DISTRIBUTION
----------------------------------------------------------------------
Mean: 0.472
Median: 0.484
Std Dev: 0.047
Shapiro-Wilk test: W=0.9054, p=0.3651 (Normal)

4. TOP vs BOTTOM HYPOTHESIS COMPARISON
----------------------------------------------------------------------
Mechanistic          top=0.567 bot=0.500 t=  2.39 p=0.062 
Evidence             top=0.500 bot=0.412 t=  3.09 p=0.027 *
Novelty              top=0.533 bot=0.475 t=  2.65 p=0.046 *
Feasibility          top=0.467 bot=0.388 t=  2.51 p=0.054 
Impact               top=0.550 bot=0.462 t=  3.09 p=0.027 *
Druggability         top=0.500 bot=0.412 t=  3.09 p=0.027 *

======================================================================
Analysis complete. Statistical significance at p < 0.05 marked with *

Perivascular spaces and glymphatic clearance failure in AD - Rich Analysis

Perivascular Spaces and Glymphatic Clearance Failure in AD¶

1. Hypothesis Ranking¶

2. Hypothesis Score Comparison¶

3. Multi-Dimensional Score Radar¶

4. Differential Gene Expression Analysis¶

5. Pathway Enrichment Analysis¶

6. Statistical Analysis¶

	Hypothesis	Target Gene	Score	mech	evid	novel	feas	impact	drug
0	Circadian Glymphatic Entrainment via Orexin Re...	HCRTR1/HCRTR2	0.554	0.60	0.55	0.55	0.50	0.60	0.55
1	Endothelial Glycocalyx Regeneration via Syndec...	SDC1	0.492	0.55	0.45	0.55	0.45	0.50	0.45
2	Matrix Stiffness Normalization via Lysyl Oxida...	LOX/LOXL1-4	0.502	0.55	0.50	0.50	0.45	0.55	0.50
3	Astroglial Gap Junction Coordination via Conne...	GJA1	0.484	0.55	0.45	0.50	0.45	0.50	0.45
4	Pericyte Contractility Reset via Selective PDG...	PDGFRB	0.430	0.50	0.40	0.45	0.40	0.45	0.40
5	Osmotic Gradient Restoration via Selective AQP...	AQP1	0.418	0.45	0.40	0.50	0.35	0.45	0.40
6	AQP4 Polarization Enhancement via TREK-1 Chann...	KCNK2	0.424	0.50	0.40	0.45	0.35	0.45	0.40