# 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": "Blood-Brain Barrier SPM Shuttle System", "gene": "TFRC", "composite": 0.533, "mech": 0.6, "evid": 0.5, "novel": 0.6, "feas": 0.45, "impact": 0.55, "drug": 0.5, "safety": 0.45, "comp": 0.55, "data": 0.5, "reprod": 0.45}, {"title": "Senescent Microglia Resolution via Maresins-Senolytics", "gene": "BCL2L1", "composite": 0.521, "mech": 0.55, "evid": 0.5, "novel": 0.6, "feas": 0.45, "impact": 0.55, "drug": 0.5, "safety": 0.4, "comp": 0.55, "data": 0.5, "reprod": 0.45}, {"title": "Microglial Efferocytosis Enhancement via GPR32 Superagonists", "gene": "CMKLR1", "composite": 0.457, "mech": 0.5, "evid": 0.45, "novel": 0.5, "feas": 0.4, "impact": 0.5, "drug": 0.45, "safety": 0.4, "comp": 0.45, "data": 0.45, "reprod": 0.4}, {"title": "Circadian-Gated Maresin Biosynthesis Amplification", "gene": "ALOX12", "composite": 0.445, "mech": 0.5, "evid": 0.4, "novel": 0.55, "feas": 0.35, "impact": 0.5, "drug": 0.4, "safety": 0.4, "comp": 0.45, "data": 0.4, "reprod": 0.35}, {"title": "Oligodendrocyte Protectin D1 Mimetic for Myelin Resolution", "gene": "GPR37", "composite": 0.421, "mech": 0.5, "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": "Astrocytic Lipoxin A4 Pathway Restoration via ALOX15 Gene Therapy", "gene": "ALOX15", "composite": 0.429, "mech": 0.5, "evid": 0.4, "novel": 0.5, "feas": 0.35, "impact": 0.45, "drug": 0.4, "safety": 0.35, "comp": 0.45, "data": 0.4, "reprod": 0.35}, {"title": "Mitochondrial SPM Synthesis Platform Engineering", "gene": "ALOX5", "composite": 0.358, "mech": 0.4, "evid": 0.35, "novel": 0.45, "feas": 0.3, "impact": 0.4, "drug": 0.3, "safety": 0.3, "comp": 0.35, "data": 0.35, "reprod": 0.3}]

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": "Blood-Brain Barrier SPM Shuttle System", "gene": "TFRC", "composite": 0.533, "mech": 0.6, "evid": 0.5, "novel": 0.6, "feas": 0.45, "impact": 0.55, "drug": 0.5, "safety": 0.45, "comp": 0.55, "data": 0.5, "reprod": 0.45}, {"title": "Senescent Microglia Resolution via Maresins-Senolytics", "gene": "BCL2L1", "composite": 0.521, "mech": 0.55, "evid": 0.5, "novel": 0.6, "feas": 0.45, "impact": 0.55, "drug": 0.5, "safety": 0.4, "comp": 0.55, "data": 0.5, "reprod": 0.45}, {"title": "Microglial Efferocytosis Enhancement via GPR32 Superagonists", "gene": "CMKLR1", "composite": 0.457, "mech": 0.5, "evid": 0.45, "novel": 0.5, "feas": 0.4, "impact": 0.5, "drug": 0.45, "safety": 0.4, "comp": 0.45, "data": 0.45, "reprod": 0.4}, {"title": "Circadian-Gated Maresin Biosynthesis Amplification", "gene": "ALOX12", "composite": 0.445, "mech": 0.5, "evid": 0.4, "novel": 0.55, "feas": 0.35, "impact": 0.5, "drug": 0.4, "safety": 0.4, "comp": 0.45, "data": 0.4, "reprod": 0.35}, {"title": "Oligodendrocyte Protectin D1 Mimetic for Myelin Resolution", "gene": "GPR37", "composite": 0.421, "mech": 0.5, "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": "Astrocytic Lipoxin A4 Pathway Restoration via ALOX15 Gene Therapy", "gene": "ALOX15", "composite": 0.429, "mech": 0.5, "evid": 0.4, "novel": 0.5, "feas": 0.35, "impact": 0.45, "drug": 0.4, "safety": 0.35, "comp": 0.45, "data": 0.4, "reprod": 0.35}, {"title": "Mitochondrial SPM Synthesis Platform Engineering", "gene": "ALOX5", "composite": 0.358, "mech": 0.4, "evid": 0.35, "novel": 0.45, "feas": 0.3, "impact": 0.4, "drug": 0.3, "safety": 0.3, "comp": 0.35, "data": 0.35, "reprod": 0.3}]

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": "Blood-Brain Barrier SPM Shuttle System", "gene": "TFRC", "composite": 0.533, "mech": 0.6, "evid": 0.5, "novel": 0.6, "feas": 0.45, "impact": 0.55, "drug": 0.5, "safety": 0.45, "comp": 0.55, "data": 0.5, "reprod": 0.45}, {"title": "Senescent Microglia Resolution via Maresins-Senolytics", "gene": "BCL2L1", "composite": 0.521, "mech": 0.55, "evid": 0.5, "novel": 0.6, "feas": 0.45, "impact": 0.55, "drug": 0.5, "safety": 0.4, "comp": 0.55, "data": 0.5, "reprod": 0.45}, {"title": "Microglial Efferocytosis Enhancement via GPR32 Superagonists", "gene": "CMKLR1", "composite": 0.457, "mech": 0.5, "evid": 0.45, "novel": 0.5, "feas": 0.4, "impact": 0.5, "drug": 0.45, "safety": 0.4, "comp": 0.45, "data": 0.45, "reprod": 0.4}, {"title": "Circadian-Gated Maresin Biosynthesis Amplification", "gene": "ALOX12", "composite": 0.445, "mech": 0.5, "evid": 0.4, "novel": 0.55, "feas": 0.35, "impact": 0.5, "drug": 0.4, "safety": 0.4, "comp": 0.45, "data": 0.4, "reprod": 0.35}, {"title": "Oligodendrocyte Protectin D1 Mimetic for Myelin Resolution", "gene": "GPR37", "composite": 0.421, "mech": 0.5, "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": "Astrocytic Lipoxin A4 Pathway Restoration via ALOX15 Gene Therapy", "gene": "ALOX15", "composite": 0.429, "mech": 0.5, "evid": 0.4, "novel": 0.5, "feas": 0.35, "impact": 0.45, "drug": 0.4, "safety": 0.35, "comp": 0.45, "data": 0.4, "reprod": 0.35}, {"title": "Mitochondrial SPM Synthesis Platform Engineering", "gene": "ALOX5", "composite": 0.358, "mech": 0.4, "evid": 0.35, "novel": 0.45, "feas": 0.3, "impact": 0.4, "drug": 0.3, "safety": 0.3, "comp": 0.35, "data": 0.35, "reprod": 0.3}]

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 = {"ALOX15": -1.4, "ALOX5": 1.6, "ALOX12": -0.8, "CMKLR1": -1.2, "GPR37": -0.9, "TFRC": 1.3, "BCL2L1": 0.7, "TNF": 2.1, "IL1B": 1.9}
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
----------------------------------------------------------------------
TNF                  2.245     2.41e-12          YES
IL1B                 1.650     7.67e-09          YES
GPR37               -1.382     1.59e-06          YES
ALOX15              -1.529     2.49e-06          YES
ALOX5                1.590     2.71e-06          YES
CMKLR1              -1.062     4.83e-04          YES
ALOX12              -0.737     7.32e-03          YES
TFRC                 0.916     8.13e-03          YES
BCL2L1               0.252     4.23e-01           no

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 = ["Lipoxin Biosynthesis", "Resolvin Synthesis", "Maresin Pathway", "Protectin D1 Pathway", "Efferocytosis", "NF-kB Resolution", "Microglial Phenotype Switching", "Astrocyte A1/A2 Transition", "Circadian Immune Regulation", "Myelin Repair", "Senescent Cell Clearance", "BBB SPM 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
--------------------------------------------------------------------------------
BBB SPM Transport                                8.01     2.73e-04        2
Resolvin Synthesis                               7.02     3.26e-03        3
Astrocyte A1/A2 Transition                       5.02     9.15e-04        5
Maresin Pathway                                  3.63     5.34e-03        4
Myelin Repair                                    3.46     1.06e-02        2
Circadian Immune Regulation                      2.84     5.21e-06        5
Protectin D1 Pathway                             2.83     5.20e-03        3
Lipoxin Biosynthesis                             1.94     1.49e-07        3
Efferocytosis                                    1.34     7.42e-04        4
NF-kB Resolution                                 1.34     2.12e-05        5
Microglial Phenotype Switching                   1.12     9.47e-05        4
Senescent Cell Clearance                         1.04     9.02e-04        4

import numpy as np
from scipy import stats

hyp_data = [{"title": "Blood-Brain Barrier SPM Shuttle System", "gene": "TFRC", "composite": 0.533, "mech": 0.6, "evid": 0.5, "novel": 0.6, "feas": 0.45, "impact": 0.55, "drug": 0.5, "safety": 0.45, "comp": 0.55, "data": 0.5, "reprod": 0.45}, {"title": "Senescent Microglia Resolution via Maresins-Senolytics", "gene": "BCL2L1", "composite": 0.521, "mech": 0.55, "evid": 0.5, "novel": 0.6, "feas": 0.45, "impact": 0.55, "drug": 0.5, "safety": 0.4, "comp": 0.55, "data": 0.5, "reprod": 0.45}, {"title": "Microglial Efferocytosis Enhancement via GPR32 Superagonists", "gene": "CMKLR1", "composite": 0.457, "mech": 0.5, "evid": 0.45, "novel": 0.5, "feas": 0.4, "impact": 0.5, "drug": 0.45, "safety": 0.4, "comp": 0.45, "data": 0.45, "reprod": 0.4}, {"title": "Circadian-Gated Maresin Biosynthesis Amplification", "gene": "ALOX12", "composite": 0.445, "mech": 0.5, "evid": 0.4, "novel": 0.55, "feas": 0.35, "impact": 0.5, "drug": 0.4, "safety": 0.4, "comp": 0.45, "data": 0.4, "reprod": 0.35}, {"title": "Oligodendrocyte Protectin D1 Mimetic for Myelin Resolution", "gene": "GPR37", "composite": 0.421, "mech": 0.5, "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": "Astrocytic Lipoxin A4 Pathway Restoration via ALOX15 Gene Therapy", "gene": "ALOX15", "composite": 0.429, "mech": 0.5, "evid": 0.4, "novel": 0.5, "feas": 0.35, "impact": 0.45, "drug": 0.4, "safety": 0.35, "comp": 0.45, "data": 0.4, "reprod": 0.35}, {"title": "Mitochondrial SPM Synthesis Platform Engineering", "gene": "ALOX5", "composite": 0.358, "mech": 0.4, "evid": 0.35, "novel": 0.45, "feas": 0.3, "impact": 0.4, "drug": 0.3, "safety": 0.3, "comp": 0.35, "data": 0.35, "reprod": 0.3}]

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             0.507    0.056    0.400    0.600    0.200
Evidence                0.429    0.052    0.350    0.500    0.150
Novelty                 0.529    0.052    0.450    0.600    0.150
Feasibility             0.379    0.052    0.300    0.450    0.150
Impact                  0.486    0.052    0.400    0.550    0.150
Druggability            0.421    0.065    0.300    0.500    0.200
Safety                  0.379    0.045    0.300    0.450    0.150
Competition             0.457    0.068    0.350    0.550    0.200
Data Avail.             0.429    0.052    0.350    0.500    0.150
Reproducibility         0.379    0.052    0.300    0.450    0.150

2. DIMENSION CORRELATION MATRIX (Pearson r)
----------------------------------------------------------------------
Mechanistic       1.00   0.90   0.90   0.90   0.90   0.94
Evidence          0.90   1.00   0.87   1.00   0.94   0.98
Novelty           0.90   0.87   1.00   0.87   0.94   0.87
Feasibility       0.90   1.00   0.87   1.00   0.94   0.98
Impact            0.90   0.94   0.94   0.94   1.00   0.95
Druggability      0.94   0.98   0.87   0.98   0.95   1.00

3. COMPOSITE SCORE DISTRIBUTION
----------------------------------------------------------------------
Mean: 0.452
Median: 0.445
Std Dev: 0.056
Shapiro-Wilk test: W=0.9453, p=0.6869 (Normal)

4. TOP vs BOTTOM HYPOTHESIS COMPARISON
----------------------------------------------------------------------
Mechanistic          top=0.550 bot=0.475 t=  1.96 p=0.107 
Evidence             top=0.483 bot=0.388 t=  4.71 p=0.005 *
Novelty              top=0.567 bot=0.500 t=  1.81 p=0.131 
Feasibility          top=0.433 bot=0.337 t=  4.71 p=0.005 *
Impact               top=0.533 bot=0.450 t=  2.99 p=0.031 *
Druggability         top=0.483 bot=0.375 t=  3.31 p=0.021 *

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

Neuroinflammation Resolution Mechanisms and Pro-Resolving Mediators

Neuroinflammation Resolution Mechanisms and Pro-Resolving Mediators¶

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	Blood-Brain Barrier SPM Shuttle System	TFRC	0.533	0.60	0.50	0.60	0.45	0.55	0.50
1	Senescent Microglia Resolution via Maresins-Se...	BCL2L1	0.521	0.55	0.50	0.60	0.45	0.55	0.50
2	Microglial Efferocytosis Enhancement via GPR32...	CMKLR1	0.457	0.50	0.45	0.50	0.40	0.50	0.45
3	Circadian-Gated Maresin Biosynthesis Amplifica...	ALOX12	0.445	0.50	0.40	0.55	0.35	0.50	0.40
4	Oligodendrocyte Protectin D1 Mimetic for Myeli...	GPR37	0.421	0.50	0.40	0.50	0.35	0.45	0.40
5	Astrocytic Lipoxin A4 Pathway Restoration via ...	ALOX15	0.429	0.50	0.40	0.50	0.35	0.45	0.40
6	Mitochondrial SPM Synthesis Platform Engineering	ALOX5	0.358	0.40	0.35	0.45	0.30	0.40	0.30