import pandas as pd
ann_rows = [{'gene': 'TREM2', 'name': 'triggering receptor expressed on myeloid cells 2', 'summary': 'This gene encodes a membrane protein that forms a receptor signaling complex with the TYRO protein tyrosine kinase bindi'}]
pd.DataFrame(ann_rows)

go_bp = [{'rank': 1, 'term': 'Chemokine (C-X-C Motif) Ligand 12 Signaling Pathway (GO:0038146)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['TREM2']}, {'rank': 2, 'term': 'Negative Regulation Of Sequestering Of Triglyceride (GO:0010891)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['TREM2']}, {'rank': 3, 'term': 'Negative Regulation Of Toll-Like Receptor 2 Signaling Pathway (GO:0034136)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['TREM2']}, {'rank': 4, 'term': 'Regulation Of Resting Membrane Potential (GO:0060075)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['TREM2']}, {'rank': 5, 'term': 'Positive Regulation Of Lipoprotein Particle Clearance (GO:0010986)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['TREM2']}, {'rank': 6, 'term': 'Regulation Of Fat Cell Proliferation (GO:0070344)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['TREM2']}, {'rank': 7, 'term': 'Negative Regulation Of Astrocyte Differentiation (GO:0048712)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['TREM2']}, {'rank': 8, 'term': 'Positive Regulation Of Microglial Cell Activation (GO:1903980)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['TREM2']}, {'rank': 9, 'term': 'Positive Regulation Of Microglial Cell Migration (GO:1904141)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['TREM2']}, {'rank': 10, 'term': 'Positive Regulation Of Amyloid-Beta Clearance (GO:1900223)', 'p_value': 0.0002999905873105514, 'odds_ratio': 19994.0, 'genes': ['TREM2']}]
go_df = pd.DataFrame(go_bp)[['term','p_value','odds_ratio','genes']]
go_df['p_value'] = go_df['p_value'].apply(lambda p: f'{p:.2e}')
go_df['odds_ratio'] = go_df['odds_ratio'].round(1)
go_df['term'] = go_df['term'].str[:60]
go_df['n_hits'] = go_df['genes'].apply(len)
go_df['genes'] = go_df['genes'].apply(lambda g: ', '.join(g))
go_df[['term','n_hits','p_value','odds_ratio','genes']]

import matplotlib.pyplot as plt
import numpy as np

go_bp = [{'rank': 1, 'term': 'Chemokine (C-X-C Motif) Ligand 12 Signaling Pathway (GO:0038146)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['TREM2']}, {'rank': 2, 'term': 'Negative Regulation Of Sequestering Of Triglyceride (GO:0010891)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['TREM2']}, {'rank': 3, 'term': 'Negative Regulation Of Toll-Like Receptor 2 Signaling Pathway (GO:0034136)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['TREM2']}, {'rank': 4, 'term': 'Regulation Of Resting Membrane Potential (GO:0060075)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['TREM2']}, {'rank': 5, 'term': 'Positive Regulation Of Lipoprotein Particle Clearance (GO:0010986)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['TREM2']}, {'rank': 6, 'term': 'Regulation Of Fat Cell Proliferation (GO:0070344)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['TREM2']}, {'rank': 7, 'term': 'Negative Regulation Of Astrocyte Differentiation (GO:0048712)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['TREM2']}, {'rank': 8, 'term': 'Positive Regulation Of Microglial Cell Activation (GO:1903980)', 'p_value': 0.0002499917995416556, 'odds_ratio': 19995.0, 'genes': ['TREM2']}]
terms = [t['term'][:45] for t in go_bp][::-1]
neglogp = [-np.log10(max(t['p_value'], 1e-300)) for t in go_bp][::-1]
fig, ax = plt.subplots(figsize=(9, 4.5))
ax.barh(terms, neglogp, color='#4fc3f7')
ax.set_xlabel('-log10(p-value)')
ax.set_title('Top GO:BP enrichment (Enrichr)')
ax.grid(axis='x', alpha=0.3)
plt.tight_layout(); plt.show()

ppi = [{'protein1': 'APOE', 'protein2': 'TREM2', 'score': 0.986, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.514, 'dscore': 0, 'tscore': 0.974}, {'protein1': 'APOE', 'protein2': 'CLU', 'score': 0.991, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0.971}, {'protein1': 'TYROBP', 'protein2': 'SIRPA', 'score': 0.8, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.8, 'tscore': 0}, {'protein1': 'TYROBP', 'protein2': 'SIRPG', 'score': 0.8, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.8, 'tscore': 0}, {'protein1': 'TYROBP', 'protein2': 'TREM2', 'score': 0.998, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.526, 'dscore': 0.8, 'tscore': 0.982}, {'protein1': 'TYROBP', 'protein2': 'SYK', 'score': 0.999, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.886, 'dscore': 0.9, 'tscore': 0.982}, {'protein1': 'FCER1G', 'protein2': 'IGKV1-33', 'score': 0.52, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.5, 'tscore': 0.081}, {'protein1': 'FCER1G', 'protein2': 'IGKV1D-33', 'score': 0.52, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.5, 'tscore': 0.081}, {'protein1': 'FCER1G', 'protein2': 'TREM2', 'score': 0.977, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.977}, {'protein1': 'FCER1G', 'protein2': 'SYK', 'score': 0.999, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.886, 'dscore': 0.9, 'tscore': 0.982}, {'protein1': 'SIRPG', 'protein2': 'TREM2', 'score': 0.9, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.9, 'tscore': 0}, {'protein1': 'SIRPG', 'protein2': 'SYK', 'score': 0.9, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.9, 'tscore': 0}, {'protein1': 'SIRPG', 'protein2': 'SIRPA', 'score': 0.966, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.495, 'dscore': 0.9, 'tscore': 0.397}, {'protein1': 'CLU', 'protein2': 'TREM2', 'score': 0.954, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.292, 'dscore': 0, 'tscore': 0.938}, {'protein1': 'TREM2', 'protein2': 'IGHV3-16', 'score': 0.9, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.9, 'dscore': 0, 'tscore': 0}, {'protein1': 'TREM2', 'protein2': 'IGKV1-33', 'score': 0.9, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.9, 'dscore': 0, 'tscore': 0}, {'protein1': 'TREM2', 'protein2': 'IGKV1D-33', 'score': 0.9, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.9, 'dscore': 0, 'tscore': 0}, {'protein1': 'TREM2', 'protein2': 'SIRPA', 'score': 0.91, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.9, 'tscore': 0.141}, {'protein1': 'TREM2', 'protein2': 'SYK', 'score': 0.929, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.9, 'tscore': 0.323}, {'protein1': 'SYK', 'protein2': 'IGKV1-33', 'score': 0.4, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.4, 'tscore': 0}, {'protein1': 'SYK', 'protein2': 'IGKV1D-33', 'score': 0.4, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.4, 'tscore': 0}, {'protein1': 'SYK', 'protein2': 'SIRPA', 'score': 0.9, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.9, 'tscore': 0}, {'protein1': 'IGHV3-16', 'protein2': 'IGKV1-33', 'score': 0.952, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.9, 'dscore': 0.54, 'tscore': 0}, {'protein1': 'IGHV3-16', 'protein2': 'IGKV1D-33', 'score': 0.952, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.9, 'dscore': 0.54, 'tscore': 0}, {'protein1': 'IGKV1-33', 'protein2': 'IGKV1D-33', 'score': 0.952, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.9, 'dscore': 0.54, 'tscore': 0}]
ppi_df = pd.DataFrame(ppi).sort_values('score', ascending=False)
display_cols = [c for c in ['protein1','protein2','score','escore','tscore'] if c in ppi_df.columns]
print(f'{len(ppi_df)} STRING edges')
ppi_df[display_cols].head(20)

25 STRING edges

import math
ppi = [{'protein1': 'APOE', 'protein2': 'TREM2', 'score': 0.986, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.514, 'dscore': 0, 'tscore': 0.974}, {'protein1': 'APOE', 'protein2': 'CLU', 'score': 0.991, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.72, 'tscore': 0.971}, {'protein1': 'TYROBP', 'protein2': 'SIRPA', 'score': 0.8, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.8, 'tscore': 0}, {'protein1': 'TYROBP', 'protein2': 'SIRPG', 'score': 0.8, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.8, 'tscore': 0}, {'protein1': 'TYROBP', 'protein2': 'TREM2', 'score': 0.998, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.526, 'dscore': 0.8, 'tscore': 0.982}, {'protein1': 'TYROBP', 'protein2': 'SYK', 'score': 0.999, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.886, 'dscore': 0.9, 'tscore': 0.982}, {'protein1': 'FCER1G', 'protein2': 'IGKV1-33', 'score': 0.52, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.5, 'tscore': 0.081}, {'protein1': 'FCER1G', 'protein2': 'IGKV1D-33', 'score': 0.52, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.5, 'tscore': 0.081}, {'protein1': 'FCER1G', 'protein2': 'TREM2', 'score': 0.977, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0, 'tscore': 0.977}, {'protein1': 'FCER1G', 'protein2': 'SYK', 'score': 0.999, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.886, 'dscore': 0.9, 'tscore': 0.982}, {'protein1': 'SIRPG', 'protein2': 'TREM2', 'score': 0.9, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.9, 'tscore': 0}, {'protein1': 'SIRPG', 'protein2': 'SYK', 'score': 0.9, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.9, 'tscore': 0}, {'protein1': 'SIRPG', 'protein2': 'SIRPA', 'score': 0.966, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.495, 'dscore': 0.9, 'tscore': 0.397}, {'protein1': 'CLU', 'protein2': 'TREM2', 'score': 0.954, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.292, 'dscore': 0, 'tscore': 0.938}, {'protein1': 'TREM2', 'protein2': 'IGHV3-16', 'score': 0.9, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.9, 'dscore': 0, 'tscore': 0}, {'protein1': 'TREM2', 'protein2': 'IGKV1-33', 'score': 0.9, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.9, 'dscore': 0, 'tscore': 0}, {'protein1': 'TREM2', 'protein2': 'IGKV1D-33', 'score': 0.9, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.9, 'dscore': 0, 'tscore': 0}, {'protein1': 'TREM2', 'protein2': 'SIRPA', 'score': 0.91, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.9, 'tscore': 0.141}, {'protein1': 'TREM2', 'protein2': 'SYK', 'score': 0.929, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.9, 'tscore': 0.323}, {'protein1': 'SYK', 'protein2': 'IGKV1-33', 'score': 0.4, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.4, 'tscore': 0}, {'protein1': 'SYK', 'protein2': 'IGKV1D-33', 'score': 0.4, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.4, 'tscore': 0}, {'protein1': 'SYK', 'protein2': 'SIRPA', 'score': 0.9, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0, 'dscore': 0.9, 'tscore': 0}, {'protein1': 'IGHV3-16', 'protein2': 'IGKV1-33', 'score': 0.952, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.9, 'dscore': 0.54, 'tscore': 0}, {'protein1': 'IGHV3-16', 'protein2': 'IGKV1D-33', 'score': 0.952, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.9, 'dscore': 0.54, 'tscore': 0}, {'protein1': 'IGKV1-33', 'protein2': 'IGKV1D-33', 'score': 0.952, 'nscore': 0, 'fscore': 0, 'pscore': 0, 'ascore': 0, 'escore': 0.9, 'dscore': 0.54, 'tscore': 0}]
if ppi:
    nodes = sorted({p for e in ppi for p in (e['protein1'], e['protein2'])})
    n = len(nodes)
    pos = {n_: (math.cos(2*math.pi*i/n), math.sin(2*math.pi*i/n)) for i, n_ in enumerate(nodes)}
    fig, ax = plt.subplots(figsize=(7, 7))
    for e in ppi:
        x1,y1 = pos[e['protein1']]; x2,y2 = pos[e['protein2']]
        ax.plot([x1,x2],[y1,y2], color='#888', alpha=0.3+0.5*e.get('score',0))
    for name,(x,y) in pos.items():
        ax.scatter([x],[y], s=450, color='#ffd54f', edgecolors='#333', zorder=3)
        ax.annotate(name, (x,y), ha='center', va='center', fontsize=8, fontweight='bold', zorder=4)
    ax.set_aspect('equal'); ax.axis('off')
    ax.set_title(f'STRING PPI network ({len(ppi)} edges)')
    plt.tight_layout(); plt.show()

pw_rows = [{'gene': 'TREM2', 'n_pathways': 4, 'top_pathway': 'Immunoregulatory interactions between a Lymphoid and a non-Lymphoid ce'}]
pd.DataFrame(pw_rows).sort_values('n_pathways', ascending=False)

hyp_data = [('TREM2-Mediated Microglial Checkpoint Therapy', 0.534)]
titles = [h[0] for h in hyp_data][::-1]
scores = [h[1] for h in hyp_data][::-1]
fig, ax = plt.subplots(figsize=(10, max(8, len(titles)*0.4)))
colors = ['#ef5350' if s >= 0.6 else '#ffa726' if s >= 0.5 else '#66bb6a' for s in scores]
ax.barh(range(len(titles)), scores, color=colors)
ax.set_yticks(range(len(titles))); ax.set_yticklabels(titles, fontsize=7)
ax.set_xlabel('Composite Score'); ax.set_title("Cell type vulnerability in Alzheimer's Disease (SEA-AD data)")
ax.grid(axis='x', alpha=0.3)
plt.tight_layout(); plt.show()

labels = ['TREM2-Mediated Microglial Checkpoint The']
matrix = np.array([[0.8, 0.7, 0.6, 0.7, 0.0, 0.7, 0.6, 0.8, 0.5]])
dims = ['novelty_score', 'feasibility_score', 'impact_score', 'mechanistic_plausibility_score', 'clinical_relevance_score', 'data_availability_score', 'reproducibility_score', 'druggability_score', 'safety_profile_score']
if matrix.size:
    fig, ax = plt.subplots(figsize=(10, 5))
    im = ax.imshow(matrix, cmap='RdYlGn', aspect='auto', vmin=0, vmax=1)
    ax.set_xticks(range(len(dims)))
    ax.set_xticklabels([d.replace('_score','').replace('_',' ').title() for d in dims],
                       rotation=45, ha='right', fontsize=8)
    ax.set_yticks(range(len(labels))); ax.set_yticklabels(labels, fontsize=7)
    ax.set_title('Score dimensions — hypotheses')
    plt.colorbar(im, ax=ax, shrink=0.8)
    plt.tight_layout(); plt.show()
else:
    print('No score data available')

lit_data = [{'year': '2021', 'journal': 'Mech Ageing Dev', 'title': "TREM2, microglia, and Alzheimer's disease.", 'pmid': '33516818'}, {'year': '2023', 'journal': 'Semin Immunol', 'title': 'TREM2: A new player in the tumor microenvironment.', 'pmid': '36989543'}, {'year': '2017', 'journal': 'Cell', 'title': "TREM2 Maintains Microglial Metabolic Fitness in Alzheimer's Disease.", 'pmid': '28802038'}, {'year': '2017', 'journal': 'Trends Mol Med', 'title': 'TREM2, Microglia, and Neurodegenerative Diseases.', 'pmid': '28442216'}, {'year': '2024', 'journal': 'Neuropharmacology', 'title': 'Microglia and TREM2.', 'pmid': '38821351'}]
if lit_data:
    df = pd.DataFrame(lit_data)
    print(f'{len(lit_data)} PubMed results')
    display(df)
else:
    print('No PubMed results')

5 PubMed results

edge_data = [{'source': 'h-722ec547', 'relation': 'targets', 'target': 'TREM2', 'strength': 0.5}, {'source': 'TREM2', 'relation': 'involved_in', 'target': 'trem2_tyrobp_microglial_signal', 'strength': 0.4}]
if edge_data:
    pd.DataFrame(edge_data).head(25)
else:
    print('No KG edge data available')

Cell type vulnerability in Alzheimer's Disease (SEA-AD data) — Analysis Notebook

Cell type vulnerability in Alzheimer's Disease (SEA-AD data)¶

Research question¶

Approach¶

Debate Summary¶

1. Target gene annotations (MyGene)¶

2. GO Biological Process enrichment (Enrichr)¶

3. STRING protein interaction network¶

4. Reactome pathway footprint¶

5. Hypothesis ranking (1 hypotheses)¶

6. PubMed literature per hypothesis¶

Hypothesis 1: TREM2-Mediated Microglial Checkpoint Therapy¶

TREM2-Mediated Microglial Checkpoint Therapy: Expanded Hypothesis¶

Molecular Mechanism of Action¶

7. Knowledge graph edges (2 total)¶

Caveats¶

	term	n_hits	p_value	odds_ratio	genes
0	Chemokine (C-X-C Motif) Ligand 12 Signaling Pa...	1	2.50e-04	19995.0	TREM2
1	Negative Regulation Of Sequestering Of Triglyc...	1	2.50e-04	19995.0	TREM2
2	Negative Regulation Of Toll-Like Receptor 2 Si...	1	2.50e-04	19995.0	TREM2
3	Regulation Of Resting Membrane Potential (GO:0...	1	2.50e-04	19995.0	TREM2
4	Positive Regulation Of Lipoprotein Particle Cl...	1	2.50e-04	19995.0	TREM2
5	Regulation Of Fat Cell Proliferation (GO:0070344)	1	2.50e-04	19995.0	TREM2
6	Negative Regulation Of Astrocyte Differentiati...	1	2.50e-04	19995.0	TREM2
7	Positive Regulation Of Microglial Cell Activat...	1	2.50e-04	19995.0	TREM2
8	Positive Regulation Of Microglial Cell Migrati...	1	2.50e-04	19995.0	TREM2
9	Positive Regulation Of Amyloid-Beta Clearance ...	1	3.00e-04	19994.0	TREM2

	protein1	protein2	score	escore	tscore
5	TYROBP	SYK	0.999	0.886	0.982
9	FCER1G	SYK	0.999	0.886	0.982
4	TYROBP	TREM2	0.998	0.526	0.982
1	APOE	CLU	0.991	0.000	0.971
0	APOE	TREM2	0.986	0.514	0.974
8	FCER1G	TREM2	0.977	0.000	0.977
12	SIRPG	SIRPA	0.966	0.495	0.397
13	CLU	TREM2	0.954	0.292	0.938
23	IGHV3-16	IGKV1D-33	0.952	0.900	0.000
24	IGKV1-33	IGKV1D-33	0.952	0.900	0.000
22	IGHV3-16	IGKV1-33	0.952	0.900	0.000
18	TREM2	SYK	0.929	0.000	0.323
17	TREM2	SIRPA	0.910	0.000	0.141
16	TREM2	IGKV1D-33	0.900	0.900	0.000
15	TREM2	IGKV1-33	0.900	0.900	0.000
14	TREM2	IGHV3-16	0.900	0.900	0.000
11	SIRPG	SYK	0.900	0.000	0.000
10	SIRPG	TREM2	0.900	0.000	0.000
21	SYK	SIRPA	0.900	0.000	0.000
2	TYROBP	SIRPA	0.800	0.000	0.000

	year	journal	title	pmid
0	2021	Mech Ageing Dev	TREM2, microglia, and Alzheimer's disease.	33516818
1	2023	Semin Immunol	TREM2: A new player in the tumor microenvironm...	36989543
2	2017	Cell	TREM2 Maintains Microglial Metabolic Fitness i...	28802038
3	2017	Trends Mol Med	TREM2, Microglia, and Neurodegenerative Diseases.	28442216
4	2024	Neuropharmacology	Microglia and TREM2.	38821351