majiayu000 Metabolite-Mediated Cell Communication
MeboCost Overview
MeboCost infers metabolite-mediated communication by:
- Predicting metabolite secretion from enzyme expression
- Identifying metabolite-sensing receptors
- Computing communication scores between cell types
Basic Workflow
import mebocost as mbc
import scanpy as sc
# Load scRNA-seq data
adata = sc.read_h5ad('adata.h5ad')
# Initialize MeboCost
mebo = mbc.create_obj(
adata=adata,
group_col='cell_type', # Cell type annotation column
species='human' # 'human' or 'mouse'
)
# Infer metabolite communication
mebo.infer_commu(
n_permutations=1000, # Permutations for significance testing
seed=42
)
# Get significant interactions
sig_interactions = mebo.commu_res[mebo.commu_res['pval'] < 0.05]
Prepare Data
import scanpy as sc
def prepare_for_mebocost(adata, cell_type_col='cell_type', min_cells=50):
'''Prepare AnnData for MeboCost analysis
Requirements:
- Log-normalized expression (sc.pp.normalize_total, sc.pp.log1p)
- Cell type annotations
- Gene symbols (not Ensembl IDs)
'''
# Check normalization
if adata.X.max() > 50:
print('Warning: Data may not be log-normalized')
# Filter rare cell types
cell_counts = adata.obs[cell_type_col].value_counts()
valid_types = cell_counts[cell_counts >= min_cells].index
adata = adata[adata.obs[cell_type_col].isin(valid_types)].copy()
print(f'Cell types: {len(valid_types)}')
print(f'Cells: {adata.n_obs}')
return adata
Run Communication Analysis
def run_mebocost(adata, cell_type_col='cell_type', species='human'):
'''Run MeboCost metabolite communication analysis
Args:
adata: AnnData with log-normalized expression
cell_type_col: Column with cell type annotations
species: 'human' or 'mouse'
Returns:
MeboCost object with communication results
'''
import mebocost as mbc
# Create MeboCost object
mebo = mbc.create_obj(
adata=adata,
group_col=cell_type_col,
species=species
)
# Infer enzyme-metabolite-receptor communication
# n_permutations: Higher = more accurate p-values but slower
# 1000 is standard; use 100 for quick exploration
mebo.infer_commu(n_permutations=1000, seed=42)
return mebo
Analyze Results
def analyze_metabolite_communication(mebo, pval_threshold=0.05):
'''Extract and summarize significant communications
Communication flow:
Sender cell -> Enzyme -> Metabolite -> Receptor -> Receiver cell
'''
results = mebo.commu_res.copy()
# Filter significant interactions
sig = results[results['pval'] < pval_threshold]
# Summary statistics
summary = {
'total_interactions': len(results),
'significant_interactions': len(sig),
'unique_metabolites': sig['metabolite'].nunique(),
'unique_sender_types': sig['sender'].nunique(),
'unique_receiver_types': sig['receiver'].nunique()
}
# Top metabolites by frequency
top_metabolites = sig['metabolite'].value_counts().head(10)
# Top sender-receiver pairs
sig['pair'] = sig['sender'] + ' -> ' + sig['receiver']
top_pairs = sig['pair'].value_counts().head(10)
return {
'summary': summary,
'top_metabolites': top_metabolites,
'top_pairs': top_pairs,
'significant_interactions': sig
}
Visualization
def plot_communication_network(mebo, pval_threshold=0.05):
'''Plot metabolite communication network'''
import matplotlib.pyplot as plt
# Filter significant
sig = mebo.commu_res[mebo.commu_res['pval'] < pval_threshold]
# Aggregate by cell type pair
pair_counts = sig.groupby(['sender', 'receiver']).size().reset_index(name='count')
# Create chord diagram or heatmap
pivot = pair_counts.pivot(index='sender', columns='receiver', values='count')
pivot = pivot.fillna(0)
plt.figure(figsize=(10, 8))
plt.imshow(pivot.values, cmap='Reds')
plt.xticks(range(len(pivot.columns)), pivot.columns, rotation=45, ha='right')
plt.yticks(range(len(pivot.index)), pivot.index)
plt.colorbar(label='Number of interactions')
plt.xlabel('Receiver')
plt.ylabel('Sender')
plt.title('Metabolite Communication Network')
plt.tight_layout()
return plt.gcf()
def plot_metabolite_flow(mebo, metabolite, pval_threshold=0.05):
'''Visualize communication flow for specific metabolite'''
sig = mebo.commu_res[
(mebo.commu_res['metabolite'] == metabolite) &
(mebo.commu_res['pval'] < pval_threshold)
]
print(f'\n{metabolite} communication:')
for _, row in sig.iterrows():
print(f" {row['sender']} ({row['enzyme']}) -> "
f"{row['receiver']} ({row['receptor']})")
print(f" Score: {row['commu_score']:.3f}, p-value: {row['pval']:.4f}")
Compare Conditions
def compare_conditions(adata, condition_col, cell_type_col, species='human'):
'''Compare metabolite communication between conditions
Useful for:
- Tumor vs normal
- Treatment vs control
- Disease vs healthy
'''
import mebocost as mbc
conditions = adata.obs[condition_col].unique()
results = {}
for condition in conditions:
adata_subset = adata[adata.obs[condition_col] == condition].copy()
mebo = mbc.create_obj(
adata=adata_subset,
group_col=cell_type_col,
species=species
)
mebo.infer_commu(n_permutations=1000, seed=42)
results[condition] = mebo.commu_res
# Find differential communications
# Interactions significant in one condition but not another
return results
Metabolite Categories
# MeboCost includes curated metabolite-receptor pairs
# Major categories:
METABOLITE_CATEGORIES = {
'amino_acids': ['Glutamine', 'Glutamate', 'Tryptophan', 'Arginine'],
'lipids': ['Prostaglandin E2', 'Leukotriene B4', 'Sphingosine-1-phosphate'],
'nucleotides': ['ATP', 'Adenosine', 'UDP'],
'vitamins': ['Retinoic acid', 'Vitamin D'],
'other': ['Lactate', 'Succinate', 'Itaconate']
}
def filter_by_category(results, category):
'''Filter results to specific metabolite category'''
metabolites = METABOLITE_CATEGORIES.get(category, [])
return results[results['metabolite'].isin(metabolites)]
Related Skills
- single-cell/cell-communication - Ligand-receptor communication analysis
- metabolomics/pathway-mapping - Metabolic pathway context
- systems-biology/flux-balance-analysis - Metabolic flux predictions