bio-multi-omics-microbiome-metabolomics

name	bio-multi-omics-microbiome-metabolomics
description	Integrate microbiome (16S/shotgun) with metabolomics to analyze host-microbe metabolic interactions. Use when: user has paired microbiome and metabolomics data, asks about gut metabolites, needs SCFA quantification, bile acid profiling, tryptophan metabolism, or microbe-metabolite co-occurrence. Triggers: gut metabolites, SCFA, short-chain fatty acids, bile acids, microbiome, 16S, microbiome-metabolomics, mmvec, microbe-metabolite, tryptophan pathway, indole derivatives, gut-brain axis metabolites, fecal metabolomics.
tool_type	mixed
primary_tool	mmvec

Version Compatibility

Reference examples tested with: scikit-bio 0.6+, pandas 2.2+, scipy 1.12+, matplotlib 3.8+, seaborn 0.13+, mmvec 1.0+, mixOmics 6.26+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Microbiome Metabolomics

"Link my gut microbiome data with metabolomics profiles" → Integrate microbial abundance tables with metabolite measurements to identify host-microbiome metabolic interactions.

• Python: scikit-bio for diversity, mmvec for co-occurrence, pandas for integration
• R: mixOmics for sPLS correlation, MIMOSA2 for community metabolic modeling

Installation

# Python environment
pip install scikit-bio pandas scipy matplotlib seaborn networkx statsmodels

# mmvec (neural network microbe-metabolite co-occurrence)
pip install mmvec

# R packages
Rscript -e '
  if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
  BiocManager::install("mixOmics")
  install.packages(c("pheatmap", "igraph", "ggplot2"))
'

SCFA Quantification from Targeted LC-MS/GC-MS

Goal: Quantify short-chain fatty acids (acetate, propionate, butyrate) from targeted assay data using calibration curves and internal standard normalization.

Approach: Load standard curve and sample peak areas, fit weighted regression per SCFA, back-calculate concentrations with IS correction.

import pandas as pd
import numpy as np
from scipy.stats import linregress

scfa_names = ['acetate', 'propionate', 'butyrate']

standards = pd.DataFrame({
    'concentration_uM': [0, 10, 50, 100, 250, 500, 1000],
    'acetate_area': [200, 15000, 72000, 148000, 365000, 740000, 1480000],
    'propionate_area': [150, 12000, 58000, 120000, 295000, 600000, 1190000],
    'butyrate_area': [180, 13500, 65000, 135000, 330000, 670000, 1340000],
    'istd_area': [500000, 498000, 502000, 497000, 501000, 499000, 503000],
})

calibrations = {}
for scfa in scfa_names:
    ratio = standards[f'{scfa}_area'] / standards['istd_area']
    conc = standards['concentration_uM'].values
    mask = conc > 0
    slope, intercept, r_value, _, _ = linregress(conc[mask], ratio.values[mask])
    calibrations[scfa] = {'slope': slope, 'intercept': intercept, 'r2': r_value**2}

samples = pd.read_csv('scfa_peak_areas.csv')
for scfa in scfa_names:
    ratio = samples[f'{scfa}_area'] / samples['istd_area']
    cal = calibrations[scfa]
    samples[f'{scfa}_uM'] = np.maximum((ratio - cal['intercept']) / cal['slope'], 0)

samples[['sample_id'] + [f'{s}_uM' for s in scfa_names]].to_csv('scfa_concentrations.csv', index=False)

Bile Acid Profiling

Goal: Classify bile acids into primary vs secondary and conjugated vs unconjugated, then summarize profiles per sample.

Approach: Map measured bile acids to biological categories, compute group totals and ratios for downstream analysis.

import pandas as pd
import numpy as np

bile_acid_classes = {
    'CA': {'type': 'primary', 'conjugation': 'unconjugated'},
    'CDCA': {'type': 'primary', 'conjugation': 'unconjugated'},
    'GCA': {'type': 'primary', 'conjugation': 'glycine'},
    'TCA': {'type': 'primary', 'conjugation': 'taurine'},
    'GCDCA': {'type': 'primary', 'conjugation': 'glycine'},
    'TCDCA': {'type': 'primary', 'conjugation': 'taurine'},
    'DCA': {'type': 'secondary', 'conjugation': 'unconjugated'},
    'LCA': {'type': 'secondary', 'conjugation': 'unconjugated'},
    'UDCA': {'type': 'secondary', 'conjugation': 'unconjugated'},
    'GDCA': {'type': 'secondary', 'conjugation': 'glycine'},
    'TDCA': {'type': 'secondary', 'conjugation': 'taurine'},
    'GLCA': {'type': 'secondary', 'conjugation': 'glycine'},
    'TLCA': {'type': 'secondary', 'conjugation': 'taurine'},
}

ba_data = pd.read_csv('bile_acid_concentrations.csv', index_col='sample_id')
class_df = pd.DataFrame(bile_acid_classes).T

profile = pd.DataFrame(index=ba_data.index)
measured = [col for col in ba_data.columns if col in class_df.index]

for category in ['primary', 'secondary']:
    members = class_df[class_df['type'] == category].index
    cols = [m for m in members if m in measured]
    profile[f'total_{category}'] = ba_data[cols].sum(axis=1)

for conj in ['unconjugated', 'glycine', 'taurine']:
    members = class_df[class_df['conjugation'] == conj].index
    cols = [m for m in members if m in measured]
    profile[f'total_{conj}'] = ba_data[cols].sum(axis=1)

profile['primary_secondary_ratio'] = profile['total_primary'] / profile['total_secondary'].replace(0, np.nan)
profile['conjugated_unconjugated_ratio'] = (
    (profile['total_glycine'] + profile['total_taurine']) /
    profile['total_unconjugated'].replace(0, np.nan)
)
profile.to_csv('bile_acid_profiles.csv')

Tryptophan Metabolite Pathway Analysis

Goal: Map measured tryptophan metabolites to the indole and kynurenine pathway branches and compute pathway activity scores.

Approach: Assign metabolites to pathway branches, sum normalized abundances per branch, and compute the kynurenine-to-tryptophan ratio (KTR) as an IDO/TDO activity proxy.

import pandas as pd
import numpy as np

trp_pathways = {
    'indole': ['indole', 'indole_3_acetate', 'indole_3_propionate',
               'indole_3_aldehyde', 'indoxyl_sulfate', 'tryptamine'],
    'kynurenine': ['kynurenine', 'kynurenic_acid', '3_hydroxykynurenine',
                   'anthranilic_acid', 'quinolinic_acid', 'picolinic_acid'],
    'serotonin': ['serotonin', '5_hydroxyindoleacetic_acid', 'melatonin'],
}

trp_data = pd.read_csv('tryptophan_metabolites.csv', index_col='sample_id')

trp_data_norm = trp_data.div(trp_data.sum(axis=1), axis=0)

pathway_scores = pd.DataFrame(index=trp_data.index)
for pathway, metabolites in trp_pathways.items():
    cols = [m for m in metabolites if m in trp_data_norm.columns]
    pathway_scores[f'{pathway}_score'] = trp_data_norm[cols].sum(axis=1)

if 'kynurenine' in trp_data.columns and 'tryptophan' in trp_data.columns:
    pathway_scores['KTR'] = trp_data['kynurenine'] / trp_data['tryptophan'].replace(0, np.nan)

pathway_scores.to_csv('tryptophan_pathway_scores.csv')

16S/Metagenomics Data Loading and Diversity

Goal: Load microbial abundance tables and compute alpha/beta diversity metrics for integration with metabolomics.

Approach: Read OTU/ASV tables, compute Shannon diversity and Bray-Curtis dissimilarity using scikit-bio.

import pandas as pd
import numpy as np
from skbio.diversity import alpha_diversity, beta_diversity
from skbio import DistanceMatrix

otu_table = pd.read_csv('otu_table.csv', index_col='sample_id')

alpha_div = pd.DataFrame({
    'shannon': alpha_diversity('shannon', otu_table.values, otu_table.index),
    'observed_otus': alpha_diversity('observed_otus', otu_table.values, otu_table.index),
    'chao1': alpha_diversity('chao1', otu_table.values, otu_table.index),
}, index=otu_table.index)

bc_dm = beta_diversity('braycurtis', otu_table.values, otu_table.index)

from skbio.stats.ordination import pcoa
pcoa_results = pcoa(bc_dm)
pcoa_coords = pcoa_results.samples[['PC1', 'PC2', 'PC3']]
pcoa_coords.index = otu_table.index

Microbiome-Metabolome Data Integration

Goal: Align microbial abundance and metabolomics matrices on shared samples for joint analysis.

Approach: Intersect sample IDs, apply CLR transform to compositional microbiome data, log-transform metabolomics, and compute Spearman correlations.

import pandas as pd
import numpy as np
from scipy.stats import spearmanr

otu_table = pd.read_csv('otu_table.csv', index_col='sample_id')
metab_table = pd.read_csv('metabolomics.csv', index_col='sample_id')

common_samples = otu_table.index.intersection(metab_table.index)
otu_aligned = otu_table.loc[common_samples]
metab_aligned = metab_table.loc[common_samples]

def clr_transform(df):
    pseudocount = 0.5
    log_data = np.log(df + pseudocount)
    geometric_mean = log_data.mean(axis=1)
    return log_data.sub(geometric_mean, axis=0)

otu_clr = clr_transform(otu_aligned)
metab_log = np.log1p(metab_aligned)

n_taxa = otu_clr.shape[1]
n_metab = metab_log.shape[1]
corr_matrix = np.zeros((n_taxa, n_metab))
pval_matrix = np.zeros((n_taxa, n_metab))

for i in range(n_taxa):
    for j in range(n_metab):
        rho, pval = spearmanr(otu_clr.iloc[:, i], metab_log.iloc[:, j])
        corr_matrix[i, j] = rho
        pval_matrix[i, j] = pval

corr_df = pd.DataFrame(corr_matrix, index=otu_clr.columns, columns=metab_log.columns)
pval_df = pd.DataFrame(pval_matrix, index=otu_clr.columns, columns=metab_log.columns)

mmvec: Neural Network Co-Occurrence

Goal: Model microbe-metabolite co-occurrence probabilities using mmvec neural network embeddings.

Approach: Prepare BIOM-format inputs, run mmvec to learn conditional probabilities, then extract co-occurrence ranks.

# Convert tables to BIOM format
biom convert -i otu_table.tsv -o microbes.biom --table-type="OTU table" --to-hdf5
biom convert -i metabolite_table.tsv -o metabolites.biom --table-type="OTU table" --to-hdf5

# Run mmvec (standalone CLI; also available as QIIME 2 plugin: qiime mmvec paired-omics)
mmvec paired-omics \
    --microbe-file microbes.biom \
    --metabolite-file metabolites.biom \
    --epochs 1000 \
    --batch-size 64 \
    --latent-dim 3 \
    --input-prior 0.01 \
    --output-prior 0.01 \
    --learning-rate 1e-3 \
    --summary-dir mmvec_output

import pandas as pd

conditionals = pd.read_csv('mmvec_output/conditionals.tsv', sep='\t', index_col=0)

top_associations = []
for microbe in conditionals.index:
    ranked = conditionals.loc[microbe].sort_values(ascending=False)
    for metabolite in ranked.head(5).index:
        top_associations.append({
            'microbe': microbe,
            'metabolite': metabolite,
            'conditional_prob': ranked[metabolite],
        })

top_df = pd.DataFrame(top_associations)
top_df.to_csv('top_microbe_metabolite_associations.csv', index=False)

sPLS Microbiome-Metabolome Correlation (R/mixOmics)

Goal: Identify correlated features between microbiome and metabolome using sparse PLS in R.

Approach: Load CLR-transformed microbiome and log-transformed metabolomics matrices, tune sPLS, and extract selected feature pairs.

library(mixOmics)

X_microbiome <- as.matrix(read.csv('otu_clr.csv', row.names = 1))
X_metabolome <- as.matrix(read.csv('metabolomics_log.csv', row.names = 1))

common <- intersect(rownames(X_microbiome), rownames(X_metabolome))
X_microbiome <- X_microbiome[common, ]
X_metabolome <- X_metabolome[common, ]

tune_spls <- perf(spls(X_microbiome, X_metabolome, ncomp = 5),
                  validation = 'Mfold', folds = 5, nrepeat = 10)

spls_result <- spls(X_microbiome, X_metabolome, ncomp = 3,
                    keepX = c(30, 30, 30), keepY = c(20, 20, 20))

plotIndiv(spls_result, comp = c(1, 2), rep.space = 'XY-variate')
plotVar(spls_result, comp = c(1, 2), var.names = TRUE, cex = c(3, 3))
cim(spls_result, comp = 1, xlab = 'Metabolites', ylab = 'Microbes',
    margins = c(8, 12))

selected_microbes <- selectVar(spls_result, comp = 1)$X$name
selected_metabolites <- selectVar(spls_result, comp = 1)$Y$name

Visualization: Paired Heatmaps and Correlation Networks

Goal: Generate publication-quality paired heatmaps and network plots of microbe-metabolite associations.

Approach: Build clustered heatmaps with significance masking and networkx correlation graphs with community detection.

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import networkx as nx
from statsmodels.stats.multitest import multipletests

_, pval_corrected, _, _ = multipletests(pval_df.values.flatten(), method='fdr_bh')
pval_fdr = pd.DataFrame(
    pval_corrected.reshape(pval_df.shape),
    index=pval_df.index,
    columns=pval_df.columns
)

sig_mask = pval_fdr < 0.05
top_taxa = corr_df[sig_mask].abs().max(axis=1).nlargest(25).index
top_metab = corr_df[sig_mask].abs().max(axis=0).nlargest(25).index

fig, ax = plt.subplots(figsize=(14, 10))
sns.heatmap(
    corr_df.loc[top_taxa, top_metab],
    cmap='RdBu_r', center=0, vmin=-0.8, vmax=0.8,
    xticklabels=True, yticklabels=True,
    linewidths=0.5, ax=ax,
)
ax.set_xlabel('Metabolites')
ax.set_ylabel('Microbial Taxa')
ax.set_title('Microbiome-Metabolome Spearman Correlations (FDR < 0.05)')
plt.tight_layout()
plt.savefig('microbiome_metabolome_heatmap.png', dpi=300)

G = nx.Graph()
for taxon in top_taxa:
    G.add_node(taxon, node_type='microbe')
for metab in top_metab:
    G.add_node(metab, node_type='metabolite')

for taxon in top_taxa:
    for metab in top_metab:
        if pval_fdr.loc[taxon, metab] < 0.05 and abs(corr_df.loc[taxon, metab]) > 0.4:
            G.add_edge(taxon, metab, weight=corr_df.loc[taxon, metab])

pos = nx.spring_layout(G, k=2, seed=42)
node_colors = ['#2196F3' if G.nodes[n]['node_type'] == 'microbe' else '#FF9800' for n in G.nodes()]
edge_colors = ['#d32f2f' if G.edges[e]['weight'] < 0 else '#388e3c' for e in G.edges()]

fig, ax = plt.subplots(figsize=(14, 12))
nx.draw_networkx(G, pos, node_color=node_colors, edge_color=edge_colors,
                 node_size=600, font_size=7, width=1.5, ax=ax)
ax.set_title('Microbe-Metabolite Correlation Network')
plt.tight_layout()
plt.savefig('correlation_network.png', dpi=300)

Best Practices

• Compositionality: Always apply CLR or other compositional transform to microbiome relative abundance data before correlation analysis; raw relative abundances produce spurious correlations.
• Multiple testing: Apply FDR correction (Benjamini-Hochberg) when computing many pairwise correlations between taxa and metabolites.
• Sample matching: Verify that microbiome and metabolomics samples are from the same biological specimens; mismatched sample IDs silently corrupt results.
• SCFA specificity: Use deuterated internal standards (d3-acetate, d5-propionate, d7-butyrate) for SCFA quantification; matrix effects in fecal samples are substantial.
• Bile acid isomers: Confirm chromatographic separation of alpha/beta-MCA and UDCA/HDCA isomers before quantification; co-elution is common on C18 columns.
• mmvec training: Monitor training loss convergence; underfitting (too few epochs) yields random co-occurrence rankings.
• Batch effects: If samples were collected across sites or sequenced in different runs, include batch as a covariate or apply ComBat correction before integration.

Related Skills

• mixomics-analysis - Supervised multi-omics integration with DIABLO
• targeted-analysis - Calibration curves and absolute quantification
• normalization-qc - QC-based normalization for metabolomics
• data-harmonization - Preprocess before multi-omics integration