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Skillsomicverse-bulk-metabol-pathway-multifactor

omicverse-bulk-metabol-pathway-multifactor

Design & creation

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Updated 10-16 10:21

About this skill

OmicVerse Bulk Metabolomics — Pathway, Multifactor, DGCA & MOFA

Goal

Cover the four downstream interpretation paths that consume a preprocessed metabolomics AnnData and produce a biological story:

  1. Pathway / MSEA — resolve metabolite names to external IDs (HMDB / KEGG / ChEBI / PubChem / LIPID MAPS) and run over-representation (ORA) or GSEA-style ranked enrichment, plot bars / dots.
  2. Multi-factor designs — ASCA (ANOVA-SCA), mixed models (MixedLM), MEBA time-series — for studies with treatment × time × patient layouts.
  3. Differential correlation (DGCA) — find metabolite pairs whose correlation rewires between conditions, plus per-condition correlation networks.
  4. Multi-omics MOFA+ — joint factorization across metabolomics and RNA-seq (or any other AnnData-aligned views).

Plus a worked end-to-end real-data case study: MTBLS1 (urine NMR, Type 2 Diabetes) that ties preprocessing → univariate / multivariate / pathway / multifactor / DGCA / network into one runbook.

Quick Workflow

Pathway / MSEA (t_metabol_03)

  1. Run differential on the preprocessed (PQN+log) AnnData to get a per-metabolite p-value DataFrame.
  2. (Optional) Resolve metabolite names to external IDs with ov.metabol.map_ids(...) to confirm KEGG coverage.
  3. Run ov.metabol.msea_ora(hits, background, min_size=3) for over-representation, or ov.metabol.msea_gsea(deg, stat_col='stat', n_perm=500) for GSEA-style ranked enrichment.
  4. Plot the result: pathway_bar(...) for raw p-value bars, pathway_dot(...) for the canonical dot plot (size = overlap count, x = odds_ratio or NES, color = p-value).
  5. Cross-check by fitting OPLS-DA, pulling the VIP table, and intersecting padj<0.20 ∧ VIP>1 for a tighter shortlist.

Multi-factor (t_metabol_07)

  1. ASCA — for crossed factor designs (e.g. treatment × time): ov.metabol.asca(adata, factors=['treatment', 'time'], include_interactions=True, n_permutations=500). Returns an ASCAResult with per-effect variance and permutation p-values; result.summary() is a one-row-per-effect DataFrame; asca_variance_bar(result) plots the variance partition.
  2. Mixed models — for repeated measures with random subject effect: ov.metabol.mixed_model(adata, formula='treatment + time', groups='patient', term='treatment[T.drug]'). Per-feature MixedLM fit; uses patsy formula syntax; the term argument selects which fixed-effect coefficient to report.
  3. MEBA — two-group time-series (Hotelling T² across timepoints, Tai 2006): ov.metabol.meba(adata, group_col='group', time_col='time', subject_col='subject'). Detects features whose temporal trajectory differs between groups (interaction-style signal).

DGCA / correlation networks (t_metabol_09)

  1. Run ov.metabol.dgca(adata, group_col='group', group_a, group_b, method='spearman'|'pearson', abs_r_threshold=0.3) for differential correlation between conditions. Returns pairs with r_a, r_b, z_diff, padj, dc_class (the rewiring class — +/+, -/-, +/-, -/+, +/0, ...).
  2. Plot the class breakdown with ov.metabol.dgca_class_bar(dc) — log-scaled counts; reversals (+/-, -/+) are red.
  3. For per-condition networks, run ov.metabol.corr_network(adata, group_col='group', group=<label>, method='spearman', abs_r_threshold=0.5, padj_threshold=0.05) once per condition, then compare edge sets (shared / condition-specific).
  4. Visualize with ov.metabol.corr_network_plot(edges_df, layout='spring', label_font_size=6). For dense graphs, slice the top-N edges first.

Multi-omics MOFA (t_metabol_10)

  1. Build sample-aligned AnnData per view (metabol view + RNA view, same obs_names in same order). Each view is its own AnnData.
  2. Run factors = ov.metabol.run_mofa(views={'metabol': adata_m, 'rna': adata_r}, n_factors=5, outfile='mofa.hdf5', scale_views=True, max_iter=200, seed=0). Returns the factor matrix (samples × retained factors) — MOFA+ prunes inactive factors automatically.
  3. The trained model is persisted to outfile (HDF5); load it with the standard MOFA+ API to get per-view loadings.

MTBLS1 case study (t_metabol_11)

  1. Ingest with ov.utils.load_metabolights('MTBLS1', group_col='Factor Value[Metabolic syndrome]', cache_dir='mtbls1_demo') — auto-downloads from EBI's Metabolights public mirror, joins the MAF with the sample sheet, and writes obs['group'].
  2. Sample-QC (sample_qc + sample_qc_plot) → CV-filter (cv_filter(across='all', cv_threshold=1.5)) → impute → normalize → log → Pareto.
  3. Run the full downstream: differential + volcano, PLS-DA + OPLS-DA + S-plot + VIP, MSEA ORA, ASCA over group + Gender, ROC + biomarker panel, DGCA + correlation network — using human-readable metabolite names from var['metabolite_identification'] for plotting.

Interface Summary

ID mapping (already discussed in untargeted-lipidomics skill for m/z-based annotation; here for name-based ID resolution):

  • ov.metabol.map_ids(names, *, targets=('hmdb', 'kegg', 'chebi'), mass_db=None) → pd.DataFrame indexed by name with columns for each requested target.
  • ov.metabol.normalize_name(name) → str — strip parenthetical synonyms, lowercase, dedupe spaces (used internally; expose for diagnostic queries).

MSEA pathway enrichment:

  • ov.metabol.msea_ora(hits, background, *, pathways=None, min_size=3, mass_db=None) → pd.DataFrame — Fisher's exact test against KEGG (default) or any user-supplied {pathway: [kegg_id, ...]} dict.
  • ov.metabol.msea_gsea(deg, *, stat_col='stat', pathways=None, n_perm=1000, min_size=3, max_size=500, seed=0, mass_db=None) → pd.DataFrame — GSEA-style ranked enrichment via gseapy.prerank. Column names follow vendored gseapy: es, nes, pval, fdr, matched_size, genes, ledge_genes. Pathway term is the DataFrame index, not a column — reset_index() before passing to pathway_dot.
  • ov.metabol.load_pathways() → dict[str, list[str]] (full KEGG pathway database, ~550 pathways, cached on first call).

Multi-factor designs:

  • ov.metabol.asca(adata, *, factors, include_interactions=True, n_components=2, n_permutations=0, layer=None, center=True, seed=0) → ASCAResult. ASCA = ANOVA on each effect → SVD on each effect's mean matrix → variance fraction + per-feature loadings.
  • ASCAResult.effects: dict[str, ASCAEffect] — per-effect data; .summary() → pd.DataFrame (one row per effect: variance_explained, permutation_pvalue, n_components); .residual_ss, .total_ss.
  • ov.metabol.mixed_model(adata, *, formula, groups, re_formula='1', term=None, layer=None) → pd.DataFrame — per-feature statsmodels.MixedLM fit. formula is patsy syntax (e.g. 'treatment + time'); groups is the random-intercept column; term selects the fixed-effect coefficient to report (use coefname[T.level] for categorical contrasts).
  • ov.metabol.meba(adata, *, group_col, time_col, subject_col, groups=None, layer=None) → pd.DataFrame — per-feature Hotelling T² for two-group time-courses (Tai 2006). Detects features whose temporal pattern differs between groups.

DGCA + correlation networks:

  • ov.metabol.dgca(adata, *, group_col, group_a=None, group_b=None, features=None, method='pearson'|'spearman', abs_r_threshold=0.3, layer=None) → pd.DataFrame — pair-level differential correlation; dc_class encodes rewiring direction.
  • ov.metabol.corr_network(adata, *, group_col=None, group=None, method='pearson'|'spearman', abs_r_threshold=0.3, padj_threshold=None, layer=None) → pd.DataFrame — within-condition pairwise correlations passing thresholds; attrs['n_samples'] records the cohort size used.
  • ov.metabol.corr_network_plot(edges_df, *, layout='spring'|'circular'|'kamada_kawai', node_size=70, edge_width_scale=2.5, r_column='r', label_font_size=7, ...).
  • ov.metabol.dgca_class_bar(dc_df, *, log=True, ax=None, figsize=(6, 3.5)) — log-scaled bar chart of DC-class counts.

Plotting helpers (pathway):

  • ov.metabol.pathway_bar(enrichment, *, term_col=None, score_col='pvalue', top_n=15, ax=None, figsize=(6, 5)).
  • ov.metabol.pathway_dot(enrichment, *, term_col=None, size_col='overlap', x_col='odds_ratio', color_col='pvalue', top_n=15, ax=None, figsize=(6.5, 5.5)).
  • ov.metabol.asca_variance_bar(asca_result, *, ax=None, figsize=(5.5, 3)).

Multi-omics MOFA+:

  • ov.metabol.run_mofa(views: dict, *, n_factors=10, outfile='mofa_model.hdf5', scale_views=True, center_groups=True, max_iter=500, convergence_mode='fast', gpu_mode=False, seed=0) → pd.DataFrame. Returns the post-pruning factor matrix; the full model lives in outfile.

MetaboLights ingest:

  • ov.utils.load_metabolights(study_id, *, group_col=None, cache_dir='metabolights_cache', maf_name=None, sample_name_col='Sample Name', refresh=False) → AnnData.

Boundary

Inside scope:

  • All of MSEA (ORA + GSEA) on named metabolites, with KEGG (default) or any user-supplied pathway DB.
  • ASCA, mixed-model, MEBA — the three multi-factor design helpers.
  • DGCA, corr_network, network plotting.
  • MOFA+ joint factorization with metabolomics + any aligned omics view.
  • Metabolights ingest + the MTBLS1 worked example.

Outside scope — separate skill:

  • m/z-based mummichog enrichment for untargeted LC-MS — see the untargeted-lipidomics skill.
  • LION ontology enrichment for lipid species — same skill above.
  • Imputation / normalization / transformation / drift / SERRF / ComBat / sample-outlier QC — preprocessing skill.
  • PLS-DA / OPLS-DA fitting itself (the case study uses them; the skill referenced is omicverse-bulk-metabol-multivariate, not this one).
  • Biomarker panel + nested CV — multivariate skill.
  • Drug-perturbation Cox / signature scoring — out of scope for ov.metabol.

Branch Selection

MSEA — ORA vs GSEA

  • ORA when you have a clear hit/non-hit cutoff (e.g. padj<0.20) and a moderate background (~50–500 metabolites). Outputs an odds ratio + p-value per pathway.
  • GSEA when the signal is distributed across many small effects rather than concentrated in a few big hits. Pass the full DEG table with stat_col='stat' (or 'log2fc') — no cutoff. Outputs NES + permutation p / FDR per pathway. The vendored gseapy.prerank puts pathway names in the index; use reset_index() before plotting.
  • Sample sizes too small for either? Skip MSEA — small-cohort metabolomics has too few hits for either test to calibrate.

map_ids target selection

  • ('hmdb', 'kegg', 'chebi') is the canonical trio; 'pubchem' and 'lipidmaps' are also supported.
  • Coverage on the shipped lookup is partial — for the cachexia 63-metabolite NMR set, most resolve; for novel LC-MS features, expect 60–80 % unresolved. Pre-fetch mass_db=ov.metabol.fetch_chebi_compounds() and pass it to widen the resolver.

Multi-factor — ASCA vs MixedLM vs MEBA

  • asca for crossed factor designs with replicates (e.g. treatment × time × subject), where you want a global variance partition plus per-feature loadings. Requires balanced data; permutation p-values calibrate effect significance.
  • mixed_model for repeated measures (same subjects measured in multiple conditions) — provides per-feature p-values for any fixed-effect coefficient. Random subject intercept handles within-subject correlation.
  • meba for two-group time-courses where the question is "which features behave differently over time between groups". Single-test, Hotelling T²-based.
  • Three groups with no time? Use anova from the preprocessing skill.

DGCA — Spearman vs Pearson

  • Spearman is the default for metabolomics: it tolerates the non-linear, non-Gaussian distributions typical of NMR / MS data.
  • Pearson only when the features are confirmed approximately log-normal (after log transform, this is reasonable for many platforms — but verify with QQ plots before trusting Pearson p-values).
  • abs_r_threshold filters weak correlations on both sides — pairs are retained only if max(|r_a|, |r_b|) >= threshold. Tighter threshold = fewer but stronger rewiring candidates.

corr_network thresholds

  • For network visualization: abs_r_threshold=0.5, padj_threshold=0.05 keeps the figure readable.
  • For network enumeration / shared-edge analysis: abs_r_threshold=0.3, padj_threshold=0.05 retains more structure for set arithmetic.
  • Always pair abs_r_threshold with padj_threshold — a high |r| on small n can be noise.

MOFA — n_factors and scale_views

  • n_factors=10 is a reasonable starting point — MOFA+ prunes inactive factors during fitting, so over-asking is cheap.
  • scale_views=True (default) z-scores each view independently — necessary when views have different units (intensity vs counts vs scaled).
  • convergence_mode='fast' for prototyping; 'medium' / 'slow' for reportable runs.
  • gpu_mode=True only if you've installed mofapy2 with CUDA support — typically not needed for the small cohorts metabolomics studies use.

Metabolights ingest

  • group_col='Factor Value[<phenotype>]' — the canonical Metabolights factor column convention.
  • cache_dir writes to disk; subsequent runs reuse cached files. Set refresh=True to bypass.
  • maf_name only needed when a study ships multiple MAFs (e.g. positive- and negative-mode runs).

Input Contract

  • For all four workflows, start from a preprocessed AnnData: obs['group'] populated, X is log-transformed. Use a Pareto-scaled copy for ASCA (which is variance-partitioning and benefits from Pareto), but keep a log-only copy for differential / roc_feature / dgca / mixed_model.
  • For MOFA: every view's AnnData has identical obs_names in the same order. The function will raise on misalignment.
  • For Metabolights ingest: an internet connection (first call) and write access to cache_dir. Subsequent calls work offline.
  • For ASCA / mixed_model: every factor in factors/formula must exist as an obs column with the right dtype (categorical for ASCA factors; categorical or numeric for MixedLM).
  • For corr_network: avoid running on the full 500+ metabolite matrix without thresholding — the pair count grows quadratically.

Minimal Execution Patterns

# MSEA — ORA + GSEA on the cachexia data
import omicverse as ov
import matplotlib.pyplot as plt
ov.plot_set()

adata = ov.metabol.read_metaboanalyst('human_cachexia.csv', group_col='Muscle loss')
adata = ov.metabol.normalize(adata, method='pqn')
adata = ov.metabol.transform(adata, method='log')

deg = ov.metabol.differential(adata, group_col='group',
                              group_a='cachexic', group_b='control',
                              method='welch_t', log_transformed=True)

# Optional ID coverage check
ids = ov.metabol.map_ids(adata.var_names.tolist())
print(f'{(ids["kegg"] != "").sum()} / {len(ids)} metabolites resolve to KEGG')

# ORA
hits = deg[deg.padj < 0.20].index.tolist()
background = deg.index.tolist()
ora = ov.metabol.msea_ora(hits, background, min_size=3)
ov.metabol.pathway_bar(ora, score_col='pvalue', top_n=10)
ov.metabol.pathway_dot(ora, size_col='overlap', x_col='odds_ratio',
                       color_col='pvalue', top_n=10)

# GSEA — pathway names are in the INDEX
gsea = ov.metabol.msea_gsea(deg, stat_col='stat', n_perm=500, seed=0)
gsea_plot = gsea.reset_index().rename(columns={gsea.index.name or 'index': 'pathway'})
ov.metabol.pathway_dot(gsea_plot, size_col='matched_size', x_col='nes',
                       color_col='pval', top_n=10)
plt.show()

# Multi-factor — ASCA, mixed model, MEBA
asca_res = ov.metabol.asca(
    adata, factors=['treatment', 'time'],
    include_interactions=True,
    n_components=2, n_permutations=500, seed=0,
)
print(asca_res.summary())
ov.metabol.asca_variance_bar(asca_res); plt.show()

mlm = ov.metabol.mixed_model(
    adata,
    formula='treatment + time',
    groups='patient',
    term='treatment[T.drug]',
)
print(mlm.sort_values('pvalue').head())

# Time-series: needs subject_col for repeated measures within group
meba_tbl = ov.metabol.meba(
    adata_ts, group_col='group',
    time_col='time', subject_col='subject',
)
print(meba_tbl.sort_values('pvalue').head())

# DGCA + per-condition correlation networks
dc = ov.metabol.dgca(adata, group_col='group',
                     group_a='cachexic', group_b='control',
                     method='spearman', abs_r_threshold=0.3)
ov.metabol.dgca_class_bar(dc); plt.show()

edges_a = ov.metabol.corr_network(adata, group_col='group', group='cachexic',
                                  method='spearman', abs_r_threshold=0.5,
                                  padj_threshold=0.05)
edges_b = ov.metabol.corr_network(adata, group_col='group', group='control',
                                  method='spearman', abs_r_threshold=0.5,
                                  padj_threshold=0.05)

a_keys = set(edges_a.apply(lambda r: frozenset((r['feature_a'], r['feature_b'])), axis=1))
b_keys = set(edges_b.apply(lambda r: frozenset((r['feature_a'], r['feature_b'])), axis=1))
print(f'shared edges: {len(a_keys & b_keys)}')
print(f'A-only:       {len(a_keys - b_keys)}')

ov.metabol.corr_network_plot(edges_a.head(40), figsize=(7, 6), label_font_size=6)
plt.show()

# Multi-omics MOFA — both views must share obs_names in the same order
factors = ov.metabol.run_mofa(
    views={'metabol': adata_m, 'rna': adata_r},
    n_factors=5,
    outfile='mofa_demo.hdf5',
    scale_views=True,
    max_iter=200,
    seed=0,
)
print(f'retained {factors.shape[1]} factors after MOFA+ pruning')
print(factors.head())

# MTBLS1 case study — full pipeline
adata = ov.utils.load_metabolights(
    'MTBLS1',
    group_col='Factor Value[Metabolic syndrome]',
    cache_dir='mtbls1_demo',
)

qc = ov.metabol.sample_qc(adata, n_components=3, alpha=0.95)
ov.metabol.sample_qc_plot(qc); plt.show()

adata = ov.metabol.cv_filter(adata, across='all', cv_threshold=1.5)
adata = ov.metabol.impute(adata, method='qrilc', seed=0)
adata = ov.metabol.normalize(adata, method='pqn')
adata_log = ov.metabol.transform(adata, method='log')
adata_pareto = ov.metabol.transform(adata_log, method='pareto', stash_raw=False)

# Univariate + volcano on log-only
deg = ov.metabol.differential(adata_log, group_col='group',
                              group_a='diabetes mellitus', group_b='Control Group',
                              method='welch_t', log_transformed=True)
ov.metabol.volcano(deg, padj_thresh=0.05, log2fc_thresh=0.5, label_top_n=8)

# Multivariate on Pareto
opls = ov.metabol.opls_da(adata_pareto, group_col='group',
                          group_a='diabetes mellitus', group_b='Control Group',
                          n_ortho=1)
mb_names = adata_pareto.var['metabolite_identification'].values
ov.metabol.vip_bar(opls, mb_names, top_n=15)

# MSEA on names
mass_db = ov.metabol.fetch_chebi_compounds()
hit_names = adata.var.loc[deg[deg['padj']<0.05].index, 'metabolite_identification'].tolist()
bg_names = adata.var['metabolite_identification'].dropna().tolist()
enr = ov.metabol.msea_ora(hit_names, bg_names, mass_db=mass_db)
ov.metabol.pathway_bar(enr, top_n=10)

# Multi-factor: phenotype + sex
asca_res = ov.metabol.asca(adata_pareto,
                           factors=['group', 'Factor Value[Gender]'],
                           include_interactions=True, n_permutations=100, seed=0)
ov.metabol.asca_variance_bar(asca_res)

# DGCA + network
dc = ov.metabol.dgca(adata_log, group_col='group',
                     group_a='diabetes mellitus', group_b='Control Group',
                     method='spearman')
ov.metabol.dgca_class_bar(dc)

edges_t2d = ov.metabol.corr_network(adata_log, group_col='group',
                                    group='diabetes mellitus',
                                    method='spearman',
                                    abs_r_threshold=0.5, padj_threshold=0.05)
named = adata.var['metabolite_identification'].to_dict()
edges_plot = edges_t2d.copy()
edges_plot['feature_a'] = edges_plot['feature_a'].map(named).fillna(edges_plot['feature_a'])
edges_plot['feature_b'] = edges_plot['feature_b'].map(named).fillna(edges_plot['feature_b'])
ov.metabol.corr_network_plot(edges_plot.head(40), figsize=(7, 6), label_font_size=6)

Validation

  • After msea_ora / msea_gsea: an empty result is a signal, not an error — log the cohort size and min_size/n_perm so the user knows whether to relax thresholds. For ORA, also report background size; with a small background (<50 metabolites), the test is underpowered.
  • After msea_gsea: pathway names are in the index, not a column. Code that does gsea['pathway'] will KeyError; use gsea.reset_index().
  • After asca: result.summary() reports per-effect variance_explained and permutation_pvalue. A residual variance fraction > 0.5 suggests the model misses dominant signal; revisit the factors. Permutation p < 0.05 is the canonical effect-significance bar.
  • After mixed_model: the term argument must match a coefficient exactly — for categorical, that's coefname[T.level] (e.g. 'treatment[T.drug]'); typos return all-NaN results.
  • After meba: validate on a synthetic interaction-only signal that the planted features rank above non-interaction features (the tutorial does this — replicate before trusting on novel data).
  • After dgca: dc_class='+/-' and '-/+' are the strongest rewiring; '+/+' and '-/-' are concordant (no rewiring); '0/0' is no signal in either group. Use the dgca_class_bar to gut-check the breakdown.
  • After corr_network: attrs['n_samples'] records the per-condition cohort size — use this for power calibration, not adata.n_obs.
  • After run_mofa: confirm factors.shape[1] < n_factors (MOFA+ pruned at least one factor) — if not, the model didn't converge on a parsimonious solution; rerun with convergence_mode='medium'.
  • For the MTBLS1 case study: the log + Pareto matrices serve different downstream stages — passing the wrong one inflates fold-changes (Pareto into differential) or degrades Q² (log-only into opls_da). Keep both copies.

Resource Map

  • See reference.md for compact copy-paste snippets per workflow.
  • See references/source-grounding.md for verified _msea / _multifactor / _correlation / _integration / _id_mapping signatures and the corr_network_plot / asca_variance_bar docstring backfill log.
  • For preprocessing chain that produces the input AnnData, see omicverse-bulk-metabol-preprocessing.
  • For PLS-DA / OPLS-DA / VIP / S-plot / biomarker panel cited in the MTBLS1 case study, see omicverse-bulk-metabol-multivariate.
  • For untargeted m/z-based pathway inference (mummichog) and lipidomics LION enrichment, see omicverse-bulk-metabol-untargeted-lipidomics.

Examples

  • "Run MSEA ORA at padj<0.20 on the cachexia DEG, plot a pathway dot plot, and cross-validate with GSEA on the same DEG ranking."
  • "Treatment × time crossed design: run ASCA with 500 permutations and plot the variance partition."
  • "Repeated-measures study: per-feature MixedLM with formula='treatment + time' and groups='patient', report the treatment[T.drug] coefficient."
  • "Detect metabolite pairs whose Spearman correlation rewires between cachexic and control (DGCA) and plot the per-condition correlation networks."
  • "Joint MOFA+ on metabolomics + RNA-seq with 5 factors, scale_views=True, save the model to mofa_demo.hdf5."
  • "Reproduce the MTBLS1 worked example end-to-end: ingest, sample-QC, CV-filter, full preprocessing, multivariate, MSEA, ASCA, biomarker panel, DGCA, correlation network."

References