Omicverse Bulk Metabol Untargeted Lipidomics

OmicVerse Bulk Metabolomics — Untargeted LC-MS & Lipidomics

Goal

Take an LC-MS peak table (m/z + RT-coded features) or a lipidomics matrix (LIPID MAPS shorthand var-names) into an AnnData, then run the two ID-aware downstream paths — peak annotation + mummichog pathway inference for untargeted metabolomics, lipid-class parsing + class aggregation + LION enrichment for lipidomics. The output is a list of pathway / ontology hits backed by the empirical feature p-values, plus a class-aggregated lipid matrix when requested.

This skill assumes the upstream preprocessing chain (impute / normalize / log) has already run; for the chain see the omicverse-bulk-metabol-preprocessing skill. ID mapping for named metabolites (HMDB / KEGG / ChEBI ID resolution) and MSEA pathway enrichment are in the pathway-multifactor skill.

Quick Workflow

Untargeted LC-MS path (t_metabol_04)

1. Load the LC-MS peak table with ov.metabol.read_lcms(..., feature_id_sep=...) so m/z and RT get parsed into var['m_z'] and var['rt'].
2. Apply preprocessing (PQN + log) — don't impute zeros for LC-MS (zeros = below-detection).
3. Run differential to get a per-peak p-value vector.
4. Untargeted LC-MS often has thousands of peaks vs. low n — BH-FDR is too strict to discriminate. For the volcano, pass use_pvalue=True and clip the log2fc axis to keep below-detection extremes from blowing the figure.
5. Enrich pathways from m/z + p-value with ov.metabol.mummichog_basic(...) — a permutation-based pathway test that handles the FDR burden internally.
6. Optionally inspect candidate compound annotations per peak with ov.metabol.annotate_peaks(...) — adduct-resolved KEGG candidates by ppm tolerance.
7. For sanity checks: build a synthetic m/z set from a known KEGG pathway (e.g. via fetch_chebi_compounds() + load_pathways()), seed it with low p-values, and confirm mummichog recovers the seeded pathway.

Lipidomics path (t_metabol_05)

1. Build the AnnData directly from a wide CSV — ov.metabol.read_wide(...) or anndata.AnnData(df) then attach clinical metadata.
2. Sanity-check the parser on a few var_names: ov.metabol.parse_lipid('PC 34:1') returns a LipidIdentity (lipid_class, total_carbons, total_db, backbone).
3. Run ov.metabol.annotate_lipids(adata) — adds var['lipid_class'], var['total_carbons'], var['total_db']. Unparseable rows get NaN; drop them before downstream stats.
4. (Optional) ov.metabol.aggregate_by_class(adata, agg='sum') for a class-level matrix (n_vars = n_lipid_classes) — useful for class-resolved QC and parsimonious modelling.
5. Run differential on the cleaned (non-NaN) species-level matrix to get per-lipid hits.
6. Enrich LION lipid-ontology terms from the hit list with ov.metabol.lion_enrichment(hits, background, min_size=2). Term categories include lipid class, function, fatty-acid composition.

Interface Summary

LC-MS ingest (creates AnnData with var['m_z'], var['rt']):

• ov.metabol.read_lcms(path, *, feature_id_sep='/', sample_col=None, group_col=None, label_row=None, transpose=True) → AnnData. Pass the actual separator your file uses ('__' for the malaria demo, '/' for many MS-DIAL exports).

Peak annotation:

• ov.metabol.annotate_peaks(mz, *, polarity='positive'|'negative', ppm=10.0, custom_adducts=None, mass_db=None) → pd.DataFrame with peak_idx, mz, adduct, kegg, name, mass_diff_ppm. Returns one row per (peak, candidate compound).
• ov.metabol.mummichog_basic(mz, pvalue, *, polarity='positive', ppm=10.0, significance_cutoff=0.05, n_perm=1000, min_overlap=2, pathways=None, mass_db=None, seed=0) → pd.DataFrame with pathway, overlap, set_size, pvalue, padj. Pure-Python port — input is the mz array and the per-peak pvalue array (not a DEG DataFrame).
• ov.metabol.mummichog_external(mz, pvalue, retention_time=None, *, mode='pos'|'neg', significance_cutoff=0.05, outdir=None, **kwargs) → pd.DataFrame — thin wrapper over Li's reference mummichog PyPI package; install with pip install mummichog for full LIBSDB / activity-network features. Polarity uses mode='pos'/'neg' here (not polarity='positive'/'negative' like mummichog_basic accepts) because the upstream Mummichog class names it that way. **kwargs is forwarded into the upstream package's config, so extras the mummichog CLI accepts (e.g. instrument, ppm, network-scoring knobs) can be passed through there.
• ov.metabol.load_pathways() → dict[str, list[str]] — {pathway_name: [kegg_id, ...]}. Default fetches full KEGG (550 pathways), cached under `/.cache/omicverse/metabol/`.
• ov.metabol.fetch_chebi_compounds() → pd.DataFrame with chebi, name, formula, mw (monoisotopic), kegg, hmdb. Cached after first call (~54k compounds). Pass back into annotate_peaks(mass_db=...) / mummichog_basic(mass_db=...) to avoid re-fetching.
• ov.metabol.fetch_kegg_pathways(organism=None) → dict — direct KEGG REST fetch.

Lipidomics ingest + parsing:

• ov.metabol.parse_lipid(name: str) → Optional[LipidIdentity] — returns None for unparseable names so the caller can filter.
• LipidIdentity dataclass: lipid_class, total_carbons, total_db, backbone, raw; convenience methods is_saturated() and is_polyunsaturated(threshold=2).
• ov.metabol.annotate_lipids(adata, *, feature_names=None) → AnnData (returns a copy; adds var['lipid_class'], var['total_carbons'], var['total_db']).
• ov.metabol.aggregate_by_class(adata, *, agg='sum'|'mean') → AnnData — class-level totals matrix.
• ov.metabol.lion_enrichment(hits, background, *, min_size=3) → pd.DataFrame — over-representation across LION lipid-ontology terms.
• ov.metabol.fetch_lion_associations() → dict — full LION ontology (cached); pass to lion_enrichment(ontology=...).

Recognized lipid classes (canonical, extensible — see LIPID_CLASSES in _lipidomics.py):

• Glycerophospholipids: PC, PE, PS, PG, PI, PA and lyso variants LPC, LPE, LPS, LPG, LPI, LPA.
• Sphingolipids: SM, Cer, GlcCer, LacCer, Hex2Cer, Hex3Cer.
• Glycerolipids: TAG, TG, DAG, DG, MAG, MG.
• Sterols / fatty acids: CE, FA, Chol, BMP.

Boundary

Inside scope:

• LC-MS ingest with m/z + RT parsing (read_lcms).
• m/z + p-value → mummichog (basic and external backends).
• Per-peak adduct annotation against ChEBI/KEGG.
• Lipid name parsing (LIPID MAPS shorthand sum-composition level).
• Lipid-class aggregation matrix.
• LION ontology enrichment.

Outside scope — separate skill:

• Imputation / normalization / transformation (preprocessing skill — but don't impute LC-MS zeros, that's enforced upstream).
• HMDB / KEGG / ChEBI ID mapping for named metabolites (ov.metabol.map_ids — pathway-multifactor skill).
• MSEA ORA / GSEA on named metabolites with KEGG / Reactome / WikiPathways (pathway-multifactor skill).
• Multivariate PLS-DA / OPLS-DA / VIP — the multivariate skill works fine on lipidomics matrices once aggregate_by_class (optional) and Pareto-scale (mandatory) have run.
• Targeted lipidomics with full sn-1/sn-2 species resolution — current parser is sum-composition only.

Branch Selection

mummichog_basic vs. mummichog_external

• mummichog_basic — pure-Python port; ships with KEGG-only pathway DB; reproducible with seed. Use for portable runs and CI.
• mummichog_external — wraps the upstream mummichog PyPI package (Li 2013 reference implementation). Adds full adduct table, activity-network scoring, LIBSDB / SMPDB pathways. Use when you need publication-grade multi-DB enrichment and have pip install mummichog available.

use_pvalue=True on volcano for untargeted LC-MS: with thousands of peaks vs. <20 samples, BH-FDR effectively zeros out everything. The honest visualization is raw p-value; mummichog's permutation null then absorbs the FDR burden at the pathway level. Set clip_log2fc=8.0 (or similar) so a few below-detection zeros don't blow the x-axis.

ppm for adduct matching: 10.0 is the typical Orbitrap / TOF tolerance. Tighten to 5 ppm for high-resolution Orbitrap; loosen to 20 ppm for older Q-TOF data. Wider ppm = more candidates per peak but also more false positives.

polarity must match the ionization mode of the run. Mixed-polarity datasets need split processing (positive ions through polarity='positive', negative through 'negative', then merge results) — there's no auto-detect.

significance_cutoff for mummichog: default 0.05 picks peaks at raw p<0.05 as the "hit" set. For very small cohorts (n<10/group), consider 0.10. The pathway p-values are still calibrated by n_perm, so loosening the per-peak cutoff doesn't double-count.

n_perm for mummichog: 1000 for reportable runs (gives 0.001-resolution pathway p-values). For interactive exploration 200–500 is fine. The cost is linear: each permutation re-shuffles peak labels and re-tabulates pathway hits.

min_overlap for mummichog / LION: 2 is the floor (single-overlap pathways are noise); 3 is canonical for publication. Higher means fewer but more reliable hits.

Lipidomics — class vs. species level

• Run differential on the species-level (cleaned) matrix when you need fine-grained chain-length / saturation differences.
• Run on the aggregate_by_class matrix when you want class-level summaries (e.g. "PC up, PE down") — a parsimonious model with fewer multiple-testing corrections.
• LION enrichment expects species-level names in the hit list, not aggregated classes — feed it deg.index from the species-level test.

Caching — for any LC-MS run that calls mummichog more than once (e.g. positive + negative), explicitly fetch pathways = ov.metabol.load_pathways() and mass_db = ov.metabol.fetch_chebi_compounds() once, then pass pathways=pathways, mass_db=mass_db into each call. The functions auto-fetch on first use but the network round-trip is slow.

Input Contract

• LC-MS ingest: file is a wide CSV with feature names that encode m/z and RT separated by feature_id_sep (e.g. 212.0345__3.45 for 212.0345 m/z, 3.45 RT). The reader writes var['m_z'] and var['rt'] as floats; obs[group] from label_row if provided.
• For mummichog_basic/_external: mz and pvalue are 1-D arrays of equal length. mz is in Daltons, not Th. Bad m/z values (NaN / non-positive) are silently dropped — confirm len(mz) == adata.n_vars before passing.
• Lipidomics ingest: var_names must look like 'PC 34:1', 'Cer d18:1/24:0', 'TAG 54:3', 'LPC 18:0', 'TAG 54:3;O' (the trailing ;O for oxidized species is recognized). Anything outside the LIPID_CLASSES registry parses to NaN and is dropped.
• After aggregate_by_class, the class-level AnnData carries the original obs only if you copy it manually (class_adata.obs = adata.obs); the function focuses on X and var only.

Minimal Execution Patterns

# Untargeted LC-MS — full path
import omicverse as ov
import numpy as np
ov.plot_set()

adata = ov.metabol.read_lcms(
    'malaria_feature_table.csv',
    feature_id_sep='__',
    label_row='Label',
    transpose=True,
)

# Preprocess: PQN + log (DON'T impute LC-MS zeros)
adata_p = ov.metabol.normalize(adata, method='pqn')
adata_p = ov.metabol.transform(adata_p, method='log')

# Differential — per-peak p-values are the mummichog input
deg = ov.metabol.differential(
    adata_p, group_col='group',
    group_a='Semi_immue', group_b='Naive',
    method='welch_t', log_transformed=True,
)

# Volcano on raw p-values + clipped log2fc — the honest untargeted view
fig, ax = ov.metabol.volcano(deg, padj_thresh=0.01, log2fc_thresh=2.0,
                             label_top_n=0, use_pvalue=True, clip_log2fc=8.0)

# Annotate peaks against KEGG via [M+H]+ adducts
ann = ov.metabol.annotate_peaks(adata.var['m_z'].values,
                                polarity='positive', ppm=10.0)

# Mummichog pathway enrichment — pre-fetch DBs to avoid re-network
pathways = ov.metabol.load_pathways()
mass_db = ov.metabol.fetch_chebi_compounds()
mumm = ov.metabol.mummichog_basic(
    mz=adata.var['m_z'].values,
    pvalue=deg['pvalue'].values,
    polarity='positive', ppm=10.0,
    significance_cutoff=0.05,
    n_perm=1000, min_overlap=2,
    mass_db=mass_db, pathways=pathways,
    seed=0,
)
mumm.head(10)[['pathway', 'overlap', 'set_size', 'pvalue', 'padj']]

# Lipidomics — full path
import pandas as pd, anndata as ad

# Build AnnData (replace with your loader; brca demo uses raw CSVs)
matrix = pd.read_csv('brca_matrix.csv', index_col=0)
clin   = pd.read_csv('brca_clin.csv',  index_col=0)
adata = ad.AnnData(matrix.T)             # samples × lipids
adata.obs = clin.loc[adata.obs_names].copy()
adata.obs['group'] = adata.obs['Group']  # rename to canonical 'group'

# Sanity-check the parser
for name in ['PC 34:1', 'TAG 54:3', 'Cer d18:1/24:0', 'LPC 18:0']:
    print(name, '→', ov.metabol.parse_lipid(name))

# Annotate the matrix
adata = ov.metabol.annotate_lipids(adata)
print(adata.var['lipid_class'].value_counts())

# Class-level totals (optional)
class_adata = ov.metabol.aggregate_by_class(adata, agg='sum')
class_adata.obs = adata.obs

# Differential at the species level (drop unparseable first)
adata_clean = adata[:, adata.var['lipid_class'].notna()].copy()
deg = ov.metabol.differential(
    adata_clean, group_col='group',
    group_a='Cancer', group_b='Benign',
    method='welch_t', log_transformed=True,
)
fig, ax = ov.metabol.volcano(deg, padj_thresh=0.10, log2fc_thresh=0.3, label_top_n=8)

# LION enrichment from species-level hits
hits = deg[deg.padj < 0.10].index.tolist()
background = deg.index.tolist()
lion = ov.metabol.lion_enrichment(hits, background, min_size=2)
lion.head(10)[['term', 'category', 'overlap', 'set_size',
               'odds_ratio', 'pvalue', 'padj']]

Validation

• After read_lcms: 'm_z' in adata.var.columns and 'rt' in adata.var.columns; both are floats. If not, feature_id_sep was wrong.
• Before mummichog: len(adata.var['m_z']) == len(deg) and the orderings match (the function consumes positionally — feed adata.var['m_z'].values and deg.loc[adata.var_names, 'pvalue'].values if deg may have been re-ordered).
• Mummichog returning an empty frame is a result, not an error — log it with the cohort size and min_overlap so the user knows to relax thresholds.
• Sanity-test mummichog by feeding a synthesized hit set drawn from a known KEGG pathway (TCA in the tutorial); the seeded pathway should appear at the top.
• After annotate_lipids: var['lipid_class'].notna().sum() should account for most of the matrix; if half the matrix is NaN, the data isn't using LIPID MAPS shorthand and you need a different ingest.
• After aggregate_by_class: class_adata.n_vars equals the number of distinct lipid_class values; class_adata.X.sum(axis=1) should equal adata_clean.X.sum(axis=1) when agg='sum' (within float tolerance).
• LION enrichment: report empirical p-values with min_size>=2 only; single-member hits are uninformative.

Resource Map

• See reference.md for copy-paste-ready snippets per workflow.
• See references/source-grounding.md for verified _mummichog / _lipidomics / _fetchers signatures and the LipidIdentity docstring backfill log.
• For ID mapping of named metabolites and MSEA pathway enrichment, see the omicverse-bulk-metabol-pathway-multifactor skill.
• For preprocessing chain and LC-MS-specific drift / SERRF / ComBat, see the omicverse-bulk-metabol-preprocessing skill.
• For multivariate / biomarker analysis on lipidomics class- or species-level matrices, see the omicverse-bulk-metabol-multivariate skill.

Examples

• "Load malaria_feature_table.csv LC-MS data with feature_id_sep='__', run PQN+log, do differential for Semi_immue vs Naive, and run mummichog on the m/z + pvalue with 1000 permutations."
• "Annotate peaks against KEGG via [M+H]+ adducts at 10 ppm, then sanity-check mummichog by seeding TCA-cycle compounds with low p-values and confirming TCA shows up at the top."
• "Parse the brca_matrix.csv lipidomics matrix, annotate every lipid species, drop unparseable, run Cancer-vs-Benign differential, then LION-enrich the padj<0.10 hits."
• "Aggregate the lipidomics matrix to class-level totals and propagate the clinical metadata."

References

• Tutorial notebooks:
  • t_metabol_04_untargeted.ipynb — malaria plasma LC-MS, mummichog.
  • t_metabol_05_lipidomics.ipynb — lipidr BRCA demo, LION.
• Live API verified against omicverse.metabol — see references/source-grounding.md.