# Vendor Export Schema Mapping

> Use when raw MRM lipidomics data arrives in vendor-specific export formats (e.g., Sciex, Waters, Thermo TSV/CSV) with inconsistent or proprietary column naming, numeric encoding, and lipid nomenclature.

- Skill: `holobiomicslab/vendor-export-schema-mapping` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/vendor-export-schema-mapping`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/vendor-export-schema-mapping/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/vendor-export-schema-mapping

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# vendor-export-schema-mapping

## Summary

Transform vendor-specific raw MRM lipidomics export files (TSV, CSV, vendor proprietary formats) into a standardized tabular schema by extracting, validating, and normalizing column headers and data types. This enables downstream matching, statistical analysis, and visualization workflows.

## When to use

Raw MRM lipidomics data arrives in vendor-specific export formats (e.g., Sciex, Waters, Thermo TSV/CSV) with inconsistent or proprietary column naming, numeric encoding, and lipid nomenclature. Apply this skill when you must ingest heterogeneous vendor outputs into a unified pipeline that expects consistent column headers (retention time, m/z, intensity, lipid ID, sample identifier) and validated numeric types before matching or statistical analysis.

## When NOT to use

- Input is already a validated, standardized feature table with normalized headers and decomposed lipid nomenclature fields.
- Data originates from a standardized, open format (e.g., mzML) that does not require vendor-specific schema mapping.
- Lipid nomenclature is already parsed into separate structured fields in the input.

## Inputs

- vendor-specific MRM export file (TSV or CSV format)
- sample identifier mapping (optional, for enriching with sample metadata)
- lipid nomenclature patterns or reference (for decomposition)

## Outputs

- cleaned, standardized CSV table with normalized column headers
- one row per lipid feature per sample
- decomposed lipid fields (class, chain composition, modification state)
- data quality report flagging missing or malformed rows

## How to apply

Load the vendor export file into a pandas DataFrame using appropriate delimiters (TSV or CSV). Extract and standardize column headers by mapping vendor-specific names to canonical names (e.g., 'RetTime' → 'retention_time', 'MZ' → 'm/z', 'Area' → 'intensity'). Validate data integrity by checking for missing values in critical columns, confirming that m/z and intensity columns are numeric (not strings), and parsing lipid nomenclature into decomposed fields (lipid class, chain composition, modification state) using regex or string-split operations. Flag rows with malformed lipid identifiers or out-of-range values. Output the cleaned, structured table as CSV with one row per lipid feature per sample, ready for downstream processing.

## Related tools

- **pandas** (DataFrame loading, column header extraction, standardization, and data validation for vendor export schema mapping)
- **Python** (Implementation language for regex parsing, string-split operations, and data integrity validation of lipid nomenclature)

## Examples

```
import pandas as pd; df = pd.read_csv('vendor_export.tsv', sep='\t'); df.rename(columns={'RetTime': 'retention_time', 'MZ': 'm/z', 'Area': 'intensity'}, inplace=True); df[['m/z', 'intensity']] = df[['m/z', 'intensity']].apply(pd.to_numeric); df.to_csv('standardized_lipids.csv', index=False)
```

## Evaluation signals

- All critical columns (retention_time, m/z, intensity, lipid_id, sample_id) are present and have expected names after standardization.
- m/z and intensity columns have numeric dtype (float or int), not string; no conversion errors or NaN artefacts introduced.
- Lipid nomenclature is successfully decomposed into separate fields (lipid_class, chain_composition, modification_state) with no rows dropped due to regex mismatch.
- No missing values in critical columns; any rows with missing values are flagged in a data quality report.
- Output CSV row count matches expected sample × feature count; spot-check several rows to confirm header alignment and value preservation.

## Limitations

- Vendor export format must be TSV or CSV; proprietary binary formats require vendor-specific parsing libraries not addressed in this workflow.
- Lipid nomenclature parsing relies on regex or string-split heuristics; non-standard or ambiguous lipid identifiers in the vendor export may fail to decompose correctly and must be manually reviewed or flagged.
- No automated detection of vendor format variant; user must specify or infer correct delimiter, encoding, and header row index from the raw file.
- Schema mapping assumes a fixed set of expected columns; vendors with completely novel or missing column types may require ad-hoc extension.

## Evidence

- [other] Load raw MRM export file (vendor-specific format, e.g., TSV or CSV) into a pandas DataFrame.: "Load raw MRM export file (vendor-specific format, e.g., TSV or CSV) into a pandas DataFrame."
- [other] Extract and standardize column headers (retention time, m/z, intensity, lipid ID, sample identifier).: "Extract and standardize column headers (retention time, m/z, intensity, lipid ID, sample identifier)."
- [other] Parse lipid nomenclature using regex or string-split operations to decompose lipid class, chain composition, and modification state into separate fields.: "Parse lipid nomenclature using regex or string-split operations to decompose lipid class, chain composition, and modification state into separate fields."
- [other] Validate data integrity: check for missing values, confirm numeric types for m/z and intensity columns, and flag rows with malformed lipid identifiers.: "Validate data integrity: check for missing values, confirm numeric types for m/z and intensity columns, and flag rows with malformed lipid identifiers."
- [readme] streamline various tasks such as data parsing, matching, statistical analysis, and visualization: "streamline various tasks such as data parsing, matching, statistical analysis, and visualization"