Vendor export schema mapping

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"