Mrm data format ingestion

mrm-data-format-ingestion

Summary

Load and standardize vendor-specific MRM lipidomics export files (TSV, CSV) into a structured pandas DataFrame with validated, normalized column headers and data types. This is the entry point for downstream lipid nomenclature parsing, matching, and statistical analysis in MRM workflows.

When to use

You have raw MRM lipidomics export files in vendor-specific formats (e.g., TSV or CSV from a mass spectrometry instrument) and need to convert them into a standardized tabular format before performing lipid identification, statistical analysis, or visualization. Apply this skill when raw exports contain inconsistent or vendor-specific column naming conventions and before any downstream matching or nomenclature decomposition.

When NOT to use

• Input is already a validated, standardized feature table with consistent column schemas and confirmed numeric types.
• Data has already been parsed into a structured tabular format by upstream vendor software or prior workflow step.
• Raw export format is not tabular (e.g., binary mzML or NetCDF files that require dedicated parsers).

Inputs

• Raw MRM export file (vendor-specific TSV or CSV format)
• Vendor format specification or sample export file
• Optional: column mapping configuration (header name aliases)

Outputs

• Cleaned, structured CSV table with standardized headers
• One row per lipid feature per sample
• Data validation report (missing values, type mismatches, malformed IDs)

How to apply

Load the raw MRM export file into a pandas DataFrame using the appropriate delimiter (TSV or CSV). Extract and standardize column headers by normalizing names to a consistent schema: retention time, m/z, intensity, lipid ID, and sample identifier. Validate data integrity by checking for missing values, confirming that m/z and intensity columns are numeric types, and flagging rows with malformed lipid identifiers. Handle vendor-specific format quirks (e.g., extra header rows, non-standard delimiters) as preprocessing steps before standardization. Output the cleaned, structured table as CSV with one row per lipid feature per sample, ensuring all rows pass validation.

Related tools

• pandas (Load, parse, standardize column headers, and validate data types in TSV/CSV exports)
• Python (Language for implementing data ingestion and validation logic) — github.com/chopralab/CLAW

Examples

import pandas as pd
df = pd.read_csv('raw_mrm_export.csv')
df.columns = df.columns.str.lower().str.strip()
df = df[['retention_time', 'm_z', 'intensity', 'lipid_id', 'sample_id']]
df[['m_z', 'intensity']] = df[['m_z', 'intensity']].apply(pd.to_numeric, errors='coerce')
df.to_csv('cleaned_mrm_data.csv', index=False)

Evaluation signals

• All rows successfully loaded into pandas DataFrame with no I/O errors.
• Column headers match the standardized schema (retention time, m/z, intensity, lipid ID, sample identifier); no vendor-specific or extra columns remain in core fields.
• m/z and intensity columns have numeric dtype (float or int); no string values present in these columns.
• No rows with missing values in required fields (m/z, intensity, lipid ID); missing values are either imputed or rows are flagged/removed with justification logged.
• Lipid ID column contains no malformed entries (e.g., truncated IDs, special characters outside expected nomenclature); validation report documents any flagged rows.

Limitations

• Vendor-specific export formats may have undocumented or inconsistent header row counts, delimiters, or encoding; preprocessing logic must be tailored per instrument vendor.
• Column name standardization assumes a fixed set of required fields; exports missing one or more core fields (e.g., no retention time) cannot be parsed and must be rejected or require manual mapping.
• No mechanism provided in the workflow description for handling multi-sheet Excel files (.xlsx) or binary formats; CSV/TSV ingestion only.
• Malformed lipid identifiers are flagged but not auto-corrected; manual curation or downstream regex-based repair is required before nomenclature decomposition.

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] 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"
• [readme] Organize your lipidomics project by creating a project folder. This directory will serve as the central location for all project-related files, including raw data, processed results, plots, and other data files.: "Organize your lipidomics project by creating a project folder. This directory will serve as the central location for all project-related files"