Orbitrap Mass Spectrometry Isotope Ratio Correction Data for Metabolic Flux Analysis

A machine learning correction algorithm for isotope ratio measurements in metabolic labeling studies using Orbitrap mass spectrometry. The dataset, authored by Zhenwen Yu and last updated in May 2026, contains results from a random forest model that reduces measurement error for mass isotopomers M1 and M2. It is stored in an 81.0 KB XLSX file.

Use Cases

• Correcting isotope ratio measurement bias in untargeted metabolomics based on the described scan-by-scan machine learning method.
• Improving accuracy of metabolic flux measurements from heavy water labeling studies based on the described correction model.
• Ranking factors impacting measurement bias in Orbitrap data based on the model's feature importance output.
• Enabling high-throughput stable isotope labeling experiments for fluxomics based on the algorithm's ability to handle a wide m/z range.

Strengths

• The described random forest model reduces mean absolute percentage error for the M1 mass isotopomer from 21.3% to 3.5%.
• The model reduces mean absolute percentage error for the M2 mass isotopomer from 25.8% to 3.1%.
• The dataset identifies the ion signal intensity/TIC ratio as the most dominant bias-causing factor.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

figshare

Collection Method

Data likely originates from mass spectrometry experiments and the application of a machine learning correction algorithm.

Time Range

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Freshness

Last updated 2026-05-04 14:57:14; freshness should be verified.

Geography

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