---
name: corrected-intensity-table-validation
description: Use when after applying one or more intensity drift correction strategies (Internal Standard correction, statistical drift correction, custom or weighted bracketing) within QuantyFey and before exporting the corrected intensity table for final quantification.
license: CC-BY-4.0
metadata:
  edam_operation: http://edamontology.org/operation_3435
  edam_topics:
  - http://edamontology.org/topic_3520
  - http://edamontology.org/topic_0121
  tools:
  - QuantyFey
derived_from:
- doi: 10.1016/j.aca.2025.344571
  title: quantyfey
evidence_spans: []
claims: []
provenance:
  collection: https://w3id.org/holobiomicslab/asb-skill/collection/metabolomics/v1
  assembled_by: scripts/collect_metabolomics_collection.py
  sources:
  - build: coll_quantyfey
    doi: 10.1016/j.aca.2025.344571
    title: quantyfey
  dedup_kept_from: coll_quantyfey
schema_version: 0.2.0
---

# corrected-intensity-table-validation

## Summary

Validation of mass spectrometry intensity tables after drift correction to ensure removal of drift artifacts and preservation of quantification accuracy. This skill verifies that the corrected intensity data is suitable for downstream quantitative analysis.

## When to use

After applying one or more intensity drift correction strategies (Internal Standard correction, statistical drift correction, custom or weighted bracketing) within QuantyFey and before exporting the corrected intensity table for final quantification. Use this skill when intensity drifts have been observed during measurement and corrective methods have been applied to the raw MS intensity table.

## When NOT to use

- Input is raw, uncorrected MS intensity data — apply drift correction first using QuantyFey before validation.
- Intensity drifts have not been detected or are not suspected in the dataset — validation is unnecessary if no correction has been applied.
- Data origin is from non-mass-spectrometry sources or data format is incompatible with QuantyFey output (e.g., already processed feature tables from other pipelines).

## Inputs

- raw MS intensity table (before drift correction)
- corrected MS intensity table (after drift correction applied in QuantyFey)
- metadata describing sample sequence and measurement order

## Outputs

- validation report confirming drift removal
- quality metrics for corrected intensity data
- approved corrected intensity table in standard MS quantification format

## How to apply

Load the corrected intensity table generated by QuantyFey's drift correction module and perform visual and statistical inspection to confirm drift artifacts have been removed. Compare corrected intensities against the original raw intensity table to verify that correction has not introduced spurious values or distorted the signal distribution. Check that intensity values remain within expected ranges for the analytes of interest and that the pattern of intensities across the sample sequence no longer exhibits systematic drift (e.g., monotonic increase or decrease over measurement time). Validate that internal standards or calibration reference peaks show stable intensities across the sequence, indicating effective drift compensation. Confirm that the corrected table maintains the original number of detected features and samples, and that no data have been lost during the correction process.

## Related tools

- **QuantyFey** (Shiny application that performs intensity drift correction and generates the corrected intensity table to be validated) — https://github.com/CDLMarkus/QuantyFey

## Evaluation signals

- Corrected intensity table shows no systematic trend (monotonic drift) when intensities are plotted against measurement order or sample sequence position.
- Internal standard or calibration reference peak intensities are stable across the sample sequence with reduced coefficient of variation compared to the raw table.
- Comparison of raw vs. corrected intensity distributions shows that correction has reduced outliers consistent with drift artifacts without introducing new anomalies.
- Data integrity checks pass: no missing values introduced, sample count and feature count match the input table, and intensity values fall within biochemically plausible ranges for the analyte class.
- If multiple correction strategies were applied, the selected corrected table shows the best visual alignment between observed and expected intensity patterns based on the bracketing or regression model used.

## Limitations

- QuantyFey is compatible with Windows operating systems only for the standalone version; validation must be performed on compatible systems or via the Apptainer version for Linux/macOS.
- Validation relies on visual inspection and statistical metrics; no automated pass/fail threshold is provided by QuantyFey, so expert judgment is required to interpret correction quality.
- Correction effectiveness depends on the appropriateness of the chosen drift correction strategy (Internal Standard, statistical model, or bracketing method) for the specific measurement conditions; mismatched strategy selection may result in over- or under-correction that validation alone cannot fully detect.
- The README notes no changelog is available, limiting traceability of changes to validation methods or correction algorithms across software versions.

## Evidence

- [other] QuantyFey offers multiple correction strategies as a mechanism to address intensity drifts in mass spectrometry datasets and ensure accurate quantification.: "QuantyFey offers multiple correction strategies as a mechanism to address intensity drifts in mass spectrometry datasets and ensure accurate quantification."
- [other] Generate corrected intensity table with drift artifacts removed. Export corrected intensity data in standard MS quantification format.: "Generate corrected intensity table with drift artifacts removed. Export corrected intensity data in standard MS quantification format."
- [intro] It is specifically designed to address intensity drifts in datasets, offering multiple correction strategies to ensure accurate quantification.: "It is specifically designed to address intensity drifts in datasets, offering multiple correction strategies to ensure accurate quantification."
- [readme] Especially when Intensity Drift is observed during the Measurement, the app provides the user with tools to effectively handle these drift.: "Especially when Intensity Drift is observed during the Measurement, the app provides the user with tools to effectively handle these drift."