# Class Conditional Calibration Mapping

> Use when you have TWIM-MS experimental data (arrival times and ion mobility parameters) paired with pre-assigned biomolecular class labels for an ion population, and you need to obtain class-conditioned CCS values without first performing feature-level identification.

- Skill: `holobiomicslab/class-conditional-calibration-mapping` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/class-conditional-calibration-mapping`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/class-conditional-calibration-mapping/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/class-conditional-calibration-mapping

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# class-conditional-calibration-mapping

## Summary

Compute collision cross section (CCS) values for ions stratified by biomolecular class assignment, using class-specific reference standards to calibrate drift-time-to-CCS relationships without requiring prior feature identification. This enables direct CCS calculation from TWIM-MS arrival time data conditioned on experimental class labels.

## When to use

Apply this skill when you have TWIM-MS experimental data (arrival times and ion mobility parameters) paired with pre-assigned biomolecular class labels for an ion population, and you need to obtain class-conditioned CCS values without first performing feature-level identification. This is especially valuable in high-dimensional multi-omic workflows where class assignment precedes feature detection.

## When NOT to use

- Input ions lack pre-assigned biomolecular class labels or class assignment is unavailable.
- Reference standard calibration sets are not available or defined for the classes present in your data.
- Raw uncalibrated TWIM data has not been processed to extract arrival times and ion mobility parameters.

## Inputs

- Biomolecular class assignments (ion identifier → class label mapping)
- TWIM-MS experimental data: arrival times and ion mobility parameters
- Class-specific reference standard calibration data

## Outputs

- Ion identifier table with class labels and class-conditioned CCS values

## How to apply

Partition the ion population by assigned class label. For each class stratum, extract arrival time measurements and convert to drift time by subtracting the time-of-flight offset. Apply the appropriate reference standard calibration set for that biomolecular class to establish a class-specific drift-time-to-CCS mapping function. Compute CCS for each ion using its calibrated relationship. Output a table mapping ion identifiers to both their class labels and class-conditioned CCS values. The rationale is that different biomolecular classes (e.g., lipids, peptides, carbohydrates) may have different ion behavior in TWIM, so using class-matched calibration standards improves accuracy over global calibration.

## Related tools

- **MOCCal** (Python application that implements class-specific CCS calibration workflow, partitions ions by class label, and computes class-conditioned CCS values from TWIM-MS data) — https://github.com/HinesLab/MOCCal
- **DEIMoS** (Dependency for MOCCal_RawDT.py; required to preprocess raw calibration files before class-conditional calibration) — http://github.com/pnnl/deimos

## Evaluation signals

- Class-stratified CCS outputs have no missing or null values for ions assigned to defined classes.
- CCS values fall within expected ranges for their assigned biomolecular class (e.g., lipids typically 200–600 Ų; peptides 400–1200 Ų).
- Class-specific calibration R² or RMSE values meet acceptable thresholds (article does not specify exact cutoffs, but calibration fit should be documented).
- Ion count per class matches the input partition; no ions are dropped or misallocated during class-conditional processing.
- CCS values computed from the same drift time differ only if ions are assigned to different classes, demonstrating class-conditional differentiation.

## Limitations

- Requires pre-existing biomolecular class assignments; skill cannot perform de novo class discovery or inference.
- Accuracy depends on availability and quality of class-specific reference standard calibration sets; missing or low-quality standards for a class will degrade CCS computation for that class.
- TWIM platforms record arrival time (time at detector) rather than drift time (time in TWIM cell); conversion requires accurate time-of-flight offset, which must be independently determined.
- No changelog or version history provided in the repository, making it difficult to track improvements, bug fixes, or methodological changes over time.

## Evidence

- [other] MOCCal performs experimental class-specific CCS calculations as a workflow component that operates on biomolecular class assignments, enabling CCS computation without requiring prior feature identification.: "MOCCal performs experimental class-specific CCS calculations as a workflow component that operates on biomolecular class assignments, enabling CCS computation without requiring prior feature"
- [other] Partition the ion population by assigned class label. For each class stratum, extract arrival time measurements and convert to drift time by subtracting the time-of-flight offset. Apply class-specific CCS calibration using the appropriate reference standard set for that biomolecular class.: "Partition the ion population by assigned class label. For each class stratum, extract arrival time measurements and convert to drift time by subtracting the time-of-flight offset. Apply"
- [readme] TWIM platforms record the time at which the ion reaches the detector (arrival time) rather than the time an ion spends within the TWIM cell (drift time).: "TWIM platforms record the time at which the ion reaches the detector (arrival time) rather than the time an ion spends within the TWIM cell (drift time)."
- [intro] MOCCal offers class assignment and CCS calculations without need for identifying the features first.: "MOCCal offers class assignment and CCS calculations without need for identifying the features first."
- [intro] Functionality includes collision cross section (CCS) calibration, experimental data biomolecular class assignment, and experimental class-specific CCS calculations.: "Functionality includes collision cross section (CCS) calibration, experimental data biomolecular class assignment, and experimental class-specific CCS calculations."