Ccs calibration polynomial fitting

ccs-calibration-polynomial-fitting

Summary

Fit a polynomial calibration model to convert ion mobility drift times and reference m/z values into collision cross section (CCS) coefficients using the single-field calibration equation. This skill produces calibration coefficients (beta and tfix) that enable downstream CCS prediction for unknown ions in positive- or negative-mode ion mobility spectrometry.

When to use

Apply this skill when you have acquired tunemix or reference standard data in ion mobility spectrometry with known m/z, drift time, and CCS values, and you need to establish a drift-time-to-CCS mapping for a specific instrument, ionization mode (positive or negative), and buffer gas. The skill is necessary as the first step in any CCS-based feature characterization workflow.

When NOT to use

• Do not use this skill if reference CCS standards are not available or not accurately characterized; the calibration will be unreliable.
• Do not use this skill if drift time and m/z data have not been properly preprocessed (e.g., baseline-subtracted, aligned) or contain significant instrumental noise; garbage input produces garbage calibration.
• Do not use this skill if switching between ionization modes or buffer gases without recalibrating; calibration coefficients are mode- and gas-specific.

Inputs

• tunemix reference m/z values (numpy array or pandas Series)
• tunemix experimental drift times (numpy array or pandas Series, units: milliseconds)
• known CCS values for reference standards (numpy array, units: Ų)
• charge states (integer array or scalar)
• buffer gas mass (float, atomic mass units)
• calibration mode identifier (string: 'positive' or 'negative')

Outputs

• calibration coefficient beta (float)
• calibration coefficient tfix (float)
• R-squared goodness-of-fit metric (float, range 0–1)
• fitted calibration model object (DEIMoS calibration model)

How to apply

Load reference tunemix data (m/z, drift_time, known CCS values, charge states) from an HDF5 file using deimos.load(). Call deimos.calibration.calibrate_ccs() with the reference m/z and drift_time arrays, specifying the calibration mode (positive or negative), known CCS standards, and buffer gas mass. The function applies the single-field calibration equation (Stow et al. 2017) to solve for calibration coefficients beta and tfix. Extract the returned R-squared metric and polynomial coefficients. Validate that the R-squared value meets the threshold of ≥0.9999; values below this indicate poor model fit and may signal data quality issues, incorrect reference standards, or instrument miscalibration.

Related tools

• DEIMoS (Python API providing calibrate_ccs() function and load() utility for HDF5 file I/O; applies single-field calibration equation to compute beta and tfix coefficients) — https://github.com/pnnl/deimos
• numpy (Numerical array operations for m/z, drift_time, and CCS value handling during calibration)
• Python (Execution environment and scripting language for DEIMoS API calls and calibration workflow)

Examples

tune_pos = deimos.load('example_tune_pos.h5', key='ms1'); calibration = deimos.calibration.calibrate_ccs(tune_pos['mz'].values, tune_pos['drift_time'].values, known_ccs, charges, buffer_gas_mass, mode='positive')

Evaluation signals

• R-squared metric ≥ 0.9999 (threshold from paper validation examples; values <0.99 indicate poor fit)
• Returned beta and tfix coefficients are finite, positive numbers and match expected physical ranges for the instrument and buffer gas
• Calibration model can be applied to independent test m/z and drift_time pairs and produce CCS predictions within ±5% of known standard values (typical IMS accuracy)
• Residual plot of predicted vs. observed drift times shows random scatter around zero with no systematic bias across m/z or drift_time ranges
• Model coefficients are reproducible when recalibrated with the same reference data (deterministic function)

Limitations

• Calibration is specific to a single ionization mode (positive or negative) and buffer gas; switching modes requires recalibration.
• Accuracy depends critically on the quality and accuracy of reference CCS standards; contaminated or mislabeled standards will corrupt the model.
• The single-field calibration equation assumes ideal gas behavior and may not account for instrumental nonlinearities or space-charge effects at high ion currents.
• Extrapolation beyond the m/z and drift_time ranges covered by reference standards is not reliable; predictions are most trustworthy within the calibration domain.

Evidence

• [other] The calibrate_ccs routine accepts reference m/z values, known CCS values, charge states, experimental drift times, and buffer gas mass, then applies the single-field calibration equation to compute calibration coefficients beta and tfix: "The calibrate_ccs routine accepts reference m/z values, known CCS values, charge states, experimental drift times, and buffer gas mass, then applies the single-field calibration equation to compute"
• [other] yielding calibration model fit with r-squared of 0.9999784552958134: "yielding calibration model fit with r-squared of 0.9999784552958134"
• [other] Load tunemix positive-mode reference data (m/z, drift_time, known CCS values) from example_tune_pos.h5 using deimos.load(): "Load tunemix positive-mode reference data (m/z, drift_time, known CCS values) from example_tune_pos.h5 using deimos.load()"
• [other] confirm the reported R-squared is ≥0.9999 (threshold criterion from paper examples): "confirm the reported R-squared is ≥0.9999 (threshold criterion from paper examples)"
• [readme] Functionality includes feature detection, feature alignment, collision cross section (CCS) calibration, isotope detection, and MS/MS spectral deconvolution: "Functionality includes feature detection, feature alignment, collision cross section (CCS) calibration, isotope detection, and MS/MS spectral deconvolution"
• [readme] algorithm implementations simultaneously utilize all dimensions to (i) offer greater separation between features, thus improving detection sensitivity: "algorithm implementations simultaneously utilize all dimensions to (i) offer greater separation between features, thus improving detection sensitivity"