HolobiomicsLab collision-cross-section-bias-quantification
Summary
Quantify systematic bias in measured collision cross section (CCS) values relative to a reference library by comparing ion mobility-mass spectrometry measurements of U¹³C-labeled internal standard lipids against their known theoretical CCS values. This enables detection of instrument drift or calibration error before applying correction.
When to use
Apply this skill when you have ion mobility-mass spectrometry lipidomics data from samples spiked with U¹³C-labeled lipid internal standards (fully labeled yeast extract) and want to assess whether measured CCS values systematically deviate from a validated DT CCS N₂ reference library, indicating bias that may affect downstream lipid quantification or identification.
When NOT to use
- Input data lacks U¹³C-labeled internal standards or does not include heavy-labeled lipids marked in the Label column — bias cannot be calculated without reference standards
- Reference library values are unavailable or do not cover the lipid classes detected in your sample
- CCS values have already been corrected by the instrument vendor — bias quantification is most meaningful on raw, uncorrected measured data
Inputs
- CSV file with measured IM-MS data (columns: File, LipidClass, LipidSpecies, Adduct, Label, CCS)
- DT CCS N₂ reference library for U¹³C-labeled lipids (CSV format)
- Requirement: Data must include U¹³C-labeled lipids marked as 'heavy' in Label column
Outputs
- CCS bias table (%) per lipid-adduct combination
- Mean and standard deviation of bias per lipid class-adduct pair
- Bias visualization figures (scatter plots, violin plots by lipid class)
- HTML and PDF reports summarizing bias statistics and distribution
- RData file storing all bias calculation results as an R list
How to apply
Load measured CCS values for U¹³C-labeled lipids from your analysis output as a CSV file (with columns: File, LipidClass, LipidSpecies, Adduct, Label, CCS), and pair each measured value with its corresponding theoretical CCS from the DT CCS N₂ library for U¹³C labeled lipids. Calculate bias as percentage deviation: [(measured CCS − library CCS) / library CCS] × 100 for each lipid-adduct pair, then stratify bias statistics by lipid class-adduct combination. The MobiLipid R Markdown automates this calculation and produces visualizations (bias distributions, mean bias per class) to identify systematic shifts. Bias values near zero indicate good agreement; systematic positive or negative shifts suggest instrument calibration drift requiring correction or recalibration.
Related tools
- MobiLipid (R Markdown workflow that automates CCS bias calculation, generates correction functions, and produces HTML/PDF reports with bias statistics and visualizations) — https://github.com/FelinaHildebrand/MobiLipid
- R (Statistical computing environment in which the MobiLipid markdown executes bias calculations and rendering)
Examples
rmarkdown::render('MobiLipid_CCS-bias-calculation.Rmd', output_format = c('html_document', 'pdf_document'), output_file = c('MobiLipid_CCS-bias-calculation_mydata.html', 'MobiLipid_CCS-bias-calculation_mydata.pdf'))
Evaluation signals
- Bias values for U¹³C-labeled lipids should cluster tightly around zero (or a small systematic offset); wide or asymmetric distributions suggest instrument instability or miscalibration
- Bias statistics should be stratified by lipid class-adduct combination and show consistency within each class (homogeneous bias suggests class-specific systematic error rather than random noise)
- Minimum of 3 U¹³C-labeled lipids per lipid class-adduct combination should be present; classes with fewer lipids will show larger confidence intervals
- Cross-check: bias values calculated from raw measured CCS and library CCS should match those output in the CCS_bias_no_correction CSV file
- Visual inspection of bias plots (scatter or violin) should reveal whether bias is random (scatter around zero) or systematic (consistent positive or negative shift)
Limitations
- CCS bias calculation and correction requires a minimum of 3 U¹³C-labeled lipids per lipid class-adduct combination; lipid classes with fewer detections cannot be corrected and will show large statistical uncertainty
- The DT CCS N₂ reference library is specific to U¹³C-labeled yeast extract; bias quantification applies only to those internal standards; correction of unlabeled analytes is indirect and depends on linear regression quality
- Linear regression-based correction functions assume a linear relationship between measured and true CCS; non-linear drift or adduct-specific calibration issues may not be fully captured
- Bias quantification is most reliable for lipid classes well-represented in the reference library; lipid classes with sparse or missing library entries will yield unreliable bias estimates
Evidence
- [readme] MobiLipid eliminates the need to measure additional external calibration besides vendor specific calibration requirements by internal standardization.: "MobiLipid eliminates the need to measure additional external calibration besides vendor specific calibration requirements by internal standardization"
- [other] Bias assessment results (bias factors and statistics per lipid class) are loaded from the upstream bias assessment step and quantify CCS deviation.: "Load bias assessment results (bias factors and statistics per lipid class) from the upstream bias assessment step"
- [readme] Bias is calculated between measured CCS values and library values of U¹³C labeled lipids using linear regression.: "to first calculate CCS bias between measured CCS values and library values of U13C labeled lipids"
- [readme] Minimum of 3 lipids per lipid class-adduct combination is required for effective CCS bias calculation.: "CCS correction functions are based on linear regression functions which require a minimum of 3 lipids within a lipid class-adduct combination"
- [readme] Bias calculation output includes CCS bias before correction, and visualizations stratified by lipid class.: "CCS bias calculation without CCS correction [with] CCS bias table [and] CCS bias figure"