What does this strategy enable?
QC-RLSC fits local curves to QC intensities and interpolates correction across study injections. It links experimental QC placement with computational drift estimation and subsequent quality assessment.
Transferable workflow
Workflow
- 1
Interleave representative QC injections
Retain injection order and batch labels with study and QC measurements.
- 2
Prepare aligned feature intensities
The documented LC-MS implementation uses XCMS for preprocessing.
- 3
Fit QC correction curves
Cross-validate feature-wise LOESS smoothing and interpolate across injections.
- 4
Correct and assess reliability
Normalize responses and inspect QC repeatability before combining batches.
Connected resources
Tools used in this strategy
Strategy profile
Components
Data acquisition, Preprocessing and feature extraction, Quality control, Statistical analysis
Scope and considerations
Designed for
- Analytical drift correction when representative QC injections span the run.
Important requirements
- Repeated QC observations with usable feature signals and recorded injection order.
Considerations
- Sparse or unrepresentative QCs cannot reliably characterize every feature.
- Check overfitting and corrected QC performance; correction does not fix experimental confounding.
Reusable resources
Publication
Dunn WB, Broadhurst D, Begley P, et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols 6, 1060–1083 (2011).