2DMS is a data-independent analysis technique that fragments every precursor in a mixture at once, without chromatographic separation. Each acquisition gives a 2D spectrum that links every product ion to its precursor. I developed statistical methods to classify samples from these spectra, model them, and simulate them.
Challenges
- Few samples, high dimensionality. Each spectrum has hundreds of millions of dimensions, but as an early technique, we only have access to a limited number of real-world samples.
- Artefacts. The 2D spectra contain artefacts that bias simple approaches such as thresholding and peak picking. They follow from how the instrument works, so a model can account for them and use the information they carry.
- Instrument settings differ between runs. Samples run under different settings can differ for that reason alone, not because of their chemistry.
- No ground truth. The true composition of a real mixture is usually unknown, so there is no reference answer to check a method against.
- Novel technique. Verdel’s 2026 paper was the first experimental demonstration of 2DMS on a QToF instrument, so there are no established statistical or machine learning models for these data.
Workstreams
Classification
Statistical and machine learning methods that classify samples from their 2D spectra, and match unknown samples to reference samples. A comparison of standard methods, with recommended defaults and experimental designs.
These methods automatically and accurately predict properties such as the country of origin of food samples, including for specimens not used in training.
Modelling
Fitting statistical and machine learning models allows us to account for artefacts in the data, improve the resolving power, identify false peaks, place uncertainty on estimates, and diagnose machine settings in real time.
Simulation
A simulator that generates realistic 2DMS data with known ions, used to test how well processing and peak-detection methods find and separate them.