ROIMCR theory

The ROIMCR approach allows to:

i) filter and compress massive LC-MS (or LCxLC-MS, CE-MS, GC-MS, MSI, …) datasets while transforming their original structure into a data matrix of features without losing relevant information through the search of regions of interest (ROIs) in the m/z domain.

ii) resolve compressed data to identify their contributing pure components without previous alignment or peak shaping by applying a Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) analysis.

Therefore, this methodology combines the benefits of data filtering and compression based on the searching of ROI features, without the loss of spectral accuracy. The method has the benefits of the application of the powerful MCR-ALS data resolution method without the necessity of performing chromatographic peak alignment or modelling.

More details can be found in the open acces manuscript:

Gorrochategui, E., Jaumot, J. & Tauler, R. ROIMCR: a powerful analysis strategy for LC-MS metabolomic datasets. BMC Bioinformatics 20, 256 (2019).