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Fig. 3 | BMC Bioinformatics

Fig. 3

From: ROIMCR: a powerful analysis strategy for LC-MS metabolomic datasets

Fig. 3

Schematic illustration of input (a) and output variables (b) of an ROI searching, filtering and compression algorithm. Data of the LC-MS chromatogram is described as a {m × 1} cell array (named as peaks), with m cells (equal to the number of retention times), each of them containing two vectors (of variable length among cells), corresponding to the m/z and intensity values acquired by the instrument at each of the retention times. Peaks and vector time (m × 1) are the input variables of ROI function together with the parameters required to define one ROI (thresh = 750, mzerror = 0.05 and minroi = 10 are used in this example), resulting in a data matrix, a data vector and a cell array (MSROI, mzroi and roicell, respectively) after ROI search. ROI (n) is the total number of ROIs obtained (in the example of the figure, nROI = 297). MSROI is a (m x ROI (n)) matrix, containing the MS spectra of every retention time in its rows, and the chromatograms of every ROI in its columns, mzroi is a vector containing mean m/z values of ROIs and roicell is a {ROI (n) × 5} cell array, containing ROI (n) × 5 cells (in the example of the figure it would be 297 × 5 = 1485). Cells comprised in roicell variable from column 1 to column 4 contain single vectors in their structures (containing information of m/z, retention times, intensities and scan number of the data enclosed in the same ROI, respectively) whereas cells comprised in the fifth column (roicell {ROI (n),5}) contain single values (corresponding to mean m/z values of ROI)

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