Raw LC-HRMS data were converted to m/z extensible markup language (.mzXML) in centroid mode using MSConvert ProteoWizard (release version 3.0.9393). Peak detection and integration were performed using R version 3.3.2 and XCMS package version 3.0.2 [62]. Briefly, features were detected using the centWave algorithm (step = 0.01, m/z deviation tolerance = 10 ppm, peak width = 10–40 s for C18, 20–120 s for HILIC, signal-to-noise ratio = 5). Peaks were grouped by density and retention times were nonlinearly smoothed (loess). Missing values (gap filling) were imputed by the chrom method. Annotation of adducts, fragments, and isotopes was achieved using the CAMERA package [63].

Features detected following XCMS-CAMERA analysis were then filtered and standardized using the Workflow4Metabolomics platform [64,65]. Data filtering was done according to the following criteria: (i) correlation coefficient between dilution factor and peak area in QC samples > 0.7, (ii) ratio of mean peak area in blanks over biological samples < 0.33, and (iii) variation coefficient of peak area in QC samples <30%. Peak intensities were then normalized using the Probabilistic Quotient Normalization (PQN) algorithm described by Dieterle et al. [66].

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