Similar protocols

Pipeline publication

[…] bottom glass tubes with screw caps and sealed. The tubes were then heated and stirred at 100°C for 1h. NEFAs were derivatized to their fatty acid methyl esters (FAMEs). The organic layer was recovered and analysed directly by GC-MS after neutralisation with aqueous potassium carbonate solution. GC-MS data were acquired with an Agilent GC-MS system in the splitless mode. An RESTEK Rtx®-2330 column (90% biscyanopropyl/10% phenylcyanopropyl polysiloxane) was installed in the system. The column temperature was computer controlled and was ramped from 45°C to 215°C in over 65 mins. Data pre-processing was performed in the Agilent MassHunter suit (version 8 of Qualitative Workflows and Profinder), Metabolite Detector (version 2.5), and AMDIS (Automated Mass Spectral Deconvolution and Identification System) (version 2.72), and the accuracy of data extraction of these software tools was compared. Data was further processed and analysed with five different normalisation methods (CRMN, EigenMS, PQN, SVR and LOWESS). The performance of the normalisation methods and the marker candidates identified were investigated. PCA was performed with EZinfo (version 3.0.3). Multilevel PCA was performed using mixOmics (version 6.1.3). Pareto scaling was used in PCA and mPCA modelling. RLA plots were drawn with the RlaPlots function of the package metabolomics (version 0.1.4). ROC was calculated with the colAUC function of caTools (version 1.17.1). Binomial logistic regression was performed with the glm function of R (version 3.3.3)., Normalisation is typically performed post-analytically (i.e., data normalisation). Data normalisation can be categorised as (1) internal standard (IS)-ba […]

Pipeline specifications

Software tools Metabolite Detector, AMDIS, EigenMS