Markerlynx xs application manager

PCA is one of the statistical analysis techniques used for finding the difference between different sample groups [25 (link), 26 (link)]. In the study, data were achieved using Waters MassLynx™ Software v. 4.1 connected to MarkerLynx™ XS Application Manager (Waters Corporation, Milford, USA). A series of procedures in the software were used to handle a retention time of 0.1 min, the chromatogram from 0.2 to 25 min, the mass range of 100–1500 Da, the mass window of 0.02 Da, and the noise elimination level of 10.00%. All compounds identified in G. elata samples before and after processing using ginger juice were distinguished via PCA and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) through the MarkerLynx XS Application Manager, as shown in Figure 1. The model has 3 score components for the samples: G. elata raw material, dried G. elata, and G. elata processed with GEP5D. These scores display a good separation for the three groups. The scores t[1] (x-axis) and t[2] (y-axis) are key variables in summarizing and separating the data. Each point in the plot corresponds to an observation. The groups are shown in different colors. This variation was assuredly caused by the use of the “Jiangchang Bang” method on G. elata in this study.

All of the MS data were analyzed primitively using the Waters Markerlynx™ XS Application Manager provided with the MassLynx software. Markerlynx Application Manager was employed for data processing including the following steps: detecting chromatographic peak, maximizing ions locating maximizing ions, assembling ion data into a matrix. Markerlynx preprocessed data was imported to EZinfo 2.0 software (Waters Corporation, Manchester, UK) for principal component analysis (PCA) and orthogonal projection to latent structures discriminant analysis (OPLS-DA). The potential markers were extracted from S-Plots constructed following OPLS analysis based on their contribution to the variation and correlation. And HMDB (http://www.hmdb.ca/), MetaboAnalyst (http://www.metaboanalyst.ca/), and LIPID MAPS (http://www.Lipidmaps.org/tools/index.html) were used to identify the selected potential biomarkers.
Additional statistical analysis was conducted in SPSS version 17.0. Data are presented as mean ± standard error of the mean (SEM). Welch’s t test was used to test whether these parameters differed between DCM and controls. Correlation studies were done using Pearson Correlation test when appropriate. Values of P < 0.05 were considered statistically significant.