The resulting MS data were first processed by the MarkerLynx Applications Manager version 4.1 (Waters Corp., Manchester, UK). This process included integration, normalization and alignment the intensities of peaks, and then give a list of m/z and retention time with corresponding intensities for each metabolites from every sample in the positive data set. The processed data list was then imported to SIMCA-P software package (v13.0, Umetric, Umea°, Sweden) for PCA and OPLS-DA. The PCA method was carried out to investigate whether each group can be separated and to find out their metabolic distinction. The OPLS-DA was used to pick out discriminating ions contributing to the classification among the experimental samples, and the results were visualized in the form of score plots to display the group clusters and S-plot to show variables contributing to the classification. In the OPLS-DA model, the variables responsible for differentiating antibiotic group and control group were selected as potential biomarkers of the diseases progression by the variable importance of project (VIP) value and the S-plot statistics.
Markerlynx applications manager version 4
Manufactured by Waters Corporation
Sourced in United Kingdom, United States
MarkerLynx Applications Manager version 4.1 is a software application designed for the analysis and management of data generated from liquid chromatography-mass spectrometry (LC-MS) systems. The software provides tools for peak detection, compound identification, and data visualization.
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Lab products found in correlation
Simca p software version 12, by Sartorius (2 mentions)
Simca p 14, by Sartorius (1 mentions)
Simca p software 12, by Sartorius (1 mentions)
Calcusyn, by Biosoft (1 mentions)
Spss 13, by IBM (1 mentions)
Simca p 12, by Sartorius (1 mentions)
Spss v16, by IBM (1 mentions)
Simca p 11, by Sartorius (1 mentions)
7500 fast system, by Thermo Fisher Scientific (1 mentions)
9 protocols using markerlynx applications manager version 4
1
Metabolomic Analysis of Antibiotic Impact
2
Multivariate Analysis of Metabolomic Profiles
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The raw data were preprocessed by Marker Lynx Applications Manager version 4.1 (Waters, Manchester, UK), which includes integration, normalization peak and alignment. A list of intensities, including retention time and m/z data pairs were imported into SIMCA-P 14.1 software (Umetrics, Umeå, Sweden) for multivariate pattern recognition analysis, which includes principal component analysis (PCA) and partial least squares (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA). The S-plot was used to observe the variables that contributed to the classification. The key variables were screened based on the variable importance in projection (VIP) values > 1 and p-values < 0.05 in order to assess the predictive abilities of the constructed OPLS-DA model by seven-fold cross-validation.
The key metabolites were identified by the m/z with HMDB (http://www.hmdb.ca ) (accessed on 2 July 2022), and MassBank (http://www.massbank.jp ) (accessed on 12 July 2022). The comprehensive metabolic network map was based on Kyoto Encyclopedia of Genes and Genomes (KEGG) (https://www.genome.jp/kegg/pathway.htm ) (accessed on 4 August 2022), and MetaboAnalyst5.0 (https://www.metaboanalyst.ca/ ) (accessed on 10 August 2022).
The key metabolites were identified by the m/z with HMDB (
3
Metabolomic Data Analysis Workflow
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The raw mass data were normalized to total intensity (area) and analyzed using the MarkerLynx Applications Manager version 4.1 (Waters, Manchester, UK). The parameters included a retention time range of 4.0–19.0 min, a mass range from 200 Da to 1,500 Da, and a mass tolerance of 0.04 Da. The isotopic data were excluded, the noise elimination level was 10, and the mass and retention time windows were 0.04 min and 0.1 min, respectively. After creating a suitable processing method, the dataset was processed through the Create Dataset window. The resulting two-dimensional matrix for the measured mass values and intensities for each sample was further exported to SIMCA-P+ software 12.0 (Umetrics, Umeå, Sweden) using both unsupervised principal component analysis and supervised OPLS-DA.
4
Multivariate Analysis of UPLC-QTOFMS Data
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The UPLC-QTOFMS ESI+ and ESI− data were analyzed by the MarkerLynx Applications Manager version 4.1 (Waters Corp.). Multivariate analysis was performed using SIMCA-P software version 12.0 (Umetrics AB, Umeå, Sweden). The combinative effects were determined by the computer software CalcuSyn (Biosoft, Cambridge, UK). Variance between different groups was determined by two-tailed Student's t-test or Mann–Whitney U-test. Association between different factors or parameters was determined by χ2 test. P<0.05 was considered significant. Values were presented as mean±s.e.m. (N=3 unless otherwise noted in the Figure legends). Statistical analyses were performed on SPSS 13.0 software (IBM Corp., Armonk, NY, USA).
5
Multivariate statistical analysis of UPLC-MS data
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We used MarkerLynx Applications Manager Version 4.1 (Waters, Milford, MA, USA) to detect, integrate and normalize the intensities of the peaks to the sum of peaks within the raw UPLC-MS data of the sample. A multivariate dataset based on the retention time, m/z and signal intensity of the peaks was acquired, and multivariate statistical analysis was carried out using statistical software. Principal components analysis (PCA) and partial least squares discriminate analysis (PLS-DA) were carried out using SIMCA-P+ 12.0 software (Umetrics AB, Sweden). Correlation coefficients were obtained with Matlab Version 7.8 (R2009a) software, and heat maps were created using Cluster 3.0 software (University of Tokyo, Human Genome Center) and Java TreeView 1.1.6r2 software (http://jtreeview.sourceforge.net/ ). Detailed multivariate statistical analysis was carried out as described in the data analysis road map (Fig. 5c ). One-way ANOVA, Kruskal-Wallis test and Kaplan-Meier analysis were conducted using SPSS v 16.0 software (SPSS Inc. Chicago, IL, USA). Differences were considered statistically significant at P < 0.05.
6
UPLC-Q-TOF/MS Metabolite Profiling
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The UPLC-Q-TOF/MS raw data were processed by MarkerLynx Applications Manager version 4.1 (Waters, Manchester, UK). The procedure included integration, normalization, and alignment of peak intensities, and a list of m/z and retention times with corresponding peaks was provided for all metabolites in every sample in the data set. The resulting data set was analyzed by PLS-DA using SIMCA-P software (version 13.0, Umetrics AB, Malmö, Sweden) after a Pareto-scaled procedure.
7
Metabolomic Profiling of MYC-regulated Lymphoma
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Serum samples (100 μl) were assessed by ultra performance liquid chromatography (UPLC) and quadruple/time-of-flight mass spectrometry. The mass spectrometer was operated in both positive and negative electrospray ionization (ESI+/ESI−) mode. The UPLC-quadruple/time-of-flight mass spectrometry ESI+ and ESI− raw data were analyzed by the MarkerLynx Applications Manager version 4.1 (Waters Corp., Milford, MA, USA) and total ion chromatography was generated. To identify different metabolomic profile between MYC-high and MYC-low groups, multivariate analysis was performed using SIMCA-P software version 12.0 (Umetrics AB, Umeå, Sweden). The unsupervised principal component analysis and supervised orthogonal partial least squares-discriminant analysis (OPLS-DA) models were constructed. Reliability of OPLS-DA models was validated by response permutation test. Quality controls were prepared by mixing the same volume of each sample and repeatedly injected during the assay to monitor instrumental stability and avoid systematic bias. As for cell metabolites, lymphoma cells (1 × 107/ml) were extracted by freeze-thaw method and cell lysate (100 μl) was used for UPLC-triple quadrupole mass spectrometry analysis. The detailed parameters were as previously reported.14 (link)
8
Metabolomic Profiling for Depression Diagnosis
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The raw data were analyzed using the MarkerLynx Applications Manager version 4.1 (Waters, Manchester, U.K.), which allowed for deconvolution, alignment and data reduction to provide a list of mass and retention time pairs with corresponding intensities for all of the detected peaks from each data file in the data set.
Multivariate data analysis (MVA) was performed using Simca-p software (v12.0, Umetric, Umeå, Sweden). Imported data set were normalization, mean-centered and pareto-scaled prior to multivariate analysis. Principal components analysis (PCA) and orthogonal partial least squares discriminate analysis (OPLS-DA) were employed to process the acquired MS data. PCA was performed to discern the natural separation between different stages of samples by visual inspection of score plots. In the OPLS-DA model, samples from different groups were classified, and the results were visualized in the form of score plots to show the group clusters and S-plots to show the variables that contributed to the classification.
The receiver operating characteristic (ROC) curve was performed to evaluate the accuracy of identified metabolites in distinguishing depression from control by using a web-based tool called ROCCET (ROC Curve Explorer,http://www.roccet.ca )54 .
Multivariate data analysis (MVA) was performed using Simca-p software (v12.0, Umetric, Umeå, Sweden). Imported data set were normalization, mean-centered and pareto-scaled prior to multivariate analysis. Principal components analysis (PCA) and orthogonal partial least squares discriminate analysis (OPLS-DA) were employed to process the acquired MS data. PCA was performed to discern the natural separation between different stages of samples by visual inspection of score plots. In the OPLS-DA model, samples from different groups were classified, and the results were visualized in the form of score plots to show the group clusters and S-plots to show the variables that contributed to the classification.
The receiver operating characteristic (ROC) curve was performed to evaluate the accuracy of identified metabolites in distinguishing depression from control by using a web-based tool called ROCCET (ROC Curve Explorer,
9
Vibrio parahaemolyticus Gene Expression and Metabolome
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The RT-qPCR data were analyzed using the ABI 7500 fast system. The quantity results based on RT-qPCR for tdh and trh genes at 25 °C were used as datum for relative quantity data, which were respectively set as to 1. The RT-qPCR value of tdh or trh genes for other sample was converted to relative quantity data in comparison with the value from datum. A one-way ANOVA was performed by Microsoft office Excel 2007 (Microsoft, Redmond, USA) to determine significant differences at α = 0.05.
UPLC-MS spectra data were first processed by Markerlynx Applications Manager Version 4.1 (Waters, Manchester, UK), including the detection and retention time (R.T.) alignment of peaks in each chromatogram. Metabolites were identified by mass-to-charge ratios in the human metabolome database (HMDB). The processed data were then introduced to SIMCA-P 11.5 (Umetrics, Umea, Sweden). Multivariate statistical analysis method of principal component analysis (PCA) was performed to determine the trend of data which transforms the correlated variables dataset into a smaller number of independent variables, i.e., the principle components [41 (link)].
Pearson’s correlation analysis was performed using the SPSS 17.0 (SPSS Inc., Chicago, USA). The correlation analysis was performed between the virulence genes expression and metabolome.
UPLC-MS spectra data were first processed by Markerlynx Applications Manager Version 4.1 (Waters, Manchester, UK), including the detection and retention time (R.T.) alignment of peaks in each chromatogram. Metabolites were identified by mass-to-charge ratios in the human metabolome database (HMDB). The processed data were then introduced to SIMCA-P 11.5 (Umetrics, Umea, Sweden). Multivariate statistical analysis method of principal component analysis (PCA) was performed to determine the trend of data which transforms the correlated variables dataset into a smaller number of independent variables, i.e., the principle components [41 (link)].
Pearson’s correlation analysis was performed using the SPSS 17.0 (SPSS Inc., Chicago, USA). The correlation analysis was performed between the virulence genes expression and metabolome.
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