Both of the LC/MS raw data was preprocessed by Progenesis QI 1.0 software (Nonlinear Dynamics, 2014, version: 1.0) and then using EZinfo 2.0 software (Waters Corp., Milford, USA) for further multivariate data analyses, including principal components analysis (PCA), partial least-squared discriminant analysis (PLS-DA) and orthogonal partial least-squared discriminant analysis (OPLS-DA). Pareto scaling transformation and data normalization were applied to the data processing before multivariate statistical analysis. The contribution rate of potential biomarkers in metabolic profiling could be determined by VIP-plot, which was obtained by OPLS-DA analysis. Using VIP-plot to select VIP value was more than 3 and Student’s t-test to select P-value was less than 0.05 as the potential metabolite biomarkers of prostate cancer.
Ezinfo 2
Manufactured by Waters Corporation
Sourced in United States
EZinfo 2.0 is a data analysis software application developed by Waters Corporation. The core function of EZinfo 2.0 is to provide users with tools for performing multivariate data analysis on analytical data.
Automatically generated - may contain errors
Lab products found in correlation
Masslynx v4, by Waters Corporation (2 mentions)
Markerlynx xs software, by Waters Corporation (1 mentions)
Prism 6 for windows, by GraphPad (1 mentions)
Quanlynx program, by Waters Corporation (1 mentions)
Masslynx software, by Waters Corporation (1 mentions)
Analyst tf 1, by AB Sciex (1 mentions)
Markerlynx xs application manager, by Waters Corporation (1 mentions)
9 protocols using ezinfo 2
1
Multivariate Analysis of Prostate Cancer Metabolites
2
Metabolomics Data Preprocessing and Analysis
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Waters MarkerLynx XS software was used for data peak detection, deconvolution, alignment, data reduction, and following normalization to total marker intensity. The datasets were eventually refined, such that they contained a list of mass and retention time pairs with corresponding intensities for all of the detected peaks from each data file. A total of 3099 and 478 variables were detected in the samples under POS and NEG modes, respectively. The main parameters for peak detection were set as follows: retention time range, 0.5–17 min.; mass range, 100–1200 Da; XIC window, 0.02 Da; mass window, 0.02 Da; and, retention time window, 0.10 min. After the peaks were recognized and aligned, the area of each peak was normalized to total marker intensity in each chromatogram. The parameters of peak integration were as follows: marker intensity threshold, 500 (NEG)/1000 (POS); peak width at 5% of the height, 1 s; peak-to-peak baseline noise, 0.00; without smoothing. The resulting data included retention time, m/z value, and normalized peak area that composed a dataset. The dataset was subjected to PCA and OPLS-DA without transformation using EZinfo 2.0 software (Waters, Milford, MA, USA). Statistical comparison in Figure 1 , paired t-test and ANOVA, followed by Fisher’s LSD post hoc analysis, was conducted while using GraphPad Prism 6 for Windows (San Diego, CA, USA).
3
Metabolomics Analysis Pipeline
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The acquired data were processed using MassLynx V4.1 software (Waters) to give a data matrix consisting of the retention time, m/z, and abundance value for each ion, and EZinfo 2.0 software was used to analyze the data matrices and identify statistically significant ions, where principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were performed. The score plot and S-plot based on OPLS-DA model were taken for class separation and showed variables contributing to the classification. The potential metabolites were screened based on the variable importance in projection (VIP) value of VIP > 1 and p < 0.05 by using Student's t-test.
These potential ions were searched against the freely available metabolome databases, HMDB, Lipid MAPS, and METLIN. Once the potential metabolites had been identified, they were confirmed by matching fragmentation patterns and retention times. Only those that matched the known metabolite identities were conducted for further analysis. The metabolic pathways associated with these putative metabolites were evaluated with MetaboAnalyst 3.0, which is available online (http://www.metaboanalyst.ca ). The metabolic network was visualized by MetScape, a plug-in CytoScape (v.3.4.0) software to provide the relationship between metabolites and genes and interpret the metabolomics data.
These potential ions were searched against the freely available metabolome databases, HMDB, Lipid MAPS, and METLIN. Once the potential metabolites had been identified, they were confirmed by matching fragmentation patterns and retention times. Only those that matched the known metabolite identities were conducted for further analysis. The metabolic pathways associated with these putative metabolites were evaluated with MetaboAnalyst 3.0, which is available online (
4
Metabolomics Data Analysis Pipeline
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All LC/MS data including retention time, accurate mass, and MS/MS spectra were acquired in the centroid mode by MarkerLynx software MassLynx™ V 4.1 software with QuanLynx™ program (Waters Corp., Milford, MA, USA). All mass spectra were aligned with mass tolerance of 0.02 Da and retention time window of 0.20 min. The noise elimination level was 6. Ion identification was based on the tR, m/z, and MS/MS spectra. The three-dimensional were introduced into the EZinfo 2.0 software (Waters Corp, Milford, MA, USA) for PCA orthogonal partial least-squares-discriminate analysis (OPLS-DA) with the purpose of visualizing discrimination between the dosed and control groups. The S-plot showing the combined covariance P (1) and correlation P (corr) from the PLS-DA model was used to visualize the metabolites contributing to the discrimination.
5
Metabolic Profiling of Diabetes
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The data are expressed as mean ± SD. The differences among the levels of FBG, HbA1c, OGTT, U-GLU, TG, TC, and serum insulin were assessed using Welch’s t-tests with SPSS 19.0 (SPSS Inc., Chicago, IL, USA); values were considered different if the p-value was less than 0.05. The acquired mass data was analyzed with MassLynx software (version 4.1, Waters Corporation, Milford, MA, USA) for peak detection and alignment. All the data were normalized to the summed total ion intensity per chromatogram, and the resultant data matrices were processed with EZinfo 2.0 software (Waters Corp., Milford, MA, USA) using the pattern recognition approach. Metabolites were assigned by MS/MS analysis or interpreted with the following available biochemical databases: METLIN, HMDB, and KEGG. The possible biological roles of metabolites were evaluated with an enrichment analysis by MetaboAnalyst 3.0.
6
Synovial Metabolic Profiling in Osteoarthritis
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Metabonomics data were acquired using the Analyst® TF 1.7.1 software (AB SCIEX, USA). The mass data were introduced to EZinfo 2.0 software (Waters corp., Milford, USA) for the principle component analysis (PCA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA). Hierarchical clustering analysis was performed by the heatmap tool implemented in MetaboAnalyst (http://www.metaboanalyst.ca/MetaboAnalyst/). PCA, OPLS-DA, and hierarchical clustering analysis can be used to investigate the synovial metabolic pro ling of control and HL-treated groups. Variable importance in the projection (VIP) ranks the overall contribution of each variable to the OPLS-DA model, and the variables with VIP 1 are considered relevant for group discrimination. The one-way analysis of variance was calculated with the R Programming Language (version 3.4.2). Differentially expressed metabolites were selected in accordance with their VIP value (VIP 1) and prede ned P-value thresholds (p 0.05).
7
Metabolomics Data Analysis Workflow
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Both of the Metabolomics raw data were analyzed by Progenesis QI 1.0 software (Nonlinear Dynamics, 2014, version: 1.0) and then using EZinfo 2.0 software (Waters Corp., Milford, USA). The mean ± standard deviation (x ¯
8
Metabolomic Profiling of Dilated Cardiomyopathy
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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.
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.
9
Metabolomic Workflow for Biomarker Discovery
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The raw files were imported into Progenesis QI software for denoising, peak identification, peak alignment, normalization and other operations. The raw data were converted into a data matrix containing tR-m/z ion pairs, sample names and peak intensities, and then the matrix was imported into multivariate statistical software EZinfo2.0 (Waters Corporation, Milford, MA, United States) for principal components analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA). The metabolites were identified by searching and comparing the mass fragment information and accurate mass number with data from METLIN (http://metlin.scripps.edu/ ), HMDB (http://www.hmdb.ca/ ) and KEGG (http://www.genome.jp/kegg/ ) databases. The MetaboAnalyst (https://www.metaboanalyst.ca/ ) was used for metabolic pathway analysis.
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