Resulting data were imported into SIMCA-P 15.0 version software package (Umetrics, Umea, Sweden) for multivariate statistical analysis such as Principal Component Analysis (PCA). Orthogonal Projection to Latent Structure Discriminant Analysis (OPLS-DA) was also performed to extract information on discriminant compounds [30 (link)]. To validate the supervised model, permutation tests were conducted with 200 iterations. A visual assessments of OPLS-DA loading or coefficient derived from 1H NMR spectra were performed with color-coded correlation coefficient of variables using an in house developed script for MATLAB. R2X and R2Y were used to explain variability of variables while Q2 was used to indicate model predictive capability. Sample distinction variables with high variable importance in projection (VIP) value (VIP > 1.0) and low p-value (p < 0.05) were selected for statistical analysis. All statistical analyses were performed using SPSS version 22.0 software package (SPSS Inc., Chicago, IL, USA). T-test was performed to compare the relative amount (peak intensity) of identified metabolites between the two groups. Analyses of variance (one-way ANOVA) followed by Duncan multiple-range test was also performed for examination of group differences.
Simca p 15
Manufactured by Sartorius
Sourced in Sweden
SIMCA-P 15.0 is a data analysis software tool developed by Sartorius. It provides multivariate data analysis capabilities for researchers and scientists working in various fields, including life sciences, process industries, and material science. The software allows users to explore, analyze, and interpret complex data sets using advanced statistical techniques.
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10 protocols using simca p 15
1
Multivariate Analysis of Metabolomic Data
2
Metabolomics Analysis of Tgfb3 Mutant Mice
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Univariate (UVA) and multivariate (MVA) analyses were performed for LC-MS and GC-MS analyses, as previously described (Wang et al., 2011 (link)). In univariate analysis, a non-parametric method through Mann–Whitney U test was applied. P-values were obtained for each feature in UVA. MVA was performed with SIMCA-P 15.0 (Umetrics). An orthogonal partial least squares discriminant analysis (OPLS-DA) model was built between Tgfb3+/+ and Tgfb3+/−, then the variable influence of projection (VIP) used to select the significant features obtained in the comparison between groups was obtained for each feature. Metabolites with P≤0.05 and/or VIP>1 were selected as significant.
3
Metabolomic Analysis of DHC and FHC
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Heatmap and HCA were generated on the MetaboAnalyst5.0 (https://www.metaboanalyst.ca/ ) to display the overall difference of chemical compositions of DHC and FHC. PCA and OPLS-DA were applied to investigate the distinction of chemical compositions of DHC and FHC by SIMCA-P 15.0 (Umetrics AB, Umea, Sweden). The significant chemical markers were evaluated based on their p value and differing variable importance in projection (VIP) value calculated with OPLS-DA. One-way ANOVA was performed using SPSS 16.0 (Chicago, IL, USA).
4
Metabolomic Profiling of Biological Samples
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GC-MS data were converted from Shimadzu GC-MS Postrun Analysis to netCDF format file and processed with XCMS web software (https://xcmsonline.scripps.edu ). Intensities of features in the data set processed in XCMS were normalized by an internal standard (methyl stearate). PCA and PLS-DA of GC-MS data were performed to visualize the variance of metabolites using SIMCA-P 15.0 (Umetrics, Umea, Sweden). Cross validation was performed using a permutation test that was repeated 200 times. Metabolites with VIP > 1.0 and p < 0.05 were considered as metabolites that could discriminate groups. Identification of metabolites was performed by comparing their mass spectra with NIST 14.0. Metabolic pathway analysis (MetPA) was conducted to determine the influence of metabolic pathways on potential marker metabolites using MetaboAnalyst (www.metaboanalyst.ca ).
5
Metabolomic Profiling of Aristolochic Acid-Induced Nephrotoxicity
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Raw data files were converted to the cdf. format by using the Xcalibur software and then imported into custom-developed high-performance imaging software (MassImager 2.0, Beijing, China) for ion image reconstructions. After background subtraction and normalization, MS profiles from regions of interest (ROIs) were precisely extracted by matching H&E staining images of the adjacent section and generating separate two-dimensional data matrixes (m/z, intensity) in .txt format. The separated sample dataset matrixes were then imported into Markerview™ software 1.2.1 (AB SCIEX, Toronto, Ontario, Canada) for background deduction, peak picking, and peak alignment before multivariate statistical analyses. Multivariate analyses of the processed datasets from the ROIs were performed by using SIMCA-P 15.0 (Umetrics AB, Umea, Sweden). The metabolic profiles of the control and AAI-treated groups were compared by performing supervised multivariate OPLS-DA to achieve the maximum separation. Potential metabolic biomarkers were selected on the basis of their contribution to the class separation and variation within the data set. The paired t-test was used to compare the differences between the control group and the AAN group. Variables with P values < 0.05 were considered to be indicative of statistical significance when compared those of the control group.
6
Metabolomic and Microbiome Analysis
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The GC-MS data was analyzed with the help of principal component analysis (PCA) and PLS-DA to visualize the variance of metabolites and using SIMCA-P 15.0 (Umetrics, Umea, Sweden). For model validation, a 200-fold cross validation was performed. Metabolites with a VIP > 1.0, and a p < 0.05 were considered different across the two groups. The mass spectra data of the metabolites were compared with the help of the AIoutput software, NIST 14.0 library, and the human metabolome database (HMDB, http://www.hmdb.ca , accessed on 1 May 2021). Metabolic differences between groups were examined for statistical significance using Student’s t-test. To determine statistically significant differences between the two groups in microbial analysis, the non-parametric Mann-Whitney U test was used for unpaired data. The Benjamini-Hochberg algorithm was used to control the false discovery rate [68 (link)]. A FDA of 5% was applied to all tests to correct for multiple testing. Predictive functional analysis OTU picking from the 16S amplicon sequencing data was performed using QIIM [69 (link)] and the greengenes database [70 (link)], and functional analysis was conducted on the OTU data using PICRUSt [71 (link)]. The predictive functional analysis results were annotated with the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway [72 (link)].
7
Multivariate Analysis of Targeted LC-MS Assays
8
Quantitative Proteomic Profiling Procedures
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Each sample batch consisted of three digestion/MS plates. The 20 replicated samples spanned 11 of the 12 analysis plates providing a means of interrogating replication between both plate level processing and run order effects. Block and batch effects due to sample processing or injection run order are common with liquid chromatography (LC)‐MS based assays. The standards and calibration curves used in this study to generate absolute concentrations for the targeted analytes, however, were designed to minimize or eliminate these biases. Simca‐P 15 (Umetrics Inc.) with seven‐fold cross‐validation was used to generate multivariate quantitative partial‐least‐squares (PLS) and class discriminant models (PLS‐DA) to test for residual block effects in the final quantitative data. The data from the five targeted analytes were used as the independent block to predict the noted outcomes. All data processing was performed on Z‐normed log‐2 scaled data.
9
Metabolomic Data Processing Pipeline
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Run alignment, peak picking, adduct deconvolution and feature annotation were sequentially performed on Progenesis QI v2.3 (Nonlinear Dynamics, Waters, Newcastle upon Tyne, UK). Detected peaks were annotated with regard to a set of pure reference standards (MSMLS Library of Standards, Sigma-Aldrich) measured under the experimental conditions described previously (Pezzatti et al., 2019b (link)). The following tolerances were used: 2.5 ppm for precursor and fragment mass, 10% for retention time (Rt), and 5% in the case of collisional cross section (CCS). Data processing was achieved by SUPreMe, which is in-house software with capabilities for drift correction, noise filtering and sample normalization. Finally, data were transferred to SIMCA-P 15.0 software (Umetrics, Umea, Sweden) for multi-variate analysis (MVA).
10
Metabolomic Data Preprocessing Protocol
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Chromatogram alignment, peak picking, adduct deconvolution, and feature annotation were sequentially performed on Progenesis QI v2.3 (Nonlinear Dynamics, Waters, Newcastle upon Tyne, UK). The following tolerances were used for feature annotation with regard to a set of pure reference standards (MSMLS Library of Standards, Sigma-Aldrich) measured in the same instrument: 2.5 ppm for precursor and fragment mass, 10% for Rt, and 5% in the case of CCS. Data pretreatment was performed with SUPreMe, an in-house software with capabilities for drift correction, noise filtering, and sample normalization. Finally, data were transferred to SIMCA-P 15.0 software (Umetrics, Umea, Sweden) to perform Principal Component Analysis (PCA). AMOPLS analysis was conducted after unit variance scaling as previously described [42 (link)] under the MATLAB® 8 environment (The MathWorks, Natick, MA, USA). A series of 104 random permutations was performed to validate the AMOPLS model and assess the statistical significance of the effects.
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