The data are presented as mean ± SD as described in the figure legends. Comparisons between two groups were carried out using an unpaired two-tailed Student’s t-test. Significant differences were defined as p < 0.05. The RNA-seq data in TPM were loaded into the EZinfo 3.0.3 software (Umetrics, Umeå, Sweden) to allow principal component analysis (PCA). The TPM values were transformed into z-scores to allow presentation as heatmaps, and these were generated by the Multi Experiment Viewer (MEV) 4.9 software [28 (link)].
Ezinfo 3
Manufactured by Sartorius
Sourced in Sweden
EZinfo 3.0 is a data analysis software designed for Sartorius' laboratory equipment. It provides statistical analysis and visualization tools to help researchers interpret experimental data.
Automatically generated - may contain errors
8 protocols using ezinfo 3
1
RNA-seq Data Analysis Workflow
2
Transcriptome Analysis with PCA and Heatmaps
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All data are presented as mean ± SD. Unpaired two‐tailed Student's t test was used to compare two groups. Significant differences were defined as having a p < 0.05. The RNA‐seq dataset from the TPM was loaded into the EZinfo 3.0.3 software (Umetrics) package for principal component analysis (PCA). TPM values were transformed into z‐scores and these were used to create heatmaps, which were in turn generated by Multi Experiment Viewer (MEV) 4.9 software (Saeed et al., 2003 (link)).
3
Lipid Biomarker Identification via UPLC-QTOF/MS
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The raw data of UPLC-QTOF/MS was loaded on QI software (Waters, Manchester, UK) for analysis, which was carried out by noise setting, baseline correction, alignment, peak detection, and compound identification. Compound determination was performed by the in-house lipids and metabolites database. The accurate contents of lipids were analyzed through peak areas, fragment intensities, and stable-labeled internal standard compounds [23 (link)].
These processed data were then imported into EZinfo 3.0 software (Umetrics, Umeå, Sweden) for Student’s t-test, ANOVA analysis, and orthogonal partial least-squares discriminant analysis. OPLS-DA, with distinct predictive and orthogonal information indicating between and within group variance, has the ability to identify which variables include the class-separating information [24 (link),25 ]. To reduce the data noise induced over-fitting, ANOVA was applied to identify the significantly different lipids among three groups with p value ≤ 0.05. For ANOVA analysis, after parameter setting, the data distribution and variance homogeneity check were automatically carried out through EZinfo 3.0 software, and then lipids with p value ≤ 0.05 were listed.
After being analyzed and filtered through ANOVA analysis with p value ≤ 0.05 and VIP ≥ 1, the filtered biomarkers were finally identified.
These processed data were then imported into EZinfo 3.0 software (Umetrics, Umeå, Sweden) for Student’s t-test, ANOVA analysis, and orthogonal partial least-squares discriminant analysis. OPLS-DA, with distinct predictive and orthogonal information indicating between and within group variance, has the ability to identify which variables include the class-separating information [24 (link),25 ]. To reduce the data noise induced over-fitting, ANOVA was applied to identify the significantly different lipids among three groups with p value ≤ 0.05. For ANOVA analysis, after parameter setting, the data distribution and variance homogeneity check were automatically carried out through EZinfo 3.0 software, and then lipids with p value ≤ 0.05 were listed.
After being analyzed and filtered through ANOVA analysis with p value ≤ 0.05 and VIP ≥ 1, the filtered biomarkers were finally identified.
4
Multivariate Analysis of Diverse Indices
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All the experiments were at least conducted in triplicates and the mean values ± standard deviations were recorded. Analysis of variance (ANOVA) and Duncan's test were used to determine the significance of difference among different varieties. PCA and SCA of different indices were analyzed by the software of SPSS22.0 (IBM, Chicago, USA). The color map of values and correlation analysis of 27 indices were obtained by HemI 1.0 (http://ccd.biocuckoo.org/ ). PCA and LDA of E‐nose data were performed using the software of Winmuster. EZinfo 3.0 software (Umetrics AB, Umea, Sweden) was used for PCA‐X model analysis.
5
Comparative Metabolomic Analysis of Bacterial Strains
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Progenesis QI (Nonlinear Dynamics, Newcastle, UK) was used for spectra deconvolution, alignment, and feature identification. Blank samples (solvents that went through the same sample preparation with no bacteria) were used to exclude artifactual mass features. Mass features which eluted at t of >1 min, with minimum intensity higher than 100, the lowest mean abundance in the blank, and fold change over 100 from blank, were used for analysis. Following quantile normalization, multivariate tests were carried out using Ezinfo 3.0 (Umetrics AB, Umea Sweden) and Metaboanalyst 4.0 (47 (link)). Variable importance in projection analysis was performed, and discriminant metabolic features in ΔsepD and ΔcsrA strains were determined. Full-scan and MSE mass spectra were acquired from all masses of 30 to 2,000 Da. Identification of mass features was carried out using 18 metabolite libraries compatible with Progenesis QI, as well as our internal library, based on mass accuracy of <5 ppm, isotope pattern, fragmentation pattern, and elution time.
6
Metabolomic Profiling of Irritable Bowel Syndrome
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The peak deconvolution, identification and autointegrator were carried out using
the Progenesis QI (Waters Technologies, UK). The resulting three-dimensional
matrix involving peak index (RT–m/z pair), sample names (observations) and ion
intensity were introduced into the EZinfo 3.0 software (Umetrics, Umeå, Sweden)
for chemometric analysis. The unsupervised principal components analysis (PCA)
and supervised orthogonal partial least square-discriminant analysis (OPLS-DA)
were both applied to show the separation between diarrhea IBS profiles and
healthy controls. The significant ions were extracted from the variable
importance in the projection (VIP) matrix in OPLS-DA based on their contribution
to the classification in the dataset. These ions were further filtered using
nonparametric tests of t test and fold change calculation. Ions
satisfying the criteria of three filters [VIP > 1.5, pvalues < 0.05, fold changes > 1.2 (or <0.83)] were regarded as
significant. The selected ions were finally identified and interpreted as
follows: we searched for their accurate masses in the metabolite databases of
METLIN (www.metlin.scripps.edu ), HMDB (www.hmdb.ca ) and KEGG (www.genome.jp/kegg ). Then, we confirmed the isotopic
distribution, retention time and fragments of commercial standards with those
metabolites of interest. p values < 0.05 were considered as
statistically significant.
the Progenesis QI (Waters Technologies, UK). The resulting three-dimensional
matrix involving peak index (RT–m/z pair), sample names (observations) and ion
intensity were introduced into the EZinfo 3.0 software (Umetrics, Umeå, Sweden)
for chemometric analysis. The unsupervised principal components analysis (PCA)
and supervised orthogonal partial least square-discriminant analysis (OPLS-DA)
were both applied to show the separation between diarrhea IBS profiles and
healthy controls. The significant ions were extracted from the variable
importance in the projection (VIP) matrix in OPLS-DA based on their contribution
to the classification in the dataset. These ions were further filtered using
nonparametric tests of t test and fold change calculation. Ions
satisfying the criteria of three filters [VIP > 1.5, pvalues < 0.05, fold changes > 1.2 (or <0.83)] were regarded as
significant. The selected ions were finally identified and interpreted as
follows: we searched for their accurate masses in the metabolite databases of
METLIN (
distribution, retention time and fragments of commercial standards with those
metabolites of interest. p values < 0.05 were considered as
statistically significant.
7
Multivariate Analysis of Lipid Profiles
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Progenesis QI (Nonlinear Dynamics) was used for spectra deconvolution, alignment, normalization, and identification of lipid species. Masses with minimum intensity cutoff of 100 m/z, lowest mean abundance in blank (solvents that went through sample preparation, but contained no sample), and fold-change >100 from blank were used for analysis. Data were exported to EZInfo 3.0 (Umetrics, v2.0) and Metaboanalyst 4.0 (22 (link)) for multivariate statistical analysis. Following quantile normalization of data, a partial least-squares discriminant analysis (PLS-DA) model was generated. Ten-fold cross-validation was applied (Q2 and R2 are presented in Figure 1
8
Multivariate Analysis of Metabolic Pathways
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Statistical analysis of behavioral test was performed using SPSS 19.0 (IBM, USA). Results of pathological scores were analyzed using IPP 6.0 (Intel, USA). The multivariate data analysis, principal component analysis (PCA) and partial least squares discriminate analysis (PLS-DA) were conducted by EZinfo 3.0 (Umetrics, Sweden). Metabolism pathway analysis was conducted by MetaboAnalyst 3.0 (http://www.metaboanalyst.ca/ ). Enzymes related to metabolites were collected by HMDB 4.0 (http://www.hmdb.ca/ ). Protein-protein interactions with confidence >0.9 in the STRING 10.5 (https://string-db.org ) were used to construct a network containing relationships between enzymes and proteins. The structures of WLT ingredients were downloaded from PubChem (http://pubchem.ncbi.nlm.nih.gov ). The structures of proteins were obtained from the Protein Data Bank (PDB, https://www.rcsb.org/ ). Protein structures not available from PDB was homology modeled by (https://www.swissmodel.expasy.org ) docking was carried out with Discovery Studio 3.5 (BIOVIA, USA). Parametric data was analyzed by ANOVA or Student's t-test for, and non-parametric data by Mann–Whitney U-test or Kruskal-Wallis. Differences were considered the results with P-values < 0.05.
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