The metabolic data were analyzed by one-way analysis of variance (ANOVA) in SPSS 21.0 software (SPSS Inc., Chicago, IL). When sampling time was included, data were analyzed using a linear mixed model with treatment and treatment by sampling time interaction as fixed effects, and sampling time as a repeated measures variable. The microbial community composition data was analyzed using Wilcoxon rank-sum test in the JMP Pro software (JMP Pro version 13.2.1, SAS Institute Inc. SAS Institute, Cary, NC, USA). The genomic ranks of attributes were evaluated by Correlation, ReliefF, Symmetrical Uncert, and multi-cluster feature selection (MCFS) methods in software of Waikato Environment for Knowledge Analysis (WEKA) (version 3.8.4, Hamilton, New Zealand) [32 ], and further comprehensively analyzed by RobustRankAggreg (RRA) R package [33 (link)]. All p values were adjusted for False Discovery Rate (FDR) using the Benjamini-Hochberg method. Statistical significance was declared at p ≤ 0.05 and tendencies at 0.05 < p ≤ 0.10.
Jmp pro
Manufactured by SAS Institute
Sourced in United States, Japan, Germany, Canada, Austria
JMP Pro is an advanced data analysis software developed by SAS Institute. It provides sophisticated statistical and visual analysis tools to assist users in exploring, modeling, and interpreting complex data. JMP Pro offers a range of analytical capabilities, including multivariate techniques, predictive modeling, and interactive visualizations, enabling users to gain deeper insights from their data.
Automatically generated - may contain errors
Lab products found in correlation
Sas 9, by SAS Institute (12 mentions)
Prism 9, by GraphPad (10 mentions)
Prism 8, by GraphPad (10 mentions)
Matlab, by MathWorks (8 mentions)
Prism 7, by GraphPad (8 mentions)
Sas version 9, by SAS Institute (8 mentions)
Prism v9, by GraphPad (4 mentions)
Jmp pro version 11, by SAS Institute (4 mentions)
Proc mixed of sas version 9, by SAS Institute (4 mentions)
Prism 5, by GraphPad (4 mentions)
Corescan, by GE Healthcare (4 mentions)
Lunar idxa densitometer, by GE Healthcare (4 mentions)
Stamp10, by IBM (4 mentions)
Spss v20, by IBM (4 mentions)
Sas mimed procedure, by SAS Institute (4 mentions)
R statistical package, by R Project for Statistical Computing (2 mentions)
R software, by SAS Institute (2 mentions)
Prism version 9, by GraphPad (2 mentions)
Instat, by GraphPad (2 mentions)
Statistical software version 13, by MedCalc (2 mentions)
707 protocols using jmp pro
1
Metabolic and Microbial Community Analysis
2
Industrial Hemp Cultivar Pollinators and Composition
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All analyses were conducted using JMP Pro (JMP Pro v. 14 SAS Institute, Cary, NC, USA). For both sampling methods, the total number of insects belonging to each pollinator type (honey bees, bumble bees, and sweat bees) captured throughout the sampling period was obtained. Plant height data from 80 randomly selected plants at flowering were recorded. Analysis of variance (ANOVA) was used to examine differences in plant height and the distribution of pollinators among the four industrial hemp varieties. Means were separated using Tukey–Kramer honestly significant difference at p = 0.05. Regression analysis was used to describe the relationships between plant height and pollinator types and total number of pollinators. Principal component analysis (PCA) was used to identify common factors that accounted for most of the variations in the chemical composition data (crude fiber, protein, ash moisture, protein, amino acid, minerals, and fatty acids) and to also examine relationships between the different independent variables of each chemical component. It also allowed us to determine the eigenvectors that maximized the variance. Thereafter, it attained a second linear function PC2 that was uncorrelated with PC1. The PCA data were not standardized since the values were similar, indicating similar variance.
3
Comparative Analysis of Phenolic Compounds
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Extraction yields of phenolic acids, flavonols, and flavan-3-ols were compared with one-way ANOVA followed by Student's t test at p ≤ 0.05 using JMP Pro (Version 14.2, SAS Institute Inc., Cary, NC, USA). Each NDES and 75% ethanol were compared using Dunnett's tests at p ≤ 0.05. Principle component analysis (PCA) was performed on JMP Pro (Version 14.2, SAS Institute Inc., Cary, NC, USA) for the phenolic compounds extracted from muscadine grape skin and seed.
4
Comparative Analysis of SHERLOCK Assays for Schistosoma Detection
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Statistical analyses were conducted using Prism GraphPad (Version 7, GraphPad Software, La Jolla, CA, USA). Each experiment was performed in duplicate and all data are presented as the mean ± SE of technical replicates. Comparisons between groups were performed to determine statistical significance using One-way ANOVA with Dunnett's correction for multiple comparisons. Sensitivity and specificity of the SHERLOCK assays were calculated from the 2 by 2 contingency tables comparing the SHERLOCK assay results versus qPCR (Sjcox1-F1R1 or Sm1-7-F1R2 primers) as the reference test. The confidence intervals were calculated using the Wilson Score method in JMP Pro (v17.1, SAS Institute, Cary, NC, USA). The receiver operating characteristic (ROC) curves were created in JMP Pro (v17.1, SAS Institute, Cary NC, USA) using simple logistic regression of the dichotomised qPCR result versus the quantitative SHERLOCK result. The thresholds were chosen to maximise the sum of sensitivity and specificity (Youden's Index). AUCs (area under the ROC curve), sensitivity and specificity are provided with 95% confidence intervals.
5
Dietary Corn Grain Effects on Dairy Cow Nutrition
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All data were analyzed using the fit model procedure of JMP Pro (version 14, SAS Institute Inc.) according to the following model:
where Y ijk = the dependent variable; μ = overall mean, C i = random effect of cow (i = 1 to 8), P j = fixed effect of period (j = 1 to 4), T k = fixed effect of treatment (k = 1 to 4), PT jk = interaction of period and treatment, and e ijk = residual, assumed to be normally distributed.
A reduced model without period × treatment interactions was used when this effect was not significant (P > 0.15). Orthogonal contrasts were made for the effect of endosperm type of corn grain, fineness of grind of corn grain, and their interaction. Normality of residuals was checked with normal probability and box plots and homogeneity of variances with plots of residuals versus predicted values. Convergence criteria were not satisfied with cow as a random effect for several variables; cow was a fixed effect to determine P-values, but cow was a random effect for the determination of least squares means and standard error of the mean. Treatment effects were declared significant at P ≤ 0.05, and tendencies for treatment effects were declared at P ≤ 0.10. Pearson correlation coefficients for measures of N flow to the duodenum and related variables across cowperiod means were determined using the multivariate procedure of JMP Pro (version 14, SAS Institute Inc.).
where Y ijk = the dependent variable; μ = overall mean, C i = random effect of cow (i = 1 to 8), P j = fixed effect of period (j = 1 to 4), T k = fixed effect of treatment (k = 1 to 4), PT jk = interaction of period and treatment, and e ijk = residual, assumed to be normally distributed.
A reduced model without period × treatment interactions was used when this effect was not significant (P > 0.15). Orthogonal contrasts were made for the effect of endosperm type of corn grain, fineness of grind of corn grain, and their interaction. Normality of residuals was checked with normal probability and box plots and homogeneity of variances with plots of residuals versus predicted values. Convergence criteria were not satisfied with cow as a random effect for several variables; cow was a fixed effect to determine P-values, but cow was a random effect for the determination of least squares means and standard error of the mean. Treatment effects were declared significant at P ≤ 0.05, and tendencies for treatment effects were declared at P ≤ 0.10. Pearson correlation coefficients for measures of N flow to the duodenum and related variables across cowperiod means were determined using the multivariate procedure of JMP Pro (version 14, SAS Institute Inc.).
6
Analyzing Metabolite Profiles and Microbial Abundances
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The generalized linear model procedure was used to analyze the metabolites concentration and production using the SPSS 21.0 software (SPSS Inc., Chicago, IL). When sampling time was included in the model, a linear mixed model was used with treatment, sampling time, and the treatment by sampling time interaction as fixed effects and the animal as a random factor. The ANCOM-BC R package was used to determine the differentially abundant taxa for amplicon and metagenome data [96 (link)]. The Wilcoxon rank-sum test in the JMP Pro software (JMP Pro version 13.2.1, SAS Institute Inc., SAS Institute, Cary, NC) was used to analyze the relative abundance of functional genes and MAGs. All P-values were adjusted for false discovery rate using the Benjamini–Hochberg method, and P < .05 was regarded as statistically significant.
7
Univariate and Multivariate Analysis Protocol
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Summary measures of outcomes such as proportions, means, Kaplan–Meier curves, standard deviation and standard error were calculated. Univariate analysis using t-tests, chi-square analysis and Pearson correlation was performed using JMP Pro version 13.0.0 (Statistical Analysis Software, Cary, NC, USA) to assess the impact of each covariate on each of the outcomes. Multivariable models were constructed for outcomes where more than one covariate demonstrated significant univariate association (P < 0.05). The threshold for multivariable significance was set at P < 0.05. No adjustment was made for multiple comparisons in order to increase sensitivity to possible risks and benefits of each factor.
8
Opioid Prescription Reduction Protocol
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Sample size calculations were performed based on data from the previous observational trial of opioid usage. Using a 6-week mean prescription volume of 618 OMEs per patient with a standard deviation of 316 OMEs, at least 86 subjects in each group would be required to detect a difference with moderate effect size (approximately 25% reduction in OMEs prescribed) in cumulative 6-week OME prescribing at an alpha of 0.05 and power of 0.9. Six-month date ranges before and after the study were chosen to achieve a sample size of at least that magnitude based on historical trends. Descriptive statistics including proportions with percentages or means and standard deviations were performed as appropriate. Student t-tests and χ2 analysis were used to evaluate the impact of preawareness versus awareness group on the primary study outcomes. Multivariable main effects linear and logistic regression models incorporating baseline and treatment characteristics as well as preawareness versus awareness group were performed on the study outcomes using the standard statistical package JMP Pro, version 14, by Statistical Analysis Software (Cary, NC). Statistical significance was taken at P < .05 in multivariable analyses.
9
Cecal Microbiome Profiling in Mice
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All data are displayed as the mean±standard error of the mean (SEM). The CRD thresholds were analyzed using one-way analysis of variance followed by the Tukey-Kramer post hoc test. The relative abundances of cecal microbe groups were analyzed using the Kruskal-Wallis test followed by the Steel-Dwass post hoc test. All analyses were performed using JMP Pro, version 12.2.0 (SAS Institute Inc., Cary, NC, USA). Probability values less than 0.05 were considered statistically significant. Additionally, microbial diversity was analyzed with QIIME.
10
Statistical Analysis of Oncology Outcomes
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The statistical significance of OS, DFS, PFS and DSS was computed using the Long-rank t-test through cBioportal. For the comparison of clinico-pathological parameters, Pearson’s chi-square (χ2) test was applied (p < 0.05). The DEGs were analyzed by the DEseq2 package in R. The results were interpreted in R using ‘plotPCA’ function for Principal component analysis and volcano plot was generated using ‘enhancedvolcano’ package (http://bioconductor.org/packages/EnhancedVolcano.html ). The cytokines z-scores were compared between each group using an unpaired t-test corrected for multiple comparisons using Holm–Sidak method (p < 0.05). For the comparison of immune cells and T cell exhaustion markers, unpaired t-test with Welch correction was used (p < 0.05). The statistical analyses were performed using R (R Foundation for Statistical Computing, Vienna, Austria, version 3.6.1) (http://www.R-project.org/ ), JMP-Pro (version 14.0.0, SAS Institute, Cary, NC, USA) and GraphPad Prism (version 8 GraphPad Software, La Jolla, CA, USA). P values < 0.05 were considered statistically significant.
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