All statistical analyses were performed using Prism Software and LMP (SAS institute). If the data passed normality and equal variance test, one-way ANOVA or t-test was used for the comparison analysis. To compare values obtained from three or more groups, one-factor analysis of variance (ANOVA) was used followed by post-hoc analysis. Non-parametric Spearman correlation was used for all correlations except for data in human studies describing relationships with logIL-18, where Pearson correlation coefficient was used. The difference between the two groups was examined with an unpaired parametric two-tailed t-test unless noted as unpaired one-tailed t-test. Experimental results are shown as the mean ± SEM except for human serum measurements which are reported as mean ±SD. A p-value of <0.05 was considered significant for all tests performed. In the patient cohort, IL-18 was not normally distributed so IL-18 was log transformed and t-test was performed.
Prism software
Manufactured by SAS Institute
Sourced in United States
Prism software is a data visualization and analysis tool developed by SAS Institute. It provides a range of features for creating and customizing visual representations of data, such as graphs, charts, and plots. The software's core function is to facilitate the effective presentation and interpretation of data through intuitive and powerful visualization capabilities.
Automatically generated - may contain errors
6 protocols using prism software
1
Analyzing Cytokine Profiles in Patients
2
ABO Blood Group and COVID-19 Transmission
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We identified instances of the following four categories from the questionnaire: ABO-compatible COVID-19 transmission; ABO-incompatible COVID-19 transmission; ABO-compatible absence of COVID-19 transmission; ABO-incompatible absence of COVID-19 transmission. Two-tailed Fisher’s exact tests or chi-squared tests were used for comparisons. A multivariable logistic regression model was used to assess the simultaneous effects of blood group and ABO incompatibility on the probability of COVID-19 transmission. Values of p ≤ 0.05 were considered as significant. Analyses were performed with Prism software version 8.4.3 and SAS statistical software (SAS 9.4 Institute, Cary, NC).
3
Survival Analysis of Cytokine Dynamics
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Survival data in each group were generated using Kaplan–Meier lifetime survival probability methodology and the log-rank (Mantel–Cox) test. GVHD scores and serum cytokine concentrations were analyzed by ANOVA with Tukey’s Honest Significance Test (HSD) post-hoc tests. PRISM software (SAS Institute, Cary, NC, USA) was used for these tests and a p value <0.05 was considered statistically significant.
4
Comparative Statistical Analysis of Donor Samples
5
Cytokine Profiling in Sepsis Patients
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The statistical analyses were performed with SPPS v23, and Prism software (Software Inc., San Diego, CA, USA) was used for graphics. Values are expressed as the median with interquartile ranges.
The normality of samples was determined with the Kolmogorov–Smirnov normality test, and samples did not follow a Gaussian distribution. The non-parametric Mann–Whitney test was used to analyze differences between two non-paired groups with a significance level of 0.05. The Kruskal–Wallis test was used to analyze differences among ICU controls, sepsis and SS patients, followed by a post hoc test using Bonferroni correction for α (0.05/3) when we compared clinical data. For comparisons in cytokine levels in different groups we performed a Kruskal-Wallis test followed by Dunns’ post hoc test. Spearman’s analysis was used for the correlation analysis among variables and the p-values were adjusted for multiple comparisons by the Benjamini–Hochberg method with the function p.adjust() from the stats package. An FDR < 0.1 was considered significant.
The R version 4.0.0 was used for clustering analysis and preparation of the heatmap. Hierarchical clustering was performed with the function hclust() from the stats package, and the heatmap was generated using the function heatmap.2() from the gplots package.
The normality of samples was determined with the Kolmogorov–Smirnov normality test, and samples did not follow a Gaussian distribution. The non-parametric Mann–Whitney test was used to analyze differences between two non-paired groups with a significance level of 0.05. The Kruskal–Wallis test was used to analyze differences among ICU controls, sepsis and SS patients, followed by a post hoc test using Bonferroni correction for α (0.05/3) when we compared clinical data. For comparisons in cytokine levels in different groups we performed a Kruskal-Wallis test followed by Dunns’ post hoc test. Spearman’s analysis was used for the correlation analysis among variables and the p-values were adjusted for multiple comparisons by the Benjamini–Hochberg method with the function p.adjust() from the stats package. An FDR < 0.1 was considered significant.
The R version 4.0.0 was used for clustering analysis and preparation of the heatmap. Hierarchical clustering was performed with the function hclust() from the stats package, and the heatmap was generated using the function heatmap.2() from the gplots package.
6
Comparing Adiposity Measures to Body Composition
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Data analyses were performed using statistical software (STATISTIX version 8.0; Analytical Software, St Paul, MN, USA), the figures were made using Graphpad Prism Software (version 5.02 for Windows, San Diego, CA, USA), and all tabulated data are presented as mean±standard deviation (s.d.). The analysis of body composition parameters (total Fat%, Trunk Fat%) vs anthropometric surrogates of adiposity (BMI, WC) was performed by linear model procedures including Pearson's product-moment correlations for determining linear associations between variables, and statistical comparisons of the two regression lines for equality of variance, slopes and elevations (that is, y-intercepts). The analytical software for comparison of regression lines utilizes the analysis of covariance (ANCOVA). It first compares the variances for the two regression lines using Bartlett's test. Subsequently, assuming homogeneity of variance, it compares the slopes. Assuming homogenous variances and parallel lines, it then tests for differences in the y-intercept. For all tests, significance was set at P<0.05.
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