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Prism v6.0f

Manufactured by GraphPad
Sourced in United States

Prism v6.0f is a data analysis and graphing software developed by GraphPad. It is designed to help researchers and scientists visualize, analyze, and present their data in a clear and professional manner. The software provides a range of tools for data handling, statistical analysis, and graph creation.

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30 protocols using prism v6.0f

1

Non-Parametric Statistical Analysis

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Statistical analysis was performed using a two-tailed non-parametric Mann-Whitney U test in Prism v6.0f (GraphPad Software Inc.).
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2

Respiratory function assessment in COPD

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A Student’s paired t test was used to identify differences between COPD and CON. A Bland-Altman analysis for repeated measurements was used to assess the agreement between the two
mV˙O2 recovery k assessments (Bland and Altman, 1999 (link)). Coefficient of variation (CV) and intraclass correlation coefficient (ICC) were used to assess within-subject test-retest reproducibility. Variables correlated with the difference between repeated-measures of kk) were sought by Spearman univariate linear regression analysis. Significant differences were accepted at P ≤ 0.05. Results are presented as mean ± SD, unless otherwise specified. A Shapiro-Wilk’s test (P ≥ 0.05) and visual inspection of the histograms, Q-Q plots and box plots were performed to determine normal distribution of k values for both COPD and CON groups (COPD, P > 0.45; CON, P > 0.06). Statistical analyses were performed using Prism v6.0f (GraphPad, San Diego, CA, USA) and SPSS v20 (IBM, Chicago, IL, USA).
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3

Statistical Analysis of Experimental Data

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Student t test was used for comparison between two groups whenever each group had ≥9 samples, after checking that distribution was normal by Shapiro-Wilk test. Non-parametric tests were used for the remaining comparisons. Mann-Whitney test or Wilcoxon test were used for comparison between two groups. Either Kruskal-Wallis or Friedman test with Dunn’s correction for multiple comparison were used for comparison among more than two groups. Differences were considered significant when * P ≤ 0.05; ** P ≤ 0.01. Statistical analysis was performed using Prism v.6.0f, GraphPad Software (La Jolla, CA, USA).
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4

Statistical Analysis of T Cell and Antibody Responses

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Statistical analysis was performed using Prism v6.0f software (Graphpad Software, Inc.). To maximize power to detect differences, T cell and antibody responses were compared using an analysis of variance accounting for the repeated measures on animals over time, and the nesting of animals within each infection group as previously described [21 (link)–23 (link)]. ELISA results on cell culture supernatants from day 7-post infection were analyzed using a 1-way ANOVA with Bonferri post-test analysis.
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5

Statistical Significance Analysis Methods

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Differences between groups were analyzed by a Student's t‐test or Mann–Whitney U‐test (GraphPad Prism v6.0f). Statistical significance is indicated with ns for > 0.05, * for  0.05, ** for  0.01, *** for  0.001 and **** for P ≤ 0.0001.
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6

Relative Gene Expression Analysis

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ΔΔCt values were used in the statistical analysis of relative gene expression. ΔΔCt values were transformed (2-ΔΔCt) and are shown as expression relative to uninfected or unstimulated control samples, as appropriate. [40 (link)]
Statistical analyses were performed using Prism v6.0f software (Graphpad Software, Inc.) Statistical comparisons were performed using a two-tailed Student’s t-test when comparing control to infected or stimulated samples; or a one-way ANOVA with Bonferri post-test analysis, when comparing multiple treatment conditions (in vitro BRDC studies).
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7

Statistical Analysis of Vaccine Effects

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The vaccinated group was compared with its corresponding untreated group by performing a two‐tailed unpaired Student t test with Welch's correction. A two‐tailed paired Student t test was used for comparison of N and R gates. Friedman test with Dunn's multiple comparison was used for comparison of multiple cell subsets within vaccinated mice samples. Differences were considered significant when *P ≤ 0.05; **P ≤ 0.01; ***P ≤ 0.001. Statistical analysis was performed using Prism v.6.0f, GraphPad Software (La Jolla, CA, USA).
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8

Profiling T Cell Subsets in Diabetes

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Data represent individual samples, mean (bars) ± SEM (error bars). Two-tailed Mann-Whitney U (two groups) and Kruskal-Wallis with Dunn's multiple comparison (more than two groups) statistical tests were used to compare different donor groups. Two-tailed Wilcoxon matched pairs signed rank (two groups) and Friedman with Dunn's multiple comparison (more than two groups) statistical tests were used to compare multiple T cell subsets from the same donors. Fisher's exact test was used to compare number of “TDS+ subjects between HD and T1D. P values were considered significant when *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. Statistical analysis was performed using Prism v.6.0f, GraphPad Software (La Jolla, CA, USA).
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9

Nonparametric Statistical Analysis of Data

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Statistical analysis was performed using Prism v6.0f software (Graphpad Software, Inc.). The data were analyzed using a Kruskal-Wallis test followed by Dunn’s Multiple Comparisons post test.
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10

Statistical Analysis of Immune Responses

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Statistical analysis was performed using Prism v6.0f software (Graphpad Software, Inc.). To account for time and repeated measures, antibody and T cell responses were analyzed using a two-way ANOVA with Sidak’s multiple comparisons test. DP T cell results (proliferation and cytokine production) were analyzed using a one-way ANOVA with Tukey’s multiple comparisons test. Experiments using sorted DP T cells were analyzed using student’s t-test. For proliferation and intracellular cytokine staining data, background (mock) responses were subtracted from the response to antigen and results are presented as change over mock.
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