All data (with the exception of the liposome assays) were collected in triplicates and analyzed by one-way ANOVA (α ≤ 0.05), followed by a multiple-range Tukey’s post-hoc test using the GraphPad Prism Analysis software (GraphPad Software Inc., San Diego, CA, USA) to identify significant differences among treatment means (p ≤ 0.01). Data were obtained from three different individual experiments for the liposome assays and submitted to linear regression using GraphPad software. Rates of propagation (i.e., slope of regression equation) were then analyzed by one-way ANOVA (α ≤ 0.05) and Tukey’s test (p ≤ 0.01).
Prism analysis
Manufactured by GraphPad
Sourced in United States
Prism is a data analysis and graphing software developed by GraphPad. It allows users to analyze, visualize, and present scientific data. Prism provides a range of statistical tests and graphing options to help researchers interpret their findings.
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Lab products found in correlation
13 protocols using prism analysis
1
Comprehensive Statistical Analysis of Data
2
Prognostic Role of MDSCs in Cancer
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The numerical data are shown as the mean ± standard error (SEM). The statistical analysis was performed with the SPSS 13.0 software (SPSS, Chicago, IL, USA) or GraphPad Prism analysis tools (La Jolla, CA, USA). Two group comparisons were tested using Student’s t test, and the association of the density of the MDSCs with the clinical pathological features was examined using Pearson’s chi-square test. The overall survival (OS) was measured from the date of the diagnosis to the date of death from any cause or to the date of the last follow-up visit. The disease-free survival (DFS) was defined as the time from the diagnosis to the first occurrence of progression, relapse after a response, death from any cause, or to the date of the last follow-up of the surviving patients. The survival curves were determined by the Kaplan–Meier method and the log-rank test. A Cox proportional hazards regression analysis was performed to identify the independent prognostic factors for the OS or DFS. The cutoff value was the median of all variants. The statistical tests were based on a level of significance at P < 0.05.
3
Statistical Analysis of Experimental Data
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Statistical analysis was performed using GraphPad Prism analysis software (GraphPad Software, San Diego, CA, USA). Comparisons among two groups were performed using an unpaired t test, followed by Welch’s correction test. Comparisons among more than two groups were performed using a parametric one-way ANOVA test followed by Bonferroni’s multiple comparisons test. p ≤ 0.05 was considered statistically significant. The legend is as follows: ∗p ≤ 0.05; ∗∗p ≤ 0.01; ∗∗∗p ≤ 0.001; ∗∗∗∗p ≤ 0.0001; ns = not significant.
4
Statistical Analysis of Experimental Data
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Independent experiments were repeated at least three times. The data are presented as mean ± SD for all duplicates within an individual experiment. Data were analyzed by the Student’s t-test or multivariate analysis or one-way analysis of variance (ANOVA) and followed by the Tukey test for multiple comparisons to determine the differences between groups. Statistically significant differences are denoted by an asterisk. The analyses were performed using GraphPad Prism analysis software (GraphPad, La Jolla, CA, USA).
5
Comparative Analysis of Protein Expression
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Results are represented as mean ± SD. Statistical significance among means was determined with GraphPad Prism analysis software (Graph-Pad Software, San Diego, CA, USA) by Student’s t-test or ANOVA followed by Tukey test for multiple comparisons. Comparisons were considered significant at P < 0.05 and highly significant at P < 0.01.
6
Survival and Tissue Burden Analysis
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Survival data from the animal experiments were analyzed using a two-group Wilcoxon test (GraphPad Prism analysis software). An unpaired t test was used for evaluation of the CFU in tissue burden studies. Two-group comparisons were done with the Student’s t test. When P values of <0.05 were obtained, differences were considered statistically significant.
7
Drosophila Starvation-Survival Behavior
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All statistics were computed using the Prism analysis package (GraphPad v.9). Starvation-survival curves were analyzed using Kaplan–Meier log-rank tests or Gehan–Breslow–Wilcoxon test. Other data were assessed for normality before comparisons were performed. For normally distributed data, pairwise comparisons were made using two-tailed unpaired Student’s t-tests and multiple samples were compared using one-way analysis of variance (ANOVA) with post hoc multiple-comparisons tests. Other data were compared using two-tailed unpaired Mann–Whitney U-tests or one-way Kruskal–Wallis ANOVA followed by multiple-comparisons tests. Bar plots show the mean plus or minus the standard error of the mean (s.e.m.). Box plots that show the median and the first and third quartile, with whiskers indicating the full range of values. No data were excluded. Sample size was chosen on the basis of similar previously published studies of Drosophila behaviour and metabolism17 (link),18 (link),20 (link),55 (link). No sample-size calculations were performed.
8
Quantifying Mitochondrial Network Structure
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The mitochondrial network structure visualized by MitoTracker® Green or Tom20 immunofluorescence staining was investigated using Imaris software (Oxford Instruments/Bitplane, Zurich, Switzerland). Mitochondria were automatically detected from 3D images by generating a surface on the fluorescence intensity channel after background subtraction and the volume for each detected object (mitochondrion) per cell was quantified. Finally, the geometric mean and the coefficient of variation of the mitochondrial volume were calculated using the Prism analysis program (GraphPad Inc., San Diego, CA, USA). An additional count was made of large objects >30 times the mean volume, and these were defined as mitochondrial clusters.
9
Statistical Analysis of Experimental Data
10
Statistical Analysis of Experimental Data
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Data were analyzed by ANOVA, followed by Tukey’s multiple comparisons test using the Prism analysis program (GraphPad 6.01). Brown–Forsythe test (sensitive to departures from normality) and Bartlett’s test were used to assess normality distribution and variance homogeneity. Results were expressed as means (±SEM) of the indicated number of observations; differences were considered significant when p values were <0.05.
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