Pearson’s correlation coefficient (R) was used to assess the agreement between QIF scores and DSP counts from near serial sections of Yale Cohort A (YTMA 356). Overall survival (OS) and Progression Free Survival (PFS) curves were constructed using the Kaplan-Meier analysis with a follow up of 60 months and statistical significance was determined using the log-rank test. For the statistical analysis, the average NanoString counts from two available cores of each case was used. All p-values were based on two-sided tests and p-values <0.05 were considered statistically significant for median stratification. For markers stratified by any other cutpoint, the significance cut-off was set after Bonferroni correction for multiple comparisons. Specifically, for markers stratified by tertiles, a p-value<0.0167 was considered significant while for quartile stratification a difference would be considered statistically significant if the p-value<0.0083. Statistical analyses were performed using IBM SPSS Version 20 (IBM Corp., Armonk, NY), JMP Pro software (version 11.2.0, 2014, SAS Institute Inc, Cary, NC) and GraphPad Prism v6.0 for Windows (GraphPad Software, Inc, San Diego, CA). All tumor spots were visually evaluated and cases with staining artifacts or presence of less than 2% tumor compartment area were systematically excluded.
Prism v6.0 for windows
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GraphPad Prism v6.0 for Windows is a software package designed for data analysis and graph visualization. It provides tools for curve fitting, statistical analysis, and creating publication-quality graphs. The software is intended to be used for scientific and medical research purposes.
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13 protocols using prism v6.0 for windows
1
Correlating Tumor Biomarkers with Survival
2
Statistical Analysis for Experiments
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Statistical analysis for each experiment was performed between all the conditions, for each timepoint analyzed. Statistical differences were determined using one-way analysis of variance (ANOVA) with a Tukey multiple comparison test. We used GraphPad Prism v6.0 for Windows software (GraphPad Software Inc.) to calculate the mean, the standard error of the mean (SEM), and to perform the statistical tests. A value of p < 0.05 denoted statistical significance.
3
Survival Analysis of Biomarker Expression
4
Quantitative Immunohistochemistry Analysis
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Immunohistochemistry scoring of the markers was performed by two pathologists on lymph node and spleen sections. For each slide evaluated, pathologists interpreted staining at 40× high power field and counted positive cells per background cell were appropriate (such as Ki67 quantification and CD4/CD8 ratio). For cytotoxic marker evaluation on immunohistochemistry, which is more difficult to ascertain on a per cell basis, approximate level of positivity was recorded (−, −/+, +, ++). Comparison of QIF scores for different markers across responders and non-responders was performed by Mann–Whitney analysis. Each patient case was represented by the 10 highest scored FOV for both lymph nodes and spleen. All P values were based on two-sided tests and any P value < 0.05 was considered statistically significant. Statistical analysis was performed using JMP Pro software (version 11.2.0, 2014, SAS Institute Inc, Cary, NC, USA) and GraphPad Prism v6.0 for Windows (GraphPad Software, Inc, San Diego, CA, USA).
5
Standardizing Tumor-Infiltrating Lymphocyte Evaluation
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For statistical analysis, QIF scores for each marker were log2 transformed to minimize the possible effects of the differential score distribution across cases. Pearson’s correlation coefficients (CC) and intraclass correlation coefficients (ICC) were calculated for each marker to measure the similarity of marker scores between FOVs, section to section in the same biopsy, and between biopsies from the same tumor. The variance components analysis used a linear mixed effect (LME) regression model was used to estimate the contribution of each source of variation to the total variation observed. Biopsies were also categorized into lymphocyte low versus high groups using the median for each lymphocyte subtype marker. Cohen’s kappa coefficient (k) was then used to calculate the concordance in TIL category obtained from assessing a single, randomly chosen biopsy versus the averaged results from all three biopsies from a given cancer. Statistical analyses were performed using the R v3.2.2 statistical platform (R Foundation for Statistical Computing) and GraphPad Prism v6.0 for Windows (GraphPad Software, Inc, San Diego, CA, USA).
6
Stress Effects on Oral Microbiome
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The sample size was calculated a priori, considering a difference of 30% between the means of the two groups, and a standard deviation of 20% of the mean. In this scenario, a sample size of 12 volunteers in both groups provides 99% of power, considering 5% for alpha. The study included 78 volunteers (21 in a stressed condition and 57 control subjects), representing almost all the male undergraduate students enrolled at the faculty during the study period. The data distribution was tested using the Shapiro-Wilk test. Non-normally distributed data (bacterial quantification and clinical VSCs determination) were analyzed using the Mann-Whitney U test. Spearman correlation coefficients (rS) were calculated for VSCs production and bacterial quantification, with rS values of 0.2–0.4, 0.4–0.8, and >0.8 considered to indicate weak, moderate, and strong correlations, respectively [33 (link)]. For bacterial quantification, the data were expressed as percentages representing the proportion of each species relative to the total quantification. The significance level was set at 5%, and all analyses were performed using GraphPad Prism v. 6.0 for Windows statistical software (GraphPad Software Inc., Los Angeles, USA).
7
Comparative Analyses of QIF Signals
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QIF signal differences between groups were analyzed using t-test for continuous variables and chi-square test for categorical variables. Linear regression coefficients were calculated to determine the association between continuous scores. Survival analysis based on marker expression was performed using Kaplan-Meier analyses with log rank test and overall survival as endpoint. Statistical significance was considered at P < 0.05 and analyses were performed using JMP® Pro software (version 9.0.0, 2010, SAS Institute Inc.) and GraphPad Prism v6.0 for Windows (GraphPad Software, Inc). All statistical tests were two-sided.
8
Glucose Uptake Quantification Methods
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Data were analyzed using Microsoft Excel 2010 and GraphPad Prism V.6.0 for Windows (GraphPad Software Inc) software. Differences in glucose uptake or glucose concentration in culture media were analyzed for significance by two-tailed Student t test or one-way analysis of variance with Tukey's or Bonferroni's post hoc tests (as indicated in the description to figures). Significance was defined as p<0.05.
9
Evaluating Marker Expression in AA and NAA
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We used the median QIF scores of every marker from all FOV in each section to represent marker expression at the section level. Statistical comparisons between AA and NAA population were made using the Mann–Whitney U test except for comparison of T-cell status for which Fisher’s exact test was used. Statistical analyses were performed using the GraphPad Prism v6.0 for Windows (GraphPad Software, Inc, San Diego, CA, USA). Significance is defined as p < 0.05.
10
Quantification of P. salmonis in Macrophages
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Statistical differences in the quantification of P. salmonis recovered from macrophage-enriched cell cultures were determined using the one-tailed Mann–Whitney U test. Statistical differences in proteolytic events between the groups analyzed were determined using two-way analysis of variance (ANOVA) with a Dunnett's multiple comparison test. We used the GraphPad Prism v6.0 for Windows software (GraphPad Software Inc.) to calculate the mean and the standard error of the mean (SEM) and perform the statistical tests. A p < 0.05 denoted statistical significance.
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