The analysis was carried out using SPSS Version 22.0 for Macintosh (IBM Corporation, Armonk, NY, USA). The Shapiro-Wilk test was employed to assess whether the data were normally distributed. Paired and independent t-tests (Wilcoxon matched-pair signed-rank test and Mann-Whitney U test for nonparametric, respectively) were used to assess differences between the groups. The Pearson correlation coefficient (Spearman for nonparametric) was calculated to assess the correlations between different variables. All data are expressed as mean ± SD. P < 0.05 was considered significant. The sample size calculation was measured using G*Power software Version 3.1.9.4 for Macintosh (Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany). With the effect size of 0.5, α of 0.05, and β of 80%, a minimal sample size of 27 subjects was calculated to establish a change. The graphs were created using GraphPad Prism Version 7.0c for Macintosh (GraphPad Software, La Jolla, CA, USA).
Prism version 7.0c
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Prism version 7.0c is a data analysis and graphing software developed by GraphPad. It allows users to create publication-quality graphs and perform statistical analysis on their data.
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Lab products found in correlation
11 protocols using prism version 7.0c
1
Analyzing Biometric Differences in Health Groups
2
Immunophenotyping of TNF-stimulated cells
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Normalized, concatenated, and debarcoded files were imported in Cytobank. Data were cleaned for doublets, debris, and dead cells by biaxial gating. viSNE analysis based on t-distributed stochastic neighbor embedding was performed for each donor and patient after downsampling to 50,000 cell events per individual and condition (20 (link)), and cell subsets were gated on individual viSNE plots (Supplementary Figure 6 ). Expression of functional markers was compared in all cell subsets, both unstimulated and TNF-stimulated, by applying non-parametric Mann-Whitney U tests using GraphPad prism version 7.0c for Mac OS X. A correction for multiple comparisons was not conducted due to the explorative character of this study.
3
Comparing Mouse Experimental Groups
4
Statistical Analysis for Biological Experiments
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Statistical analysis was performed using either R for the microarray and proteomic analysis, or for all other experiments in Prism version 7.0c (Graphpad). Data is presented as standard deviation (SD) or standard error of the mean (SEM). For all experiments a minimum of 3 biological replicates was used. Comparison between two paired groups employed a paired t-test, for comparison of 3 or more conditions a one-way analysis of variance (ANOVA) with Tukey’s post-test was performed. For comparison of multiple observations in more than two groups a two-way ANOVA with Tukey’s multiple comparison post-test was performed.
5
Statistical Analysis of Experimental Data
6
Pangenome Analysis of Microbial Genomes
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Gene content matrices were obtained for all species groups with more than 50 isolates using the pangenome analysis program Roary v3.11 (57 (link)). Roary was run using a protein identity cutoff 80% for genera containing multiple species, and a cutoff 95% for individual species. Pangenome collector’s curves were generated for each species group by calculating the number of unique genes present at increasing numbers of sampled genomes, with 1000 iterations of each sample size up to 250. Genetic clustering of genomes within species groups based on variable gene content was calculated and visualized using principal-component analysis of accessory genes (PCA-A) using the R packages vegan v2.5-7, and ggbiplot v0.55, with matrices of gene presence/absence used as input. Genes that were present in all isolates, present in only one isolate, or absent in only one isolate, were removed from analysis. PCA-A coordinate plots were visualized using GraphPad Prism version 7.0c.
7
Statistical Analysis of Biological Data
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Statistical analyses were performed using Prism version 7.0c (GraphPad) or R software version 3.4.4. Significance was assigned at p < 0.05, unless stated otherwise. Nonsignificant differences are not depicted for simplicity. Specific tests are indicated in the relevant figure legends. Statistical analysis was performed from at least three independent experiments, where applicable, and the number of experiments is indicated in the figure legends. Otherwise, independent biological replicates were included in a single experiment. Justifications for data exclusion are present in the “Methods” section. The sample size was chosen based on previous experience.
8
Bland-Altman Analysis of Ocular Biometry
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Unless otherwise noted, all statistical tests (including the Bland-Altman analyses) were performed using Prism version 7.0c (GraphPad, LaJolla, CA). The bias, limits of agreement (LOA), and 95% confidence intervals (CIs) for the bias and LOA were calculated following the methods of Bland and Altman.32 (link)–34 (link, link) For all data sets, normality was assessed using the Shapiro-Wilk normality test. Where normality could not be confirmed, nonparametric tests were used. The specific tests used are included alongside each result, as appropriate. Intraclass correlation coefficients (ICCs) were calculated for log-transformed HW and VW measurements using R statistical software (Foundation for Statistical Computing, Vienna, Austria). Variance components models fitted separately for HW and VW data were used to evaluate the contributions of subject, observer and reading within observer (trial) to the total variance of the measurements (SAS version 9.4; SAS, Cary, NC).
9
Statistical Analyses for Research
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Statistical analyses were performed using Prism version 7.0c (GraphPad) or R software version 3.4.4. Significance was assigned at P < 0.05 unless stated otherwise. Specific tests are indicated in the relevant figure legends.
10
Statistical Analyses for Research
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