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Rstudio 2021

Manufactured by Posit
Sourced in United States, Austria

RStudio is an Integrated Development Environment (IDE) for the R programming language. It provides a user-friendly interface for writing, testing, and executing R code. RStudio 2021.09.1 is the latest version, released in September 2021.

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13 protocols using rstudio 2021

1

Quantitative Exocytosis Analysis Protocol

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All analysis was conducted blinded to the genotype. Data analysis and statistics were done using Microscoft Excel 16.63.1 and GraphPad Prism 9.0 software. Heat map representations of exocytosis events were produced using Matlab R2021b and RStudio 2021.09.0. Descriptive statistics (mean, standard error of mean, and N) were reported in Figure Legends or Supplementary Tables. Statistical tests are stated in the Figure Legends. For cellular assays, the biological replicate represented the mean of technical replicates from one mouse brain. In figures with single cell analysis, we represented the distribution of data in the SuperPlots format to depict cell-level and experimental variability107 (link).
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2

Statistical Analysis of Sample Differences

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The Smirnov test was performed to analyze the type of distribution. Differences between samples were evaluated by one-way ANOVA followed by the Tukey test. A value of p < 0.05 was considered significant. Data were presented as the mean ± standard error of the mean (SEM). Data were analyzed using RStudio 2021.09.0.
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3

Substance Dependence and Mental Health Predictors

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This analysis used the routine service clinical intervention dataset. Multiple linear regression was used to test if the magnitude of difference in SDS score between intake and the last recorded timepoint could be predicted by the following variables: gender (6 self-reported non-binary participants were omitted from analysis), Aboriginal and/or Torres Strait Islander identity, SDS intake tertile group (1 = lowest substance dependence, 2 = middle, 3 = highest substance dependence), and the difference in K10 and EQoL scores between intake and the last recorded timepoint. An analysis of residuals confirmed the assumptions of linearity.
All data analysis was conducted using RStudio 2021.09.0.
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4

Evaluating Reporting Quality in EEs

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Descriptive statistics summarizing the characteristics of the included EEs, the condition of CHEERS items and the results of CHEERS scores were reported by Microsoft Excel 2020. The Kruskal–Wallis test and Mann–Whitney U test were performed to explore the potential relationship between reporting quality and various characteristics of EEs using RStudio 2021.09.0. A p-value of < 0.05 was considered as the threshold for statistical significance.
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5

Demographic and Clinical Differences Analysis

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Statistical analyses were conducted using RStudio 2021.09.0. Demographic (age and education) and clinical (CDR, FTLD‐CDR) variables were examined across groups via analysis of variance (ANOVA) with post hoc comparison using the Dunnett‐Hsu test. Sex was compared by chi‐square. A significance level of p < 0.05 was considered statistically significant.
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6

Differential Gene Expression Analysis

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R software 4.1.2 and RStudio 2021.09.1 + 372 (Boston, USA) were used for data analysis and visualization. The comparison of gene expression between two groups was assessed by the two-tailed unpaired t-test using GraphPad Prism 9.0 (California, USA). The results were presented as means and standard deviations. Unless otherwise stated, the p value < 0.05 was considered statistically.
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7

Prostate Cancer Biomarker Discovery

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The statistical analyses were performed with statistical software RStudio 2021.09.1. The Shapiro–Wilk test was employed for assessing the normality of the distribution of results, while the F-test was used to analyze the equality of variances in study groups. Two-tailed Student’s T-test was used for paired comparisons of normally distributed results, while the Mann–Whitney U test was applied to analyze the data that significantly deviated from the normal distribution. P values < 0.05 were considered as statistically significant. The results of microRNA expression quantification were correlated with serum PSA levels using a linear regression analysis. The results were presented as Pearson correlation coefficients (r) and the corresponding p values. The area under the curve (AUC) was calculated through a receiver-operating characteristic (ROC) curve analysis to determine the sensitivity and specificity of the potential biomarkers. Patients with missing data on some of the prognostic parameters were excluded from the association analyses relevant for that exact parameter.
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8

Comparative Statistical Analysis Methods

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Statistical analyses were conducted using RStudio 2021.09.1. Differences between groups were assessed using the Kruskal–Wallis test with subsequent pairwise Nemenyi testing. Correlations were assessed using Spearman rank correlation. For all analyses, p values of < 0.05 were considered statistically significant.
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9

Statistical Analysis of Metric Variables

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To compare distributions between metric variables, the Mann-Whitney-U, Kruskal–Wallis and Wilcoxon signed-rank tests were used as appropriate, supplemented by non-parametric methods for longitudinal data [26 (link)]. A two-sided p < 0.05 was considered as a significance threshold. Due to the exploratory and hypothesis-generating design of the present study, no adjustment for multiple testing was applied [27 (link)]. Statistical analysis was performed using GraphPad Prism 9.3.0 (La Jolla, CA, USA) and R 4.1.2 (The R Project for Statistical Computing, Vienna, Austria) with RStudio 2021.09.1 (RStudio Inc., Boston, MA, USA).
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10

Detailed Protocol for Colon Assembloid Analysis

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Data are expressed as mean ± SEM. P < 0.05 was considered significant. Student’s t-test (two-tailed) was used to determine differences between two groups. One-way ANOVA followed by respective post-hoc analysis (Tukey’s test) was used for statistical comparison of more than two groups. Data are displayed for at least three independent biological replicates, except for the scRNA-seq of stromal cells from assembloids and epithelial cells from assembloids, organoids, and colon tissue, for which two biological replicates per group were used. In Figs. 1b–e, h; 2a, b, f; 3d, e; 4a, h; Supplementary Figs. 1a, c-h; 2c, d; 4a–c; 5a; 8a, b, e–g, at least three independent experiments were performed with similar results. In Figs. 1g, 3g, 4c; Supplementary Fig. 2a, b, the average percentage of specific epithelial cells from three biological replicates was analyzed. In Figs. 3c, h, 4d–f; Supplementary Fig. 7a, e, signal areas in random fields of view from three biological replicates were analyzed. In Supplementary Fig. 8c, d, all organoids in three Matrigel drops each group from three biological replicates were analyzed. GraphPad Prism 8, R 4.2.2, and RStudio 2021.09.1 were used for data visualization and statistical analysis.
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