R software platform
Manufactured by R Project for Statistical Computing
R is a free and open-source software environment for statistical computing and graphics. It provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, and clustering. R is an interpreted language, allowing for interactive data exploration and analysis.
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Lab products found in correlation
9 protocols using r software platform
1
Statistical Analysis in R
2
Statistical Analysis of Biological Data
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Heatmaps, boxplots and statistical tests were performed within the R software platform (http://www.r-project.org/ ) using the ggplot2 (http://had.co.nz/ggplot2/ ) and vegan (http://vegan.r-forge.r-project.org/ ) packages. Data are expressed as mean±s.d., and differences are considered significant at P<0.05.
3
Biomarkers for Type 2 Diabetes
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Statistical analysis was performed using the R software platform (version 3.6.1, the R Project for Statistical Computing, the R Foundation, Vienna, Austria). Three‐dimensional modeling of participant features was completed using the Plotly open‐source graphical library in R. Descriptive statistics, which report medians and interquartile ranges (IQR), are displayed in order to summarize participant characteristics. Wilcoxon and χ2 tests were performed to calculate the significance (p value) of differences in biomarkers between participants with T2D and those without T2D. P < 0.05 was deemed significant following a Bonferroni correction. To calculate the associations between participant biomarkers and T2D, we performed univariate and stepwise multivariate logistic regression modeling using the brglm2 package in the R software platform (version 3.6.1). Model inputs included the following: pancreas fat, liver fat, liver cT1, VAT, SAT, SMI, HDL‐cholesterol (HDL‐C), triglycerides, AST, ALT, BMI, waist circumference, age, and gender. Risk scores and confidence intervals (CI) were calculated against the odds ratios of participants who had self‐reported T2D. Spearman correlation tests were calculated to investigate the relationships between biomarkers.
4
Fetal Growth and Brain Development
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Treatment group differences in fetal birthweight and fetal tissue weights were assessed using Welch’s t-test, and in histological assessments using a Mann–Whitney t-test. All statistical tests were two-sided and used an alpha of 0.05. Linear mixed effect models (lme4 package)75 (link) considering age, sex, and treatment as fixed effects including interactions, with fetus as a random effect were used to analyze regional and whole brain volumes across gestation using the R software platform (http://www.R-project.org ). Reported p-values were calculated using the Satterthwaite approximation method76 .
5
Statistical Analysis in R
6
Two-Sample Mendelian Randomization Analysis
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Evidence for a causal relationship between JIA and selected outcomes was explored using 2SMR implemented using the TwoSampleMR package (11 (link)) in the R software platform (version 1.2.5019; R Foundation for Statistical Computing) (12 ). The selected JIA instruments were looked up in the outcome GWAS data sets from the MR Base database (11 (link)). LD proxies (r2 > 0.8) were used where the instrument was not available in the outcome GWAS. The alleles were harmonized to ensure that the SNP exposure effect and the SNP outcome effect corresponded to the same allele. Palindromic SNPs were aligned using default settings, and noninferable palindromic SNPs were excluded to limit effect allele ambiguity between JIA and outcome data sets. We applied the inverse variance weighted (IVW) method to estimate the effect of JIA on selected outcomes. Briefly, the IVW method calculates a weighted average of effect estimates obtained using each individual SNP (via a Wald ratio) through a fixed‐effect meta‐analysis. SNPs are typically weighted by their corresponding inverse variance (13 (link)).
7
Statistical Analysis of Research Protocols
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All statistical analyses were conducted using the R software platform (v4.1.0, R Foundation for Statistical Computing, Vienna, Austria). Key R packages utilized included "limma," "survival," "ROCR," "ggplot2," and "caret."
For the comparison of variables across multiple groups, parametric factors were assessed using Student's t-test and ANOVA analysis, while nonparametric factors were evaluated using the Wilcoxon rank-sum test and Kruskal-Wallis test.
To measure the correlation between different variables, both Spearman's rank-order correlation and Pearson's r correlation were employed. Survival analysis was conducted using Kaplan-Meier analysis along with the log-rank test. To assess the efficacy of receiver operating characteristic curves, the area under the curve (AUC) was calculated. In all statistical analyses, significance was determined by a two-tailed P-value of less than 0.05.
For the comparison of variables across multiple groups, parametric factors were assessed using Student's t-test and ANOVA analysis, while nonparametric factors were evaluated using the Wilcoxon rank-sum test and Kruskal-Wallis test.
To measure the correlation between different variables, both Spearman's rank-order correlation and Pearson's r correlation were employed. Survival analysis was conducted using Kaplan-Meier analysis along with the log-rank test. To assess the efficacy of receiver operating characteristic curves, the area under the curve (AUC) was calculated. In all statistical analyses, significance was determined by a two-tailed P-value of less than 0.05.
8
Comprehensive Statistical Analysis of Biomedical Data
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All statistical analyses were conducted with the R software platform (v4.0.2, R Foundation for Statistical Computing, Vienna, Austria). Some major R packages included “edgeR,” “limma,” “survival,” “ROCR,” “ggplot2,” “pRRophetic,” and “randomForestSRC”. To compare variables in multiple groups, Student’s t-test and ANOVA analysis were used for parametric factors, whereas the Wilcoxon rank-sum test and Kruskal–Wallis test were applied for nonparametric factors. To measure the correlation of different variables, Spearman’s rank-order correlation and Pearson’s r correlation were set. Furthermore, Kaplan-Meier and the log-rank test were used for survival analysis. The area under the curve (AUC) was measured to judge the efficacy of the receiver operating characteristic curve. For all statistical calculations, a two-tailed p < 0.05 was considered significant.
9
Parathyroidectomy and Carpal Tunnel Syndrome
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We calculated the number and proportions according to sex and comorbidities between the PTX and non-PTX groups and compared them using Pearson's chi-square test. We measured the mean age and corresponding standard deviation (SD) and analyzed differences by using Student's t test. For each group, we calculated the incidence density of CTS and plotted the cumulative incidence curve by using the Kaplan–Meier method. Differences were determined using the log-rank test. To clarify the association between PTX and CTS, we used single and multivariable cox proportional hazard models to estimate hazard ratios (HRs) and corresponding 95% confidence intervals (CIs). A sensitivity analysis was also conducted and applied in the competing risk analysis. Because death might result in study bias, the competing risk model, developed from the standard Cox model, was used to estimate subhazard ratios (SHRs) and 95% CIs to compare the risk of CTS between maintenance dialysis patients with and without PTX. SAS statistical package (version 9.4; SAS Institute Inc., Cary, NC) was used to analyze all data. The cumulative incidence curve was plotted using the R software platform (R Foundation for Statistical Computing, Vienna, Austria). Statistical significance was determined for two-tailed P values lower than .05.
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