R language Software (version 3.3.4, The R Foundation for Statistical Computing) and Prism 8 (GraphPad Software, USA) were used for plotting and analysis. Survival analysis was statistically analyzed using the KM survival curve and log-rank analysis. The data were tested for normal distribution. The Student’s t test was used for statistical comparison between the two groups conforming to the normal distribution. One-way analysis of variance (ANOVA) is used for statistical comparison between groups that conform to normal distribution. Wilcox nonparametric test was used for data that did not conform to normal distribution. P < 0.05 was considered statistically significant.
R language software
Manufactured by R Project for Statistical Computing
Sourced in United States
R is an 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, clustering, and more. R is an interpreted language, which means that users can execute commands directly within the R environment.
Automatically generated - may contain errors
Lab products found in correlation
12 protocols using r language software
1
Statistical Analysis of Survival Data
2
Differential Gene Expression in HGSOC
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The DEGs were obtained by conducting differential expression analysis. The downloaded platform and series of matrix file(s) were converted using the R language software (version 4.0.5; R Foundation, Vienna, Austria) and annotation package. Gene differential expression analysis was performed using the limma package in the Bioconductor package (available online:http://www.bioconductor.org/ ). We divided the samples into two subgroups, namely the primary tumor group (PT) and the metastasis tumor group (MT). By employing absolute |log2 fold change (FC)| >1 and P <0.05, DEGs, including significantly upregulated and downregulated genes between HGSOC primary tumors vs. matched metastasis tissues in every GSE dataset, were identified.
3
Photosynthetic Response to Wheat Blotch
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To evaluate the differences in Fv/Fm, Y(II), Y(NPQ), Y(NO), ETR, qP, and qN between the different severity scales of WB, a linear mixed model (LMM) was fitted. For this purpose, the fixed effects within the LMM were severity level and PAR, as well as their interaction. The AOI, leaves, and plants within the plots were considered as random effects. The assumptions of normality and homogeneity of variances were validated by means of an exploratory analysis of the residuals. Likewise, a Fisher’s LSD post-hoc mean comparison test was performed, with a significance of α = 0.05. The graphs were displayed in quadrants with the corresponding means and statistical significance on each axis. To determine the relationship between the disease severity scale and the photosynthetic variables, a Pearson’s correlation analysis was performed, and the results were presented graphically using a heat map at the general level, and a chord diagram was used for the most contrasting severity scales using the “corrplot” [89 ] and “circlize” [90 (link)] packages. LMMs were performed with the lme function of the nlme package, and graphical outputs were performed in the packages “ade4”, “ggplot2”, “factoextra”, and “dplyr” in R language software, version 3.4.4 (R Foundation for Statistical Computing, Vienna, Austria) [91 ], using the interface in InfoStat [92 ].
4
Identifying miRNA Target Genes
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According to the results of differentially expressed miRNAs, the prediction of gene targets of significantly differentially expressed miRNAs was performed by the Targetscan (www.targetscan.org ), miRTarbase (https://miRTarBase.cuhk.edu.cn ) and miRDB (www.mirdb.org ). The following Venn diagram was created using the R language software (R Foundation for Statistical Computing, Vienna, Austria).
5
Predictive Model for Colorectal Cancer Liver Metastasis
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In this study, we used X-tile, SPSS 26.0, and R language software (version 3.6.1, www.r-project.org ) for statistical analysis. A range of software packages were used in R language software, including ROCR, rmda, foreign, and survival, to plot ROC and K–M curves. p values were calculated by the chi-square test for categorical variables. p < 0.05 was considered to be statistically significant. In this study, according to the treatment methods in previous studies [14 (link)], the variables with P < 0.05 in univariate Cox regression analysis were included in the multivariate Cox regression analysis for further analysis to observe their synergistic effect. We calculated the best cutoff value, sensitivity for, specificity for, and area under the curve by ROC to predict the model of DLR in the diagnosis of CRCLM. Cox regression analysis using dichotomous variables was used to screen and identify the DLR as a prognostically relevant independent risk factor.
6
Statistical Analysis with R Software
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Statistical analysis was performed using R language software (version 4.0.2, https://www.r-project.org ).
7
Statistical Analysis of Research Data
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As for descriptive variables (i.e. continuous or classified variables), the median (interquartile range) or frequency (percentage) were used for statistical analysis. The χ2 test or Mann–Whitney test was used to calculate the variables between groups to evaluate whether there was a statistical difference. Stepwise regression based on the minimum value of the Akaike information standard was used to select the variables. All data analysis was completed with the help of R language software (version 4.0.4, http://www.r-project.org/ ). All P values were double tailed, and P < 0.05 was statistically significant.
8
Statistical Analysis of Omics Data
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Statistical analysis was performed using the R language software (version 4.2.0) (https://www.r-project.org ) and GraphPad Prism v9.0. ANOVA and Wilcoxon's test were used to analyze differences between two and more groups, respectively. A log-rank test and Kaplan-Meier explanation were used to estimate the difference in OS between groups. The subtypes, clinicopathological features, risk scores, immune checkpoint expression, methyltransferases, and levels of immune infiltration were determined using Pearson's correlation test. The results were found to be statistically significant (P<0.05).
9
Statistical Analyses in R Software
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The R language software (version 4.0.2) (https://www.r-project.org/ ) was used for statistical analyses. Continuous variables were defined as mean ± standard deviation (SD) or median (interquartile range). For continuous variables, t-test or a one-way analysis of variance (ANOVA) model was used for comparisons. The Spearman rank test was used to test correlations. Variables with a P<0.05 in the univariate analysis were subjected to multivariate analysis using a Cox proportional hazards model to determine independent prognostic factors. The Kaplan-Meier method and the log-rank test were used for survival analysis. All tests were two sided, and P value <0.05 was considered statistically significant.
10
Radiomics Signature Construction and Analysis
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During the construction of the radiomics signature, the R language software (Version 3.6.1, https://www.r-project.org/ ] was used for all statistical analyses. The Shapiro–Wilk test was used to test whether the variables were normal distributions. Bartlett’s test was used to assess the homogeneity of variance. The “lme4” and “psych” packages were used for the intra-class correlation coefficient (ICC). The “glmnet” and “pROC” packages were used for LASSO regression. Basic clinical data were analyzed by Statistical Package for the Social Sciences software (version 25.0). Clinical characteristics were measured based on the variable type. Categorical variables were measured as percentages, and Fisher’s exact or chi-square test was used for comparison, depending on the expected frequencies. Continuous variables were recorded as mean values or medians, and were compared by independent t-tests (normally distributed continuous variables) or the Mann–Whitney U tests (non-normally distributed continuous variables). A two-tailed p value < 0.05 was considered statistically significant in all statistical analyses.
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