Correlation coefficients were computed by Spearman’s and distance correlation analyses. Log-rank tests were utilized to identify the significance of differences in survival curves. The cut-off value mentioned in this article was 1⋅258 as the best cut-off value from the “survminer” package. ROC curves, time-dependent ROC curves, and the area under curves (AUC) were derived using the “pROC” and the “timeROC” packages, respectively. Comparisons of the integrated area under the curves (IAUC) were carried out with the “iauc.comp” package. The “RCircos” package allowed us to plot the copy number variation landscape of pyroptosis regulators in 23 pairs of chromosomes (Mayakonda et al., 2018 (link)). Analyses between the two groups were performed using the Wilcox test. The Kruskal Wallis test was also used to compare three or more groups. Gene expression data, and all statistical analyses were carried out in R 4.0.0, GraphPad Prism 8, and SPSS26 software. Clinical features were compared by Chi-square tests or Fisher’s exact tests where appropriate. All statistical P values are two-side and P < 0⋅05 represents statistical significance.
R 4.0.0
Manufactured by GraphPad
R (4.0.0) is a free, 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.
Automatically generated - may contain errors
Lab products found in correlation
8 protocols using r 4.0.0
1
Pyroptosis regulators in cancer prognosis
2
Survival Analysis of Clear Cell Renal Cell Carcinoma
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
In this study, the Mann-Whitney U test was performed to compare continuous variable. Kaplan–Meier curve analysis was carried out by using a log-rank test to compare OS. Cox regression analysis was performed to explore whether risk score could act as a prognostic factor for ccRCC. All statistical analyses were performed in R (4.0.0) and GraphPad Prism 8.
3
Multivariate Analysis of Risk Factors
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Univariate and multivariate Cox analyses were used to search for independent risk factors. The MannWhitney test or Wilcoxon signed-rank test was adopted for comparisons between two groups. The KruskalWallis one-way analysis of variance (ANOVA) was used in multiple groups. The predictive performance of MVP was assessed by the receiver operating characteristic (ROC) curve analysis. The KaplanMeier curve analysis and log-rank test were applied to calculate the prognostic results. The best MVP critical point for KaplanMeier curves was determined by res. cut function. The CCK-8 assay was analyzed by two-way analysis of variance. In all experiments, at least three biological replicates were performed for each group. R 4.0.0 and Graphpad Prism 8.3.0 were recruited in our work.
4
Statistical Analysis of Survival Data
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
All statistical analyses were conducted with R (4.0.0) software and GraphPad Prism 8. All the results were presented as mean ± standard deviation (SD). The student’s t-test was performed to compare the differences between the two groups. Differences in survival between different risk groups were compared by Kaplan-Meier curves followed by a log-rank test. p < 0.05 was regarded as statistically significant.
5
Statistical Analysis Techniques in Research
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
In this study, we used the Mann–Whitney U test to analyze the difference between continuous variables and the ANOVA test for continuous variables of more than two groups. Kaplan–Meier curve analysis was used to compare OS with the log-rank test. The Spearman test was used to test for correlation. All statistical analyses were performed in R (4.0.0) and GraphPad Prism 8. All statistical p values were two-sided p < 0.05, indicating statistical significance.
6
Statistical Analysis of Muscle Data
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
R 4.0.0 and Graphpad Prism 8.0.0 were used for statistical analyses. An unpaired student’s t-test for two groups and one-way or two-way ANOVA for more than two groups were used to generate p values. Pearson correlation analysis were used to measure the linear correlation of two variables. Quantitative data are presented as described in each figure, either as box plots including individual data points for each muscle analyzed along with minimum, first quartile (Q1), median, third quartile (Q3) and maximum numbers of the data set, or as bar plots with mean ± SEM. Outliers are identified as below Q1-1.5*(Q3-Q1) or above Q3+1.5*(Q3-Q1). Significance level is set at p < 0.05.
7
Statistical Methods for Survival Analysis
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
In this study, we used the Mann–Whitney U test to analyze the difference between continuous variables and the Analysis of Variance (ANOVA) test for continuous variables of more than two groups. Kaplan–Meier curve analysis was used to compare disease-free survival (DFS) with the log-rank test. Comparisons between different areas under the curve (AUCs) were performed through a nonparametric approach reported by DeLong et al.18 (link) All statistical analyses were performed in R (4.0.0) and GraphPad Prism 8.
8
Prognostic Model for Clear Cell Renal Cell Carcinoma
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
We use the edger package to normalize the gene count value, and then use the limma package to select genes with |log 2 FC|≥ 1 and FDR < 0.05 in the TCGA database.
Functional enrichment analysis.
In order to explore the potential mechanism by which our prognostic model can predict the prognosis of ccRCC patients, we performed a weighted gene co-expression network analysis (WGCNA) analysis using the previously identi ed differentially expressed genes in cancer and adjacent tissues. Whereafter, select the gene module most relevant to the risk score, extract the genes from the gene module and perform the Kyoto Encyclopedia of Genes and Genomes (KEGG) approach and genetic ontology (GO) analysis in Metascape [11] .
Statistical analysis.
In this study, the Two-sided Student's t-test and one-way ANOVA were used to detect statistically signi cant differences between groups. Kaplan-Meier curve analysis was carried out by using log-rank test to compare OS. Cox regression analysis were performed to explore whether risk score could act as a prognostic factor for ccRCC. All statistical analysis were performed in R (4.0.0) and GraphPad Prism 8.
Functional enrichment analysis.
In order to explore the potential mechanism by which our prognostic model can predict the prognosis of ccRCC patients, we performed a weighted gene co-expression network analysis (WGCNA) analysis using the previously identi ed differentially expressed genes in cancer and adjacent tissues. Whereafter, select the gene module most relevant to the risk score, extract the genes from the gene module and perform the Kyoto Encyclopedia of Genes and Genomes (KEGG) approach and genetic ontology (GO) analysis in Metascape [11] .
Statistical analysis.
In this study, the Two-sided Student's t-test and one-way ANOVA were used to detect statistically signi cant differences between groups. Kaplan-Meier curve analysis was carried out by using log-rank test to compare OS. Cox regression analysis were performed to explore whether risk score could act as a prognostic factor for ccRCC. All statistical analysis were performed in R (4.0.0) and GraphPad Prism 8.
About PubCompare
Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.
We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.
However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.
Ready to get started?
Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required
Revolutionizing how scientists
search and build protocols!