The optimal cutoff value for count of each immune cell was calculated with “survminer” package in R. The LASSO Cox regression model was generated to integrate features of prognostic related immune cells with ideal coefficient for survival prediction using training set by “glmnet” package. The OS was analyzed by Kaplan–Meier method and log rank test. Univariate and multivariate Cox regression analysis were performed to identify independent prognostic variables for survival. In the validation set, differential gene expression was analyzed using “edgeR” package, and the pathways which enriched between the two groups were calculated with gene set enrichment analysis (GSEA).20 (link) Statistical analysis was performed with R software (version 3.5.1) and SPSS (version 19.0). Statistical significance was set at 0.05.
R software version 3.5.1
Manufactured by IBM
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 others. R is extensively used in various fields for data manipulation, analysis, and visualization.
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Lab products found in correlation
5 protocols using r software version 3.5.1
1
Immune Cell Profiling for Survival Prediction
2
Survival Analysis of Lung Adenocarcinoma
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Survival analysis was conducted using the Kaplan-Meier (KM) method, and a log-rank test was used for comparison. Chi-square analysis was used to evaluate the relationship between LUAD classification and clinical features. Unpaired Student’s t-test and Mann-Whitney U-test were used to comparing two groups with normally distributed variables and non-normally distributed variables, respectively. One-way analysis of variance of parametric and Kruskal-Wallis tests of nonparametric variance were used to compare the three groups. Univariate COX and multivariable Cox regression were conducted to evaluate the molecular clusters and classifier using the clinical signatures as concomitant variable (Table S2 ). All analyses were conducted using R software (version 3.5.1) and SPSS software (version 24). A two-tailed p-value <0.05 was considered statistically significant.
3
Evaluation of HCC Differentiation Score
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The discriminative accuracy of the HCC differentiation score was evaluated with area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. Model calibration was assessed by the calibration curve with the Hosmer–Lemeshow test. Decision curve analysis was performed to investigate the clinical usefulness of the HCC differentiation score by quantifying the net benefits at different threshold probabilities.
The R software (version 3.5.1) and SPSS (version 26) were used to perform the statistical analysis. Two- tailed p ≤ 0.05 was considered statistically significant.
The R software (version 3.5.1) and SPSS (version 26) were used to perform the statistical analysis. Two- tailed p ≤ 0.05 was considered statistically significant.
4
Mycotoxin Levels and Water Activity Analysis
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All the descriptive statistics were conducted using the R software version 3.5.1, SPSS version 20 and Microsoft excel version 13. A non-parametric Kruskal–Wallis test and a post hoc Games–Howell and Dunn’s test for pairwise comparisons at a significance level of α = 0.05 were performed. Mycotoxin levels were displayed in median ± SE (standard errors). The water activity of 230 samples was analyzed using one-way ANOVA in IBM SPSS Statistic version 20.0. Data of the interview were analyzed by Microsoft Excel 2013.
5
Multivariate Analysis of Lung Cancer Survival
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GraphPad prism 8, R software version 3.5.1 and SPSS 23 were used to plot the gures. Differences between groups were assessed by two-tailed Student's t test. The strength of the association between continuous variables was tested with Pearson's correlation test. Uni-and Multi-variate Cox regressions were used to identify independent risk factors of LUAD. For survival analysis, overall survival was calculated using the Kaplan-Meier method and the log-rank test. All P values were two sided and P value < 0.05 were considered to be statistically signi cant.
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