Visualise survival analysis based on INCOMMON classes.

Usage

plot_survival_analysis(
  x,
  tumor_type,
  gene,
  cox_covariates = c("age", "sex", "tmb")
)

Arguments

x: A list of objects of class 'INCOMMON' containing the classification results for multiple samples, as produced by using function classify.
tumor_type: The selected tumour type.
gene: The selected gene.
cox_covariates: A character vector listing the covariates to be used in the multivariarte regression.

Value

An object or a list of class 'ggplot2' showing Kaplan-Meier curves and Cox regression forest plot.

Examples

# First load example classified data
data(MSK_classified)
# Perform survival analysis based on the classification of KRAS mutant samples of pancreatic adenocarcinoma
MSK_classified = kaplan_meier_fit(x = MSK_classified, tumor_type = 'PAAD', gene = 'KRAS', survival_time = 'OS_MONTHS', survival_status = 'OS_STATUS')
#> ℹ There are 20289 different genotypes
#> ℹ The most abundant genotypes are:
#> • Mutant TP53 with LOH (562 Samples, Frequency 0.02)
#> • Mutant KRAS without AMP (199 Samples, Frequency 0.01)
#> • Mutant KRAS without AMP,Mutant TP53 with LOH (149 Samples, Frequency 0.01)
#> Call: survfit(formula = "survival::Surv(OS_MONTHS, OS_STATUS) ~ group", 
#>     data = data)
#> 
#>    6 observations deleted due to missingness 
#>                           n events median 0.95LCL 0.95UCL
#> KRAS WT                 109     63   21.5   18.86    30.8
#> Mutant KRAS without AMP 940    565   17.2   15.74    19.4
#> Mutant KRAS with AMP    667    457   11.2    9.69    12.5
# Perform Cox regression
MSK_classified = cox_fit(x = MSK_classified, tumor_type = 'PAAD', gene = 'KRAS', survival_time = 'OS_MONTHS', survival_status = 'OS_STATUS', covariates = c('age', 'sex', 'tmb'), tmb_method = ">10")
#> Call:
#> survival::coxph(formula = formula %>% stats::as.formula(), data = data %>% 
#>     as.data.frame())
#> 
#>                                  coef exp(coef) se(coef)      z        p
#> groupMutant KRAS with AMP     0.41896   1.52039  0.13594  3.082  0.00206
#> groupMutant KRAS without AMP  0.13670   1.14649  0.13460  1.016  0.30980
#> AGE_AT_DEATH>68              -1.32060   0.26698  0.23843 -5.539 3.05e-08
#> AGE_AT_SEQUENCING>67          1.23185   3.42756  0.23771  5.182 2.19e-07
#> SEXMale                       0.13644   1.14619  0.06167  2.213  0.02693
#> TMB_NONSYNONYMOUS> 10         0.56316   1.75621  0.26202  2.149  0.03161
#> 
#> Likelihood ratio test=70.61  on 6 df, p=3.068e-13
#> n= 1066, number of events= 1066 
#>    (656 observations deleted due to missingness)
plot_survival_analysis(x = MSK_classified, tumor_type = 'PAAD', gene = 'KRAS')
#> Joining with `by = join_by(var)`
#> Joining with `by = join_by(var)`
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_point()`).
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_rect()`).
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_point()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_point()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_rect()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_point()`).