ALL IMPORTED FILES MUST BE IN THE SAME DIRECTORY AS THIS SCRIPT
High-performance computing cluster (e.g. using RStudio Server) strongly recommended, due to large memory requirements
Load required packages
library("tidyverse")
library("readbulk")
library("glmmTMB")
library("betareg")
library("DHARMa")
Import data; each file comprises 1000 SIGNAL bootstrap solutions (generated using https://signal.mutationalsignatures.com/analyse2) using the OvCa-specific signature set (OVARY_A/B/C/D/E/F/G- see https://signal.mutationalsignatures.com/explore/studyTissueType/1-15) for the 12 samples comprising the pooled reference group (‘HGSrefMutSig’), imported twice as separate data sets 1 and 2; refer to Chapters 4.2.2.3-4 for further details
mut_sig_SIGNAL_exposures_HGSrefMutSig1 <- read.delim("masterfile_tumour_samples_SIGNAL_HGSrefMutSig.tsv") %>%
mutate(Group = "HGSrefMutSig1")
mut_sig_SIGNAL_exposures_HGSrefMutSig2 <- read.delim("masterfile_tumour_samples_SIGNAL_HGSrefMutSig.tsv") %>%
mutate(Group = "HGSrefMutSig2")
Import number of somatic mutations used for signature fitting for the 12 HGSrefMutSig samples
HGSrefMutSig_samples <- read.delim("HGSrefMutSig_samples.tsv")
Perform random sampling of two samples with combined total of >= 60 mutations from the HGSrefMutSig group 100 times, comparing these each time against the complete HGSrefMutSig group using the GLMM, and output results with exact p-values (NB may need to be run multiple times to generate n = 100 results)
for (i in c(1:100)) {
HGSrefMutSigTest <- sample_n(HGSrefMutSig_samples, 2, replace=FALSE)
if(sum(HGSrefMutSigTest$No_Mutations)>=60){
mut_sig_SIGNAL_exposures_HGSrefMutSig_subset <- filter(mut_sig_SIGNAL_exposures_HGSrefMutSig2, Tumour_Sample%in%HGSrefMutSigTest$Tumour_Sample)
mut_sig_SIGNAL_exposures_HGSrefMutSig1_2 <- bind_rows(mut_sig_SIGNAL_exposures_HGSrefMutSig1,mut_sig_SIGNAL_exposures_HGSrefMutSig_subset)
mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Exposures <- replace(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Exposures, mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Exposures==1, 0.9999999)
mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Group <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Group)
mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Solu_Sample <- paste(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Solution,
mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Tumour_Sample,
sep=":") %>%
as.factor()
mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Tumour_Sample <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Tumour_Sample)
mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Solution <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Solution)
mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Signature <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Signature)
mut_sig_mixed_lmer_bin_HGSrefMutSig <- glmmTMB(Exposures ~ Age + Group +
(1|Solu_Sample) + (1|Signature),
data = mut_sig_SIGNAL_exposures_HGSrefMutSig1_2,
family=beta_family(),
ziformula = ~1,
control = glmmTMBControl(parallel = 48)
)
sink(file=(paste(`i`,"testSamples_vs_HGSrefMutSig_SIGNAL_lmer_bin_2&60_results.txt",sep="")),append = FALSE)
print(HGSrefMutSigTest)
summary(mut_sig_mixed_lmer_bin_HGSrefMutSig) %>% print()
sink()
sink(file="testSamples_vs_HGSrefMutSig_SIGNAL_lmer_bin_2&60_all_p_values.txt",append = TRUE)
print(i)
print(HGSrefMutSigTest)
print(coef(summary(mut_sig_mixed_lmer_bin_HGSrefMutSig))$cond)
sink()
}else{
rm(HGSrefMutSigTest)
}
closeAllConnections()
}
Perform random sampling of three samples with combined total of >= 150 mutations from the HGSrefMutSig group 100 times, comparing these each time against the complete HGSrefMutSig group using the GLMM, and output results with exact p-values (NB may need to be run multiple times to generate n = 100 results)
for (i in c(1:100)) {
HGSrefMutSigTest <- sample_n(HGSrefMutSig_samples, 3, replace=FALSE)
if(sum(HGSrefMutSigTest$No_Mutations)>=150){
mut_sig_SIGNAL_exposures_HGSrefMutSig_subset <- filter(mut_sig_SIGNAL_exposures_HGSrefMutSig2, Tumour_Sample%in%HGSrefMutSigTest$Tumour_Sample)
mut_sig_SIGNAL_exposures_HGSrefMutSig1_2 <- bind_rows(mut_sig_SIGNAL_exposures_HGSrefMutSig1,mut_sig_SIGNAL_exposures_HGSrefMutSig_subset)
mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Exposures <- replace(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Exposures, mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Exposures==1, 0.9999999)
mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Group <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Group)
mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Solu_Sample <- paste(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Solution,
mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Tumour_Sample,
sep=":") %>%
as.factor()
mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Tumour_Sample <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Tumour_Sample)
mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Solution <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Solution)
mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Signature <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Signature)
mut_sig_mixed_lmer_bin_HGSrefMutSig <- glmmTMB(Exposures ~ Age + Group +
(1|Solu_Sample) + (1|Signature),
data = mut_sig_SIGNAL_exposures_HGSrefMutSig1_2,
family=beta_family(),
ziformula = ~1,
control = glmmTMBControl(parallel = 48)
)
sink(file=(paste(`i`,"testSamples_vs_HGSrefMutSig_SIGNAL_lmer_bin_3&150_results.txt",sep="")),append = FALSE)
print(HGSrefMutSigTest)
summary(mut_sig_mixed_lmer_bin_HGSrefMutSig) %>% print()
sink()
sink(file="testSamples_vs_HGSrefMutSig_SIGNAL_lmer_bin_3&150_all_p_values.txt",append = TRUE)
print(i)
print(HGSrefMutSigTest)
print(coef(summary(mut_sig_mixed_lmer_bin_HGSrefMutSig))$cond)
sink()
}else{
rm(HGSrefMutSigTest)
}
closeAllConnections()
}
Compare HGSrefMutSig individual samples against the complete HGSrefMutSig group using the GLMM, perform DHARMa residual diagnostics tests for each comparison, and output results
HGSrefMutSig_samples_list <- HGSrefMutSig_samples$Tumour_Sample
for (HGSrefMutSig in HGSrefMutSig_samples_list){
mut_sig_SIGNAL_exposures_sample <- filter(mut_sig_SIGNAL_exposures_HGSrefMutSig2, Tumour_Sample%in%HGSrefMutSig) %>%
mutate(Group = HGSrefMutSig)
mut_sig_SIGNAL_exposures_HGSrefMutSig_sample <- bind_rows(mut_sig_SIGNAL_exposures_HGSrefMutSig1,mut_sig_SIGNAL_exposures_sample)
mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Exposures <- replace(mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Exposures, mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Exposures==1, 0.9999999)
mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Group <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Group)
mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Solu_Sample <- paste(mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Solution,
mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Tumour_Sample,
sep=":") %>%
as.factor()
mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Tumour_Sample <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Tumour_Sample)
mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Solution <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Solution)
mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Signature <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Signature)
mut_sig_mixed_lmer_bin_HGSrefMutSig_sample <- glmmTMB(Exposures ~ Age + Group +
(1|Solu_Sample) + (1|Signature),
data = mut_sig_SIGNAL_exposures_HGSrefMutSig1_2,
family=beta_family(),
ziformula = ~1,
control = glmmTMBControl(parallel = 48)
)
simres_mut_sig_mixed_lmer_bin_HGSrefMutSig_sample <- simulateResiduals(mut_sig_mixed_lmer_bin_HGSrefMutSig_sample)
sink(file=(paste(`HGSrefMutSig`,"_vs_HGSrefMutSig_SIGNAL_lmer_bin_simRes_results.txt",sep="")),append = FALSE)
pdf(paste(`HGSrefMutSig`,"_vs_HGSrefMutSig_SIGNAL_lmer_bin_simRes_results.pdf",sep=""))
print(HGSrefMutSigTest)
summary(mut_sig_mixed_lmer_bin_HGSrefMutSig_sample) %>% print()
plot(simres_mut_sig_mixed_lmer_bin_HGSrefMutSig_sample)
testDispersion(simres_mut_sig_mixed_lmer_bin_HGSrefMutSig_sample) %>% print()
testUniformity(simres_mut_sig_mixed_lmer_bin_HGSrefMutSig_sample) %>% print()
testOutliers(simres_mut_sig_mixed_lmer_bin_HGSrefMutSig_sample,type="bootstrap") %>% print()
testZeroInflation(simres_mut_sig_mixed_lmer_bin_HGSrefMutSig_sample) %>% print()
plotResiduals(simres_mut_sig_mixed_lmer_bin_HGSrefMutSig_sample, mut_sig_mixed_lmer_bin_HGSrefMutSig_sample$Group)
sink()
sink(file="testSamples_vs_HGSrefMutSig_SIGNAL_lmer_bin_all_p_values.txt",append = TRUE)
print(HGSrefMutSig)
print(HGSrefMutSigTest)
print(coef(summary(mut_sig_mixed_lmer_bin_HGSrefMutSig_sample))$cond)
sink()
}
closeAllConnections()
---
title: "Thesis SIGNAL Mutational Signature GLMM Testing Script"
output: html_notebook
---

ALL IMPORTED FILES MUST BE IN THE SAME DIRECTORY AS THIS SCRIPT

High-performance computing cluster (e.g. using RStudio Server) strongly recommended, due to large memory requirements 

Load required packages
```{r}
library("tidyverse")
library("readbulk")
library("glmmTMB")
library("betareg")
library("DHARMa")
```

Import data; each file comprises 1000 SIGNAL bootstrap solutions (generated using https://signal.mutationalsignatures.com/analyse2) using the OvCa-specific signature set (OVARY_A/B/C/D/E/F/G- see https://signal.mutationalsignatures.com/explore/studyTissueType/1-15) for the 12 samples comprising the pooled reference group ('HGSrefMutSig'), imported twice as separate data sets 1 and 2; refer to Chapters 4.2.2.3-4 for further details
```{r}
mut_sig_SIGNAL_exposures_HGSrefMutSig1 <- read.delim("masterfile_tumour_samples_SIGNAL_HGSrefMutSig.tsv") %>% 
  mutate(Group = "HGSrefMutSig1")
mut_sig_SIGNAL_exposures_HGSrefMutSig2 <- read.delim("masterfile_tumour_samples_SIGNAL_HGSrefMutSig.tsv") %>% 
  mutate(Group = "HGSrefMutSig2")
```

Import number of somatic mutations used for signature fitting for the 12 HGSrefMutSig samples
```{r}
HGSrefMutSig_samples <- read.delim("HGSrefMutSig_samples.tsv")
```

Perform random sampling of two samples with combined total of >= 60 mutations from the HGSrefMutSig group 100 times, comparing these each time against the complete HGSrefMutSig group using the GLMM, and output results with exact p-values (NB may need to be run multiple times to generate n = 100 results)
```{r}
for (i in c(1:100)) {
  HGSrefMutSigTest <- sample_n(HGSrefMutSig_samples, 2, replace=FALSE)
  if(sum(HGSrefMutSigTest$No_Mutations)>=60){
    mut_sig_SIGNAL_exposures_HGSrefMutSig_subset <- filter(mut_sig_SIGNAL_exposures_HGSrefMutSig2, Tumour_Sample%in%HGSrefMutSigTest$Tumour_Sample)
    mut_sig_SIGNAL_exposures_HGSrefMutSig1_2 <- bind_rows(mut_sig_SIGNAL_exposures_HGSrefMutSig1,mut_sig_SIGNAL_exposures_HGSrefMutSig_subset)
    mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Exposures <- replace(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Exposures, mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Exposures==1, 0.9999999)
    mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Group <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Group)
    mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Solu_Sample <- paste(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Solution,
                                                                      mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Tumour_Sample,
                                                                      sep=":") %>% 
      as.factor()
    mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Tumour_Sample <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Tumour_Sample)
    mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Solution <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Solution)
    mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Signature <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Signature)
    
    mut_sig_mixed_lmer_bin_HGSrefMutSig <- glmmTMB(Exposures ~ Age + Group +
                                                          (1|Solu_Sample) + (1|Signature),
                                                          data = mut_sig_SIGNAL_exposures_HGSrefMutSig1_2, 
                                                          family=beta_family(), 
                                                          ziformula = ~1,
                                                          control = glmmTMBControl(parallel = 48) #parallel = no. of available processor cores/threads
    )
    sink(file=(paste(`i`,"testSamples_vs_HGSrefMutSig_SIGNAL_lmer_bin_2&60_results.txt",sep="")),append = FALSE)
    print(HGSrefMutSigTest)
    summary(mut_sig_mixed_lmer_bin_HGSrefMutSig) %>% print()
    sink()
    sink(file="testSamples_vs_HGSrefMutSig_SIGNAL_lmer_bin_2&60_all_p_values.txt",append = TRUE)
    print(i)
    print(HGSrefMutSigTest)
    print(coef(summary(mut_sig_mixed_lmer_bin_HGSrefMutSig))$cond)
    sink()
  }else{
    rm(HGSrefMutSigTest)
  }
  closeAllConnections()
}
```

Perform random sampling of three samples with combined total of >= 150 mutations from the HGSrefMutSig group 100 times, comparing these each time against the complete HGSrefMutSig group using the GLMM, and output results with exact p-values (NB may need to be run multiple times to generate n = 100 results)
```{r}
for (i in c(1:100)) {
  HGSrefMutSigTest <- sample_n(HGSrefMutSig_samples, 3, replace=FALSE)
  if(sum(HGSrefMutSigTest$No_Mutations)>=150){
    mut_sig_SIGNAL_exposures_HGSrefMutSig_subset <- filter(mut_sig_SIGNAL_exposures_HGSrefMutSig2, Tumour_Sample%in%HGSrefMutSigTest$Tumour_Sample)
    mut_sig_SIGNAL_exposures_HGSrefMutSig1_2 <- bind_rows(mut_sig_SIGNAL_exposures_HGSrefMutSig1,mut_sig_SIGNAL_exposures_HGSrefMutSig_subset)
    
    mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Exposures <- replace(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Exposures, mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Exposures==1, 0.9999999)
    mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Group <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Group)
    mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Solu_Sample <- paste(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Solution,
                                                                      mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Tumour_Sample,
                                                                      sep=":") %>% 
      as.factor()
    mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Tumour_Sample <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Tumour_Sample)
    mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Solution <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Solution)
    mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Signature <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig1_2$Signature)
    mut_sig_mixed_lmer_bin_HGSrefMutSig <- glmmTMB(Exposures ~ Age + Group +
                                                          (1|Solu_Sample) + (1|Signature),
                                                          data = mut_sig_SIGNAL_exposures_HGSrefMutSig1_2, 
                                                          family=beta_family(), 
                                                          ziformula = ~1,
                                                          control = glmmTMBControl(parallel = 48)
    )
    sink(file=(paste(`i`,"testSamples_vs_HGSrefMutSig_SIGNAL_lmer_bin_3&150_results.txt",sep="")),append = FALSE)
    print(HGSrefMutSigTest)
    summary(mut_sig_mixed_lmer_bin_HGSrefMutSig) %>% print()
    sink()
    sink(file="testSamples_vs_HGSrefMutSig_SIGNAL_lmer_bin_3&150_all_p_values.txt",append = TRUE)
    print(i)
    print(HGSrefMutSigTest)
    print(coef(summary(mut_sig_mixed_lmer_bin_HGSrefMutSig))$cond)
    sink()
  }else{
    rm(HGSrefMutSigTest)
  }
  closeAllConnections()
}
```

Compare HGSrefMutSig individual samples against the complete HGSrefMutSig group using the GLMM, perform DHARMa residual diagnostics tests for each comparison, and output results
```{r}
HGSrefMutSig_samples_list <- HGSrefMutSig_samples$Tumour_Sample
for (HGSrefMutSig in HGSrefMutSig_samples_list){
    mut_sig_SIGNAL_exposures_sample <- filter(mut_sig_SIGNAL_exposures_HGSrefMutSig2, Tumour_Sample%in%HGSrefMutSig) %>% 
      mutate(Group = HGSrefMutSig)
    mut_sig_SIGNAL_exposures_HGSrefMutSig_sample <- bind_rows(mut_sig_SIGNAL_exposures_HGSrefMutSig1,mut_sig_SIGNAL_exposures_sample)
    
    mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Exposures <- replace(mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Exposures, mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Exposures==1, 0.9999999)
    mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Group <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Group)
    mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Solu_Sample <- paste(mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Solution,
                                                                      mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Tumour_Sample,
                                                                      sep=":") %>% 
      as.factor()
    mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Tumour_Sample <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Tumour_Sample)
    mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Solution <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Solution)
    mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Signature <- as.factor(mut_sig_SIGNAL_exposures_HGSrefMutSig_sample$Signature)
    
    mut_sig_mixed_lmer_bin_HGSrefMutSig_sample <- glmmTMB(Exposures ~ Age + Group +
                                                          (1|Solu_Sample) + (1|Signature),
                                                          data = mut_sig_SIGNAL_exposures_HGSrefMutSig1_2, 
                                                          family=beta_family(), 
                                                          ziformula = ~1,
                                                          control = glmmTMBControl(parallel = 48)
    )
    simres_mut_sig_mixed_lmer_bin_HGSrefMutSig_sample <- simulateResiduals(mut_sig_mixed_lmer_bin_HGSrefMutSig_sample)
    sink(file=(paste(`HGSrefMutSig`,"_vs_HGSrefMutSig_SIGNAL_lmer_bin_simRes_results.txt",sep="")),append = FALSE)
    pdf(paste(`HGSrefMutSig`,"_vs_HGSrefMutSig_SIGNAL_lmer_bin_simRes_results.pdf",sep=""))
    print(HGSrefMutSigTest)
    summary(mut_sig_mixed_lmer_bin_HGSrefMutSig_sample) %>% print()
    plot(simres_mut_sig_mixed_lmer_bin_HGSrefMutSig_sample)
    testDispersion(simres_mut_sig_mixed_lmer_bin_HGSrefMutSig_sample) %>% print()
    testUniformity(simres_mut_sig_mixed_lmer_bin_HGSrefMutSig_sample) %>% print()
    testOutliers(simres_mut_sig_mixed_lmer_bin_HGSrefMutSig_sample,type="bootstrap") %>% print()
    testZeroInflation(simres_mut_sig_mixed_lmer_bin_HGSrefMutSig_sample) %>% print()
    plotResiduals(simres_mut_sig_mixed_lmer_bin_HGSrefMutSig_sample, mut_sig_mixed_lmer_bin_HGSrefMutSig_sample$Group)
    sink()
    sink(file="testSamples_vs_HGSrefMutSig_SIGNAL_lmer_bin_all_p_values.txt",append = TRUE)
    print(HGSrefMutSig)
    print(HGSrefMutSigTest)
    print(coef(summary(mut_sig_mixed_lmer_bin_HGSrefMutSig_sample))$cond)
    sink()
}
closeAllConnections()
```
