diff --git a/experiments/assessing_cluster_clonality/sandbox/WBC_analysis.R b/experiments/assessing_cluster_clonality/sandbox/WBC_analysis.R
new file mode 100644
index 0000000..b97f017
--- /dev/null
+++ b/experiments/assessing_cluster_clonality/sandbox/WBC_analysis.R
@@ -0,0 +1,76 @@
+library(tidyverse)
+data <- read_tsv("~/Downloads/splittingSummary_full_final.tsv")
+data <- data %>% mutate(WBC = n_wbcs > 0)
+
+data <- data %>% mutate(impact_mutations = high_impact_mutations + medium_impact_mutations)
+
+
+View(data)
+filtered_data <- data %>%
+  filter(str_detect(`Sample Name`, "Br|Pr|LM2"))
+
+
+fit <- glm(as.factor(Oligoclonal) ~ n_cells + `Sample Name` + WBC, data = data, family = binomial(link = "logit"))
+summary(fit)
+#### Not signficant
+
+
+fit2 <- glm(as.factor(Oligoclonal) ~ n_cells + `Sample Name` + WBC, data = filtered_data, family = binomial(link = "logit"))
+summary(fit2)
+#### Not signficant
+
+
+fit3 <- glm(n_wbcs ~ n_cells + `Sample Name` + Oligoclonal, data = filtered_data, family = poisson(link = "log"))
+summary(fit3)
+
+fit4 <- glm(n_wbcs ~ n_cells + `Sample Name` + Oligoclonal, data = filtered_data, family = poisson(link = "log"))
+summary(fit4)
+### Not significant
+
+
+fit6 <- glm(high_impact_mutations ~ n_cells + `Sample Name` + Oligoclonal + WBC, data = filtered_data, family = poisson(link = "log"))
+summary(fit6)
+### Not significant
+
+
+fit7 <- glm(as.factor(Oligoclonal) ~ n_cells + `Sample Name` + impact_mutations + Oligoclonal + WBC, data = filtered_data, family = binomial(link = "logit"))
+summary(fit7)
+
+
+fit5 <- glm(impact_mutations ~ n_cells + `Sample Name` + Oligoclonal + WBC, data = filtered_data, family = poisson(link = "log"))
+summary(fit5)
+### significant effect of WBC presence on impact mutations
+
+filtered_data %>%
+  ggplot(aes(x = WBC, y = impact_mutations, group = WBC)) +
+  geom_boxplot() +
+  theme(
+    axis.text.x = element_text(angle = 45, hjust = 1, size = 18),
+    axis.title.x = element_text(size = 20),
+    axis.title.y = element_text(size = 20),
+    axis.text.y = element_text(size = 18)
+  ) +
+  ylab("# Mutations with functional impact")
+
+
+
+fit8 <- glm(impact_mutations ~ n_cells + `Sample Name` + Oligoclonal + n_wbcs, data = filtered_data, family = poisson(link = "log"))
+summary(fit8)
+
+
+filtered_data %>%
+  ggplot(aes(x = n_wbcs, y = impact_mutations, group = n_wbcs)) +
+  geom_boxplot() +
+  theme(
+    axis.text.x = element_text(angle = 45, hjust = 1, size = 18),
+    axis.title.x = element_text(size = 20),
+    axis.title.y = element_text(size = 20),
+    axis.text.y = element_text(size = 18)
+  ) +
+  ylab("# Mutations with functional impact")
+
+
+
+fit9 <- glm(n_cells ~ `Sample Name` + Oligoclonal, data = filtered_data, family = poisson(link = "log"))
+summary(fit9)
+### Not significant
diff --git a/experiments/assessing_cluster_clonality/sandbox/assessment_of_oligoclonals.R b/experiments/assessing_cluster_clonality/sandbox/assessment_of_oligoclonals.R
new file mode 100644
index 0000000..6fbc7b1
--- /dev/null
+++ b/experiments/assessing_cluster_clonality/sandbox/assessment_of_oligoclonals.R
@@ -0,0 +1,142 @@
+input_folder <- "/Users/jgawron/Documents/projects/CTC_backup/input_folder"
+
+simulation_input_folder <- "/Users/jgawron/Documents/projects/CTC_backup/simulations/simulations2"
+tree_name <- "Br16_AC"
+n_sampling_events <- 100
+
+source("functions.R")
+
+
+tree_names <- c("Br11", "Br16_AC", "Br16_B", "Br16_C", "Br23", "Br26", "Br30", "Br37", "Br38", "Br39", "Br44", "Br45", "Br46", "Br53", "Br57", "Br61", "Br7", "Brx50", "LM2", "Lu2", "Lu7", "Ov8", "Pr6", "Pr9")
+
+
+mean_splitting_scores_mono <- vector()
+for (tree_name in tree_names) {
+  # input <- load_data(input_folder, tree_name)
+
+  # description_data <-
+  #  read_delim(
+  #    file.path(
+  #      input$directory,
+  ###      input$sampleName,
+  #      paste0(input$sampleName, "_samples_nodeDescription.tsv")
+  #    ),
+  #    delim = "\t",
+  ###    col_names = FALSE,
+  #    quote = "none"
+  #  )
+  # colnames(description_data) <-
+  #  c("sample_name", "total_number_cells", "tumor_cells", "WBCs", "description")
+
+
+  folders <- list.dirs(path = simulation_input_folder, recursive = FALSE)
+  matching_folders <- folders[grepl(tree_name, basename(folders))]
+
+  for (idx1 in 1:length(matching_folders)) {
+    simulation_instance <- basename(matching_folders[idx1])
+    input_simulated <- load_data(simulation_input_folder, simulation_instance)
+    sample_description_simulated <- input_simulated$sample_description
+    for (color in c(
+      "orchid", "orchid1", "orchid2",
+      "orchid3", "orchid4", "darkorchid",
+      "darkorchid1", "darkorchid2", "darkorchid3",
+      "darkorchid4", "purple", "purple1",
+      "purple2", "purple3", "purple4"
+    )) {
+      distance_simulated <-
+        computeClusterSplits(
+          input_simulated$sample_description, input_simulated$postSampling,
+          simulation_instance, input_simulated$nCells, input_simulated$nMutations,
+          input_simulated$nClusters, input_simulated$alleleCount,
+          input_simulated$mutatedReadCounts, input_simulated$totalReadCounts,
+          nMutationSamplingEvents = n_sampling_events,
+          nTreeSamplingEvents = n_sampling_events,
+          cellPairSelection = c(color)
+        )
+      plot(
+        ggplot(
+          data.frame(x = distance_simulated$aggregatedBranchingProbabilities), aes(x = x)
+        ) +
+          geom_histogram(binwidth = 0.01)
+      )
+
+      mean_splitting_scores_mono <- c(mean_splitting_scores_mono, mean(distance_simulated$aggregatedBranchingProbabilities))
+    }
+  }
+}
+
+
+
+involved_cell_indices <- sub(paste0(".*", tree_name, "_"), "", simulation_instance) %>%
+  strsplit("_") %>%
+  unlist()
+involved_cell_indices <- as.numeric(involved_cell_indices)
+
+involved_single_tumor_cells <- description_data$sample_name[involved_cell_indices]
+
+pairs <- combn(involved_single_tumor_cells, 2, simplify = FALSE)
+
+distance_separate <-
+  computeClusterSplits(input$sample_description, input$postSampling,
+    treeName, input$nCells, input$nMutations,
+    input$nClusters, input$alleleCount,
+    input$mutatedReadCounts, input$totalReadCounts,
+    nMutationSamplingEvents = n_sampling_events,
+    nTreeSamplingEvents = n_sampling_events,
+    cellPairSelection = pairs
+  )
+
+plot(
+  ggplot(
+    data.frame(x = distance_separate$aggregatedBranchingProbabilities), aes(x = x)
+  ) +
+    geom_histogram(binwidth = 0.01)
+)
+mean(distance_separate$aggregatedBranchingProbabilities)
+
+
+
+
+#########
+
+mean_splitting_scores_mono <- mean_splitting_scores_mono[!is.na(mean_splitting_scores_mono)]
+
+data.frame(splitting_probs = mean_splitting_scores_mono, Monoclonal = TRUE) %>%
+  ggplot(aes(y = splitting_probs)) +
+  geom_boxplot() +
+  theme_minimal()
+
+load("~/Documents/projects/CTC_backup/simulations/simulation3/mean_branching_probs_oligo.RData")
+load("~/Documents/projects/CTC_backup/simulations/simulation3/mean_branching_probs_mono.RData")
+load("~/Documents/projects/CTC_backup/simulations/simulation3/deviance_splitting_score.RData")
+
+splitting_probs <- data.frame(splitting_probs = mean_splitting_scores_mono, Oligoclonal = "Monoclonal")
+splitting_probs2 <- data.frame(splitting_probs = mean_branching_probs_oligo, Oligoclonal = "Oligoclonal")
+splitting_probs_single_cells <- data.frame(splitting_probs = -deviance_splitting_score + mean_branching_probs_oligo, Oligoclonal = "Genetically distinct single cells")
+
+splitting_probs <- rbind(splitting_probs, splitting_probs2, splitting_probs_single_cells)
+splitting_probs <- splitting_probs %>% mutate(Oligoclonal = factor(Oligoclonal, levels = c("Monoclonal", "Oligoclonal", "Genetically distinct single cells")))
+
+splitting_probs %>%
+  ggplot(aes(y = splitting_probs, x = Oligoclonal, group = Oligoclonal)) +
+  geom_boxplot() +
+  ylab("Mean splitting probability") +
+  xlab("Clonality status of CTC cluster") +
+  theme_minimal()
+theme(
+  axis.text.x = element_text(angle = 45, hjust = 1, size = 18),
+  axis.title.x = element_text(size = 20),
+  axis.title.y = element_text(size = 20),
+  axis.text.y = element_text(size = 18)
+)
+
+data.frame(y = deviance_splitting_score) %>%
+  ggplot(aes(y = y)) +
+  geom_boxplot() +
+  theme_minimal()
+
+
+data.frame(y = -deviance_splitting_score + mean_branching_probs_oligo) %>%
+  ggplot(aes(y = y)) +
+  geom_boxplot() +
+  theme_minimal()