Simulation analysis for all lines

Last updated: 2018-08-25

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File	Version	Author	Date	Message
Rmd	d618fe5	davismcc	2018-08-25	Updating analyses
html	090c1b9	davismcc	2018-08-24	Build site.
html	02a8343	davismcc	2018-08-24	Build site.
Rmd	43f15d6	davismcc	2018-08-24	Adding data pre-processing workflow and updating analyses.
html	43f15d6	davismcc	2018-08-24	Adding data pre-processing workflow and updating analyses.

Load libraries and simulation results

dir.create("figures/simulations", showWarnings = FALSE, recursive = TRUE)
library(ggpubr)
library(tidyverse)
library(cardelino)
library(viridis)
library(cowplot)
lines <- c("euts", "fawm", "feec", "fikt", "garx", "gesg", "heja", "hipn", 
            "ieki", "joxm", "kuco", "laey", "lexy", "naju", "nusw", "oaaz", 
            "oilg", "pipw", "puie", "qayj", "qolg", "qonc", "rozh", "sehl", 
            "ualf", "vass", "vuna", "wahn", "wetu", "xugn", "zoxy", "vils")

Load simulation results

all_files <- paste0(lines, ".simulate.rds")
assign_0 <- matrix(0, nrow = 500, ncol = length(lines))
assign_1 <- matrix(0, nrow = 500, ncol = length(lines))
prob_all <- matrix(0, nrow = 500, ncol = length(lines))

for (i in seq_len(length(all_files))) {
  afile <- all_files[i]
  sim_dat <- readRDS(file.path("data", "simulations", afile))
  assign_0[, i] <- get_prob_label(sim_dat$I_sim)
  assign_1[, i] <- get_prob_label(sim_dat$prob_Gibbs)
  prob_all[, i] <- get_prob_value(sim_dat$prob_Gibbs, mode = "best")
}

Load results from real data

all_files <- paste0("cardelino_results.", lines, 
                    ".filt_lenient.cell_coverage_sites.rds")
n_sites <- rep(0, length(lines))
n_clone <- rep(0, length(lines))
recall_all <- rep(0, length(lines))
for (i in seq_len(length(all_files))) {
  afile <- all_files[i]
  carde_dat <- readRDS(file.path("data", "cell_assignment", afile))
  n_sites[i] <- nrow(carde_dat$D)
  n_clone[i] <- ncol(carde_dat$prob_mat)
  recall_all[i] <- mean(get_prob_value(carde_dat$prob_mat, mode = "best") > 0.5)
}

Overall correlation in assignment rates (recall) from simulated and observed data is 0.959.

precision_simu <- rep(0, length(lines))
for (i in seq_len(length(lines))) {
  idx <- prob_all[, i] > 0.5
  precision_simu[i] <- mean(assign_0[idx, i] == assign_1[idx, i])
}

df <- data.frame(line = lines, n_sites = n_sites, n_clone = n_clone, 
                 recall_real = recall_all, recall_simu = colMeans(prob_all > 0.5),
                 precision_simu = precision_simu)

Plot observed vs simulated assignment rates (recall)

df %>%
  dplyr::mutate(sites_per_clone = cut(n_sites / pmax(n_clone - 1, 1), 
                                      breaks = c(0, 3, 8, 15, 25, 60))) %>%
  ggplot(
         aes(x = recall_simu, y = recall_real, 
             fill = sites_per_clone)) +
  geom_abline(slope = 1, intercept = 0, colour = "gray40", linetype = 2) +
  geom_smooth(aes(group = 1), method = "lm", colour = "firebrick") +
  geom_point(size = 3, shape = 21) +
  xlim(0, 1) + ylim(0, 1) +
  scale_fill_manual(name = "mean\n# variants\nper clonal\nbranch", 
                    values = magma(6)[-1]) +
  guides(colour = FALSE, group = FALSE) +
  xlab("Assignment rate: simulated") +
  ylab("Assignment rate: observed")

Expand here to see past versions of unnamed-chunk-4-1.png:

Version	Author	Date
02a8343	davismcc	2018-08-24

ggsave("figures/simulations/assign_rate_obs_v_sim.png", 
       height = 4.5, width = 5)
ggsave("figures/simulations/assign_rate_obs_v_sim.pdf", 
       height = 4.5, width = 5)

Plot simulation precision-recall curve

df %>%
  dplyr::mutate(sites_per_clone = cut(n_sites / n_clone, 
                                      breaks = c(0, 5, 10, 20, 40))) %>%
  ggplot(
         aes(x = recall_simu, y = precision_simu, 
             fill = sites_per_clone)) +
  geom_hline(yintercept = 0.85, colour = "gray40", linetype = 2) +
  geom_smooth(aes(group = 1), method = "lm", colour = "firebrick") +
  geom_point(size = 3, shape = 21) +
  xlim(0, 1) + ylim(0, 1) +
  scale_fill_manual(name = "mean\n# variants\nper clone", 
                    values = magma(5)[-1]) +
  guides(colour = FALSE, group = FALSE) +
  xlab("Assignment rate (recall)") +
  ylab("Precision")

Expand here to see past versions of unnamed-chunk-5-1.png:

Version	Author	Date
02a8343	davismcc	2018-08-24

ggsave("figures/simulations/sim_precision_v_recall.png", 
       height = 4.5, width = 5.5)
ggsave("figures/simulations/sim_precision_v_recall.pdf", 
       height = 4.5, width = 5.5)

Clone statistics

Table showing the number of lines with 2, 3 and 4 clones.

table(df$n_clone)


 2  3  4 
 4 24  4

Summary of the average number of mutations per clonal branch across lines.

summary(df$n_sites / (df$n_clone - 1))

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   3.00    8.50   11.50   18.69   25.00   57.50

Session information

devtools::session_info()

Session info -------------------------------------------------------------

 setting  value                       
 version  R version 3.5.1 (2018-07-02)
 system   x86_64, darwin15.6.0        
 ui       X11                         
 language (EN)                        
 collate  en_GB.UTF-8                 
 tz       Europe/London               
 date     2018-08-25

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