Last updated: 2022-04-26

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Knit directory: diamantopoulou-ctc-dynamics/

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Load libraries, additional functions and variables

Setup environment

knitr::opts_chunk$set(results='asis', echo=TRUE, message=FALSE, warning=FALSE, error=FALSE, fig.align = 'center', fig.width = 3.5, fig.asp = 0.618, dpi = 600, dev = c("png", "pdf"), fig.showtext = TRUE)

options(stringsAsFactors = FALSE)

Load packages

library(tidyverse)
library(showtext)
library(grid)
library(gridExtra)

Set font family for figures

font_add("Helvetica", "./configuration/fonts/Helvetica.ttc")
showtext_auto()

Load ggplot theme

source("./configuration/rmarkdown/ggplot_theme.R")

Load color palettes

source("./configuration/rmarkdown/color_palettes.R")

Data wrangling

use_data_raw <- read_tsv(file = 'data/patients/ctc_frequency_patients.tsv.txt') %>% 
  mutate(
   n_active = `Single CTCs active phase` + `CTC Clusters active phase` +`CTC-WBC clusters active phase`,
   n_rest = `Single CTCs rest phase` + `CTC Clusters rest phase` + `CTC-WBC clusters rest phase`,
   p_active = n_active / (n_active + n_rest)
  ) %>% 
  pivot_longer(cols = contains("CTC"), names_to = 'timepoint_sample_type_legend', values_to = 'n') %>% 
  mutate(
    donor = paste('Patient', Patient),
    Status = ifelse(`AJCC Stage 8th Edition` == 'IV', 'Late', 'Early'),
    timepoint_sample_type_legend = recode(
      timepoint_sample_type_legend,
      `CTC Clusters active phase` = 'Active phase CTC Clusters',
      `CTC Clusters rest phase` = 'Rest phase CTC Clusters',
      `CTC-WBC clusters active phase` = 'Active phase CTC-WBC Clusters',
      `CTC-WBC clusters rest phase` = 'Rest phase CTC-WBC Clusters',
      `Single CTCs active phase` = 'Active phase Single CTCs',
      `Single CTCs rest phase` = 'Rest phase Single CTCs',
    ),
    timepoint_sample_type_legend = factor(
      timepoint_sample_type_legend,
      levels = c('Active phase Single CTCs', 'Active phase CTC Clusters', 'Active phase CTC-WBC Clusters',
                    'Rest phase Single CTCs', 'Rest phase CTC Clusters', 'Rest phase CTC-WBC Clusters')
    )
  ) %>% 
  filter(n > 0) %>% 
  select(Status, donor, timepoint_sample_type_legend, n, p_active) %>% 
  unique

use_data_all <- use_data_raw %>% 
  group_by(donor) %>% 
  summarise(t = sum(n)) %>% 
  right_join(use_data_raw) %>% 
  mutate(p = n/t) %>% 
  mutate(
    donor = fct_reorder(donor, t),
    id = gsub(".* ", "", donor) %>% as.numeric)

Setup polar plot early status

use_data <- use_data_all %>% filter(Status == 'Early')
# Get the name and the y position of each label
label_data <- use_data %>% dplyr::select(donor, id, t) %>% unique
number_of_bar <- nrow(label_data)
angle <- 90 - 360 * (label_data$id-0.5) / (number_of_bar +0.5)   # I substract 0.5 because the letter must have the angle of the center of the bars. Not extreme right(1) or extreme left (0)
label_data$hjust <- ifelse( angle < -90, 1, 0)
label_data$angle <- ifelse(angle < -90, angle+180, angle)


# prepare a data frame for grid (scales)
grid_data <- data.frame(start = 1, end = (use_data$donor %>% unique %>% length))

# Add two additional donor levels to have additional space for grid labels
use_data_to_plot <- rbind(
    c('empty1', rep(NA, ncol(use_data)-1)), 
    c('empty2',rep(NA, ncol(use_data)-1)), 
    use_data
  ) %>% 
  mutate(p = as.numeric(p))

# Generate plot
ctc_dist_plot_early <- use_data_to_plot %>% 
  # mutate(donor = fct_reorder(donor, t)) %>% 
  ggplot(aes(x = donor, y = p, fill = timepoint_sample_type_legend, label = donor)) +
  geom_bar(stat="identity", position = "fill", width=0.8, size = 0.2) +
  # Add a val=100/75/50/25 lines. I do it at the beginning to make sur barplots are OVER it.
  geom_segment(data=grid_data, aes(x = end, y = 0, xend = start-0.5, yend = 0), colour = "grey50", alpha=1, size=0.2, inherit.aes = FALSE ) +
  geom_segment(data=grid_data, aes(x = end, y = 0.25, xend = start-0.5, yend = 0.25), colour = "grey50", alpha=1, size=0.2 , inherit.aes = FALSE ) +
  geom_segment(data=grid_data, aes(x = end, y = 0.50, xend = start-0.5, yend = 0.50), colour = "grey50", alpha=1, size=0.2 , inherit.aes = FALSE ) +
  geom_segment(data=grid_data, aes(x = end, y = 0.75, xend = start-0.5, yend = 0.75), colour = "grey50", alpha=1, size=0.2 , inherit.aes = FALSE ) +
  geom_segment(data=grid_data, aes(x = end, y = 1, xend = start-0.5, yend = 1), colour = "grey50", alpha=1, size=0.2 , inherit.aes = FALSE ) +
  # Add text showing the value of each 100/75/50/25 lines
  annotate("text", x = rep(grid_data$end + 3 ,5), y = c(0, 0.25, 0.50, 0.75, 1), label = c('0%', '25%', '50%', '75%', '100%') , color="black", size=geom_text_size , angle=0, fontface="plain", hjust=1) +
  geom_bar(stat="identity", position = "fill", width=0.8, color = 'white', size = 0.2) +
  scale_fill_manual(values = timepoint_sample_type_legend_palette_2) +
  # scale_y_continuous(breaks = seq(0 , 1, 0.1), minor_breaks = seq(0 , 1, 0.1)) +
  ylim(-0.5, 1.5) +
  labs (fill = '') +
  theme(
    axis.text = element_blank(),
    axis.title = element_blank(),
    axis.ticks = element_blank(),
    axis.line = element_blank(),
    panel.grid = element_blank(),
    plot.margin = unit(rep(-1,4), "cm")
  ) +
  coord_polar(start = 0) #+
  # geom_text(data=label_data, aes(x=id, y=1+0.1, label=donor, hjust=hjust), color="black", fontface="plain",alpha=0.6, size=geom_text_size, angle= label_data$angle, inherit.aes = FALSE )

Setup polar plot early status

use_data <- use_data_all %>% filter(Status == 'Late')
# Get the name and the y position of each label
label_data <- use_data %>% dplyr::select(donor, id, t) %>% unique
number_of_bar <- nrow(label_data)
angle <- 90 - 360 * (label_data$id-5-0.5) / (number_of_bar +0.5)   # I substract 0.5 because the letter must have the angle of the center of the bars. Not extreme right(1) or extreme left (0)
label_data$hjust <- ifelse( angle < -90, 1, 0)
label_data$angle <- ifelse(angle < -90, angle+180, angle)

# prepare a data frame for grid (scales)
grid_data <- data.frame(start = 1, end = (use_data$donor %>% unique %>% length))

# Add two additional donor levels to have additional space for grid labels
use_data_to_plot <- rbind(
  c('empty1', rep(NA, ncol(use_data)-1)), 
  use_data) %>% 
  mutate(p = as.numeric(p))


ctc_dist_plot_late <- use_data_to_plot %>% 
  # mutate(donor = fct_reorder(donor, t)) %>% 
  ggplot(aes(x = donor, y = p, fill = timepoint_sample_type_legend, label = donor)) +
  geom_bar(stat="identity", position = "fill", width=0.8, size = 0.2) +
  # Add a val=100/75/50/25 lines. I do it at the beginning to make sur barplots are OVER it.
  geom_segment(data=grid_data, aes(x = end, y = 0, xend = start-0.5, yend = 0), colour = "grey50", alpha=1, size=0.2, inherit.aes = FALSE ) +
  geom_segment(data=grid_data, aes(x = end, y = 0.25, xend = start-0.5, yend = 0.25), colour = "grey50", alpha=1, size=0.2 , inherit.aes = FALSE ) +
  geom_segment(data=grid_data, aes(x = end, y = 0.50, xend = start-0.5, yend = 0.50), colour = "grey50", alpha=1, size=0.2 , inherit.aes = FALSE ) +
  geom_segment(data=grid_data, aes(x = end, y = 0.75, xend = start-0.5, yend = 0.75), colour = "grey50", alpha=1, size=0.2 , inherit.aes = FALSE ) +
  geom_segment(data=grid_data, aes(x = end, y = 1, xend = start-0.5, yend = 1), colour = "grey50", alpha=1, size=0.2 , inherit.aes = FALSE ) +
  # Add text showing the value of each 100/75/50/25 lines
  annotate("text", x = rep(grid_data$end + 2 ,5), y = c(0, 0.25, 0.50, 0.75, 1), label = c('0%', '25%', '50%', '75%', '100%') , color="black", size=geom_text_size , angle=0, fontface="plain", hjust=1) +
  geom_bar(stat="identity", position = "fill", width=0.8, color = 'white', size = 0.2) +
  scale_fill_manual(values = timepoint_sample_type_legend_palette_2) +
  # scale_y_continuous(breaks = seq(0 , 1, 0.1), minor_breaks = seq(0 , 1, 0.1)) +
  ylim(-0.5, 1.5) +
  labs (fill = '') +
  theme(
    axis.text = element_blank(),
    axis.title = element_blank(),
    axis.ticks = element_blank(),
    axis.line = element_blank(),
    panel.grid = element_blank(),
    plot.margin = unit(rep(-1,4), "cm")
  ) +
  coord_polar(start = 0) #+
  # geom_text(data=label_data, aes(x=id-5, y=1+0.1, label=donor, hjust=hjust), color="black", fontface="plain",alpha=0.6, size=geom_text_size, angle= label_data$angle, inherit.aes = FALSE )

Plot polar distribution

The radial histograms show the percent of single CTCs, CTC clusters and CTC-WBC clusters isolated during the rest or active phase in early- or late-stage breast cancer patients. n=21 early-stage and n=9 late-stage patients.

plot_grid(
  ctc_dist_plot_early + theme( legend.position = "none"),
  ctc_dist_plot_late + theme( legend.position = "none"),
  labels = c("Early", "Late"),
  label_size = 8,
  label_x = 0.35
)

Version Author Date
1006c84 fcg-bio 2022-04-26

Plot legend

legend <- cowplot::get_legend(ctc_dist_plot_late)
grid.newpage()
grid.draw(legend)

Version Author Date
1006c84 fcg-bio 2022-04-26 

sessionInfo()

R version 4.1.0 (2021-05-18) Platform: x86_64-apple-darwin17.0 (64-bit) Running under: macOS Big Sur 10.16

Matrix products: default BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib

locale: [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages: [1] grid stats graphics grDevices utils datasets methods
[8] base

other attached packages: [1] cowplot_1.1.1 gridExtra_2.3 showtext_0.9-4 showtextdb_3.0 [5] sysfonts_0.8.5 forcats_0.5.1 stringr_1.4.0 dplyr_1.0.7
[9] purrr_0.3.4 readr_2.0.2 tidyr_1.1.4 tibble_3.1.5
[13] ggplot2_3.3.5 tidyverse_1.3.1 workflowr_1.6.2

loaded via a namespace (and not attached): [1] httr_1.4.2 sass_0.4.0 bit64_4.0.5 vroom_1.5.5
[5] jsonlite_1.7.2 modelr_0.1.8 bslib_0.3.1 assertthat_0.2.1 [9] highr_0.9 cellranger_1.1.0 yaml_2.2.1 pillar_1.6.4
[13] backports_1.3.0 glue_1.4.2 digest_0.6.28 promises_1.2.0.1 [17] rvest_1.0.2 colorspace_2.0-2 htmltools_0.5.2 httpuv_1.6.3
[21] pkgconfig_2.0.3 broom_0.7.10 haven_2.4.3 scales_1.1.1
[25] whisker_0.4 later_1.3.0 tzdb_0.2.0 git2r_0.28.0
[29] generics_0.1.1 farver_2.1.0 ellipsis_0.3.2 withr_2.4.2
[33] cli_3.1.0 magrittr_2.0.1 crayon_1.4.2 readxl_1.3.1
[37] evaluate_0.14 fs_1.5.0 fansi_0.5.0 xml2_1.3.2
[41] tools_4.1.0 hms_1.1.1 lifecycle_1.0.1 munsell_0.5.0
[45] reprex_2.0.1 compiler_4.1.0 jquerylib_0.1.4 rlang_0.4.12
[49] rstudioapi_0.13 labeling_0.4.2 rmarkdown_2.11 gtable_0.3.0
[53] DBI_1.1.1 R6_2.5.1 lubridate_1.8.0 knitr_1.36
[57] fastmap_1.1.0 bit_4.0.4 utf8_1.2.2 rprojroot_2.0.2 [61] stringi_1.7.5 parallel_4.1.0 Rcpp_1.0.7 vctrs_0.3.8
[65] dbplyr_2.1.1 tidyselect_1.1.1 xfun_0.27