R/plot_cohort_CNA.R

In caravagn/CNAqc: CNAqc - Copy Number Analysis quality check

# #
# # y = readRDS("~/Downloads/dataqc_A4164_1004_PDO.rds")
# # z = readRDS("~/Downloads/dataqc_A3770_1018_PDO.rds")
# # w = readRDS("~/Downloads/dataqc_A3994_117_PDO.rds")
# #
# # x = list(y, z, w)
# #
# # plot_multisample_CNA(x)
# #
# x = readRDS("~/Downloads/all_drivers (1).rds")
#
# # Colours adopted
# CNA_colors = CNAqc:::get_karyotypes_colors(x$karyotype)
# CNA_colors = c(CNA_colors, `Other` = 'yellow')
# CNA_colors = CNA_colors[names(CNA_colors) %in% c("1:0", "1:1", "2:0", "2:1", "2:2", "Other")]
#
#
# mutation_colors = ggsci::pal_lancet()(2)
# names(mutation_colors) = c("SNV", 'indel')
#
# colors = c(CNA_colors, mutation_colors)
#
# # Driver annotations
# driver_cna = x %>%
#   dplyr::mutate(type = ifelse(karyotype %in% names(CNA_colors), karyotype, "Other")) %>%
#   dplyr::select(sample, driver_label, type)
#
# driver_muts = x %>%
#   dplyr::select(sample, driver_label, type)
#
# wide_format = bind_rows(driver_cna, driver_muts) %>%
#   distinct() %>%
#   group_by(sample, driver_label) %>%
#   mutate(type = paste(type, collapse = ';')) %>%
#   distinct() %>%
#   pivot_wider(names_from = 'driver_label', values_from = "type") %>%
#   ungroup() %>%
#   replace(is.na(.), "") %>%
#   t() %>%
#   data.frame()
#
# colnames(wide_format) = wide_format[1,]
# wide_format = wide_format[-1,]
#
# # CNA colors
# fun_CNA_colors = lapply(CNA_colors,
#                         function(event_color) {
#                           function(x, y, w, h) {
#                             grid::grid.rect(x,
#                                             y,
#                                             w - unit(0.5, "mm"),
#                                             h - unit(0.5, "mm"),
#                                             gp = grid::gpar(fill = event_color, col = NA))
#                           }
#                         })
#
# # Background colors
# fun_background_color = list(
#   `background` = function(x, y, w, h) {
#     grid::grid.rect(x,
#                     y,
#                     w - unit(0.5, "mm"),
#                     h - unit(0.5, "mm"),
#                     gp = grid::gpar(fill = "gainsboro", col = NA))
#   }
# )
#
# # Mutation colour
# fun_mutation_colors = lapply(mutation_colors,
#                              function(event_color) {
#                                function(x, y, w, h) {
#                                  grid::grid.rect(x,
#                                                  y,
#                                                  w - unit(0.5, "mm"),
#                                                  h * 0.33,
#                                                  gp = grid::gpar(fill = event_color, col = NA))
#                                }
#                              })
#
# # Alternate list
# alter_fun_list = append(fun_CNA_colors, fun_background_color) %>% append(fun_mutation_colors)
#
# # Labels
# legend_labels = names(alter_fun_list)
# legend_labels = legend_labels[legend_labels != "background"]
#
#
# pdf(paste0("~/Downloads/Driver_statistcs.pdf"),
#     width = 7,
#     height = 13)
# ComplexHeatmap::oncoPrint(
#   wide_format,
#   get_type = function(x)
#     strsplit(x, ";")[[1]],
#   alter_fun = alter_fun_list,
#   alter_fun_is_vectorized = FALSE,
#   col = colors,
#   column_title = "OncoPrint",
#   show_column_names = TRUE,
#   heatmap_legend_param = list(title = "Alternations",
#                               at = legend_labels,
#                               labels = legend_labels)
# )
# dev.off()