# upset_plots.R

# install.packages("UpSetR")

# install.packages("ggupset")

require(UpSetR)

# require(ggupset)

require(data.table)

require(stringr)

require(ggplot2)

require(patchwork)

line_info <- fread("Data/DRP_Training_Data/DepMap_21Q2_Line_Info.csv")

ctrp <- fread("Data/DRP_Training_Data/CTRP_AAC_SMILES.txt")

# gdsc1 <- fread("Data/DRP_Training_Data/GDSC1_AAC_SMILES.txt")

gdsc2 <- fread("Data/DRP_Training_Data/GDSC2_AAC_SMILES.txt")

exp <- fread("Data/DRP_Training_Data/DepMap_21Q2_Expression.csv")

mut <- fread("Data/DRP_Training_Data/DepMap_21Q2_Mutations_by_Cell.csv")

cnv <- fread("Data/DRP_Training_Data/DepMap_21Q2_CopyNumber.csv")

prot <- fread("Data/DRP_Training_Data/DepMap_20Q2_No_NA_ProteinQuant.csv")

mirna <- fread("Data/DRP_Training_Data/DepMap_2019_miRNA.csv")

hist <- fread("Data/DRP_Training_Data/DepMap_2019_ChromatinProfiling.csv")

metab <- fread("Data/DRP_Training_Data/DepMap_2019_Metabolomics.csv")

rppa <- fread("Data/DRP_Training_Data/DepMap_2019_RPPA.csv")

dim(rppa)

dim(metab)

dim(hist)

dim(mirna)

dim(prot)

dim(exp)

dim(cnv)

dim(mut)

uniqueN(gdsc2$ccl_name)

uniqueN(ctrp$ccl_name)

# ctrp$ccl_name = str_replace(toupper(ctrp$ccl_name), "-", "")

# 

# exp_ccl_names = exp$stripped_cell_line_name

# exp_ccl_names = str_replace(toupper(exp_ccl_names), "-", "")

# 

# mut_ccl_names = mut$stripped_cell_line_name

# mut_ccl_names = str_replace(toupper(mut_ccl_names), "-", "")

# 

# cnv_ccl_names = cnv$stripped_cell_line_name

# cnv_ccl_names = str_replace(toupper(cnv_ccl_names), "-", "")

mut$stripped_cell_line_name = str_replace(toupper(mut$stripped_cell_line_name), "-", "")

cnv$stripped_cell_line_name = str_replace(toupper(cnv$stripped_cell_line_name), "-", "")

exp$stripped_cell_line_name = str_replace(toupper(exp$stripped_cell_line_name), "-", "")

prot$stripped_cell_line_name = str_replace(toupper(prot$stripped_cell_line_name), "-", "")

mirna$stripped_cell_line_name = str_replace(toupper(mirna$stripped_cell_line_name), "-", "")

hist$stripped_cell_line_name = str_replace(toupper(hist$stripped_cell_line_name), "-", "")

metab$stripped_cell_line_name = str_replace(toupper(metab$stripped_cell_line_name), "-", "")

rppa$stripped_cell_line_name = str_replace(toupper(rppa$stripped_cell_line_name), "-", "")

ctrp$ccl_name = str_replace(toupper(ctrp$ccl_name), "-", "")

gdsc2$ccl_name = str_replace(toupper(gdsc2$ccl_name), "-", "")

mut_line_info <- line_info[stripped_cell_line_name %in% unique(mut$stripped_cell_line_name)]  

cnv_line_info <- line_info[stripped_cell_line_name %in% unique(cnv$stripped_cell_line_name)]  

exp_line_info <- line_info[stripped_cell_line_name %in% unique(exp$stripped_cell_line_name)]  

prot_line_info <- line_info[stripped_cell_line_name %in% unique(prot$stripped_cell_line_name)]

mirna_line_info <- line_info[stripped_cell_line_name %in% unique(mirna$stripped_cell_line_name)]  

hist_line_info <- line_info[stripped_cell_line_name %in% unique(hist$stripped_cell_line_name)]  

metab_line_info <- line_info[stripped_cell_line_name %in% unique(metab$stripped_cell_line_name)]  

rppa_line_info <- line_info[stripped_cell_line_name %in% unique(rppa$stripped_cell_line_name)]

ctrp_line_info <- line_info[stripped_cell_line_name %in% unique(ctrp$ccl_name)]

gdsc2_line_info <- line_info[stripped_cell_line_name %in% unique(gdsc2$ccl_name)]

mut_line_info <- mut_line_info[, c("stripped_cell_line_name", "primary_disease")]

mut_line_info$data_type <- "Mutational"

cnv_line_info <- cnv_line_info[, c("stripped_cell_line_name", "primary_disease")]

cnv_line_info$data_type <- "Copy Number Variation"

exp_line_info <- exp_line_info[, c("stripped_cell_line_name", "primary_disease")]

exp_line_info$data_type <- "Gene Expression"

prot_line_info <- prot_line_info[, c("stripped_cell_line_name", "primary_disease")]

prot_line_info$data_type <- "Protein Quantification"

mirna_line_info <- mirna_line_info[, c("stripped_cell_line_name", "primary_disease")]

mirna_line_info$data_type <- "microRNA Expression"

hist_line_info <- hist_line_info[, c("stripped_cell_line_name", "primary_disease")]

hist_line_info$data_type <- "Histone Modification"

metab_line_info <- metab_line_info[, c("stripped_cell_line_name", "primary_disease")]

metab_line_info$data_type <- "Metabolomics"

rppa_line_info <- rppa_line_info[, c("stripped_cell_line_name", "primary_disease")]

rppa_line_info$data_type <- "Reverse-Phase Protein Array"

ctrp_line_info <- ctrp_line_info[, c("stripped_cell_line_name", "primary_disease")]

ctrp_line_info$data_type <- "Dose-Response"

gdsc2_line_info <- gdsc2_line_info[, c("stripped_cell_line_name", "primary_disease")]

gdsc2_line_info$data_type <- "Dose-Response"

list_input <- list(

  Mutational = mut_line_info$stripped_cell_line_name,

  `Copy Number Variation` = cnv_line_info$stripped_cell_line_name,

  `Gene Expression` = exp_line_info$stripped_cell_line_name,

  `Protein Quantification` = prot_line_info$stripped_cell_line_name,

  `microRNA Expression` = mirna_line_info$stripped_cell_line_name,

  `Histone Modification` = hist_line_info$stripped_cell_line_name,

  `Metabolomics` = metab_line_info$stripped_cell_line_name,

  `Reverse-Phase Protein Array` = rppa_line_info$stripped_cell_line_name,

  `CTRPv2 Dose-Response` = unique(ctrp$ccl_name),

  `GDSC2 Dose-Response` = unique(gdsc2$ccl_name)

)

make_all_combinations <- function(set){

  unlist(lapply(seq_along(set), function(size){

    apply(combn(set, size), 2, paste0, collapse="-")

  }))

}

?upset

p <- upset(fromList(list_input),

      sets = c("CTRPv2 Dose-Response", "GDSC2 Dose-Response",

               "Mutational","Copy Number Variation", "Gene Expression", "Protein Quantification",

               "microRNA Expression", "Histone Modification", "Metabolomics", "Reverse-Phase Protein Array"

               ),

      keep.order = T,

      # sets = c("Dose-Response"),

      mainbar.y.label = "Data Intersection Size",

      # mainbar.y.max = 30,

      sets.x.label = "Cell Lines per Data Type",

      # group.by = "sets",

      order.by = "freq",

      scale.sets = "identity",

      # set_size.angles = 45,

      text.scale = c(1.3, 1.3, 1, 1, 1, 0.75))

p

pdf(file="Plots/Dataset_Exploration/UpSetR_Overlap_Plot_CTRPv2.pdf",

    width = 10, height = 5

    )

p

dev.off()

# ggsave(filename = "Plots/Dataset_Exploration/UpSetR_Overlap_Plot_CTRPv2.pdf")

# all_cells <- rbindlist(list(

#   mut_line_info,

#   cnv_line_info,

#   exp_line_info,

#   prot_line_info,

#   mirna_line_info,

#   hist_line_info,

#   metab_line_info,

#   rppa_line_info)

# )

# all_cells <- all_cells[, -2]

# 

# # install.packages("tidyverse")

# require(tidyverse)

# all_cells <- tidyr::as_tibble(all_cells[, 1:2])

# simple_groups_df <- all_cells %>%

#   group_by(stripped_cell_line_name) %>%

#   summarize(groups = list(data_type))

# 

# extended_groups_df <- simple_groups_df %>%

#   mutate(groups = lapply(groups, make_all_combinations)) %>%

#   unnest()

# 

# unique(extended_groups_df)

# ggplot(extended_groups_df, aes(x=groups)) +

#   geom_bar() +

#   axis_combmatrix(sep = "-", )

# 

# all_cells[, extended_groups := lapply(data_type, make_all_combinations)]

# lapply(all_cells, make_all_combinations)

# ==== Bimodal Intersections Counts ====

require(ggplot2)

require(data.table)

require(flextable)

require(magrittr)

require(scales)

require(officer)

ctrp <- fread("Data/DRP_Training_Data/CTRP_AAC_SMILES.txt")

mut_line_info$data_type <- "MUT"

cnv_line_info$data_type <- "CNV"

exp_line_info$data_type <- "EXP"

prot_line_info$data_type <- "PROT"

mirna_line_info$data_type <- "MIRNA"

hist_line_info$data_type <- "HIST"

metab_line_info$data_type <- "METAB"

rppa_line_info$data_type <- "RPPA"

ctrp_line_info$data_type <- "CTRP"

all_cells <- rbindlist(list(mut_line_info, cnv_line_info, exp_line_info, prot_line_info,

                            mirna_line_info, metab_line_info, hist_line_info, rppa_line_info))

all_cells <- unique(all_cells)

ctrp_cells <- unique(ctrp_line_info$stripped_cell_line_name)

all_omics <- data.table(

  `Data Type(s)` = c("Mutational","Copy Number", "Gene Expression", "Protein Quantification",

                     "microRNA Expression", "Metabolomics", "Histone Modification", "RPPA"),

  `Abbreviation` = c("MUT", "CNV", "EXP", "PROT", "MIRNA", "METAB", "HIST", "RPPA"),

                        `Number of Samples` = vector(mode = "integer", length = 8))

for (i in 1:nrow(all_omics)) {

  first_cells <- all_cells[data_type == all_omics[i, 2]]$stripped_cell_line_name

  # second_cells <- all_cells[data_type == all_omics[i, 2]]$stripped_cell_line_name

  # cell_overlap <- Reduce(intersect, list(first_cells, second_cells, ctrp_cells))

  ctrp_overlap <- uniqueN(ctrp[ccl_name %in% first_cells])

  all_omics[i, 3] <- ctrp_overlap

}

set_flextable_defaults(

  font.size = 10, theme_fun = theme_vanilla,

  padding = 6,

  background.color = "#EFEFEF")

colourer <- col_numeric(

  palette = c("red", "white"),

  domain = c(min(all_omics$`Number of Samples`), max(all_omics$`Number of Samples`)))

# ==== bimodal 

ft <- flextable(all_omics)

final_ft <- ft %>%

  merge_v(j = c("Data Type(s)", "Number of Samples")) %>%

  border_inner(border = fp_border(color="gray", width = 1)) %>%

  border_outer(part="all", border = fp_border(color="gray", width = 2)) %>%

  align(align = "center", j = c(2, 3), part = "header") %>%

  bg(

    bg = colourer,

    j = "Number of Samples", 

    part = "body")

final_ft <- autofit(final_ft)

read_docx() %>% 

  body_add_flextable(value = final_ft) %>% 

  print(target = "Plots/Dataset_Exploration/bimodal_samples_per_data_type_combo.docx")

# ==== trimodal

all_tri_omic_combos_el <- utils::combn(c("MUT", 'CNV', 'EXP', 'PROT', 'MIRNA', 'METAB', 'HIST', 'RPPA'), 2, simplify = T)

all_tri_omic_combos_el <- t(all_tri_omic_combos_el)

all_tri_omic_combos_el <- as.data.table(all_tri_omic_combos_el)

# all_sample_counts <- vector(mode = "numeric", length = nrow(temp))

ctrp_cells <- unique(ctrp_line_info$stripped_cell_line_name)

all_tri_omic_combos_el$sample_counts <- vector(mode = "integer")

for (i in 1:nrow(all_tri_omic_combos_el)) {

  first_cells <- all_cells[data_type == all_tri_omic_combos_el[i, 1]]$stripped_cell_line_name

  second_cells <- all_cells[data_type == all_tri_omic_combos_el[i, 2]]$stripped_cell_line_name

  cell_overlap <- Reduce(intersect, list(first_cells, second_cells, ctrp_cells))

  ctrp_overlap <- uniqueN(ctrp[ccl_name %in% cell_overlap])

  all_tri_omic_combos_el[i, 3] <- ctrp_overlap

}

colnames(all_tri_omic_combos_el) <- c("Data Type 1", "Data Type 2", "Number of Samples")

colourer <- col_numeric(

  palette = c("red", "white"),

  domain = c(min(all_tri_omic_combos_el$`Number of Samples`), max(all_tri_omic_combos_el$`Number of Samples`)))

ft <- flextable(all_tri_omic_combos_el)

final_ft <- ft %>%

  merge_v(j = c("Data Type 1", "Data Type 2", "Number of Samples")) %>%

  border_inner(border = fp_border(color="gray", width = 1)) %>%

  border_outer(part="all", border = fp_border(color="gray", width = 2)) %>%

  align(align = "center", j = 1:3, part = "all") %>%

  bg(

    bg = colourer,

    j = "Number of Samples", 

    part = "body")

final_ft <- autofit(final_ft)

read_docx() %>% 

  body_add_flextable(value = final_ft) %>% 

  print(target = "Plots/Dataset_Exploration/trimodal_samples_per_data_type_combo.docx")