# 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")
Datasets
Models
