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figures.R
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figures.R
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here::i_am("figures.R")
out_dir <- here::here("figures")
# Setup ------------------------------------------------------------------------
library(DDCompanion)
library(iCOBRA)
library(SingleCellExperiment)
library(scuttle)
library(tidyverse)
library(ggthemes)
library(patchwork)
# Setup-------------------------------------------------------------------------
theme_set(theme_light(base_size = 14))
method_cols <- c(
"edgeR_NB" = "#F28E2B",
"edgeR_QP" = "red",
"edgeR_NB_optim" = "gold1",
"edgeR_QP_optim" = "hotpink3",
"bGLM" = "grey32",
"qbGLM" = "bisque3",
"qbGLM_offset" = "dodgerblue",
"qbGLM_offset_squeeze" = "dodgerblue4"
)
main_methods <- c("bGLM", "qbGLM", "qbGLM_offset", "qbGLM_offset_squeeze",
"edgeR_NB", "edgeR_QP", "edgeR_NB_optim", "edgeR_QP_optim")
# Obtain helper functions for generating the figures----------------------------
source("./figures/figures_helpers.R")
message("Loaded helper functions to generate figures")
# Read and wrangle the data objects needed for the figures----------------------
message("Started reading and wrangling data for figures")
message("This will take a couple of minutes")
source("./figures/figures_prepare.R")
message("Finished reading and wrangling data for figures")
# Figure 1: Lupus T4_naive_10 mock results ------------------------------------
fig_ncM_sub <- plot_mock(data = lupus_mock_table_ncM$ncM_10,
multisample = FALSE,
stratifier_row = "method")
fig_ncM_sub <- fig_ncM_sub +
theme(strip.text = element_text(hjust = 0, size = 11, face = "bold", color = "black"))
ggsave(file.path(out_dir, "fig1_lupus_ncM_mock.png"), fig_ncM_sub, width = 12, height=12*0.618)
# Figure S1-3: Lupus all cell types all sample size pseudobulk mock results-----
## We generate 3 plots, one for each cell type. Each plot plot has 8*4 panels,
## for 8 methods and 4 sample sizes.
fig_ncM <- plot_mock(data = as.data.frame(do.call(rbind, lupus_mock_table_ncM)),
multisample = TRUE,
stratifier_col = "samplesize",
stratifier_row = "method")
fig_T4naive <- plot_mock(data = as.data.frame(do.call(rbind, lupus_mock_table_T4naive)),
multisample = TRUE,
stratifier_col = "samplesize",
stratifier_row = "method")
fig_Bmem <- plot_mock(data = as.data.frame(do.call(rbind, lupus_mock_table_Bmem)),
multisample = TRUE,
stratifier_col = "samplesize",
stratifier_row = "method")
ggsave(file.path(out_dir, "figS1_lupus_ncM_mock.png"), fig_ncM, width = 12, height=28*0.618)
ggsave(file.path(out_dir, "figS2_lupus_T4naive_mock.png"), fig_T4naive, width = 12, height=28*0.618)
ggsave(file.path(out_dir, "figS3_lupus_Bmem_mock.png"), fig_Bmem, width = 12, height=28*0.618)
# Figure S4: Lupus all cell types, 22v22 single-cell data ----------------------
fig_sce_mock <- plot_mock(data = as.data.frame(do.call(rbind, lupus_mock_table_sce)),
multisample = TRUE, stratifier_col = "celltype", stratifier_row = "method")
ggsave(file.path(out_dir, "figS4_lupus_mock_sce.png"), fig_sce_mock, width = 12, height=28*0.618)
# Figure S5-7: Covid all celltype mock results------------------------------------
fig_covid_class_switched <- plot_mock(data = as.data.frame(do.call(rbind,
list(covid_mock_tables$class_switched_memory_B_cell_Healthy,
covid_mock_tables$class_switched_memory_B_cell_Mild,
covid_mock_tables$class_switched_memory_B_cell_Moderate,
covid_mock_tables$class_switched_memory_B_cell_Critical))),
multisample = TRUE,
stratifier_col = "disease",
stratifier_row = "method")
ggsave(file.path(out_dir, "figS5_covid_class_switched_mock.png"), fig_covid_class_switched, width = 12, height=28*0.618)
fig_covid_immature <- plot_mock(data = as.data.frame(do.call(rbind,
list(covid_mock_tables$immature_B_cell_Healthy,
covid_mock_tables$immature_B_cell_Mild,
covid_mock_tables$immature_B_cell_Moderate,
covid_mock_tables$immature_B_cell_Critical))),
multisample = TRUE,
stratifier_col = "disease",
stratifier_row = "method")
ggsave(file.path(out_dir, "figS6_covid_immature_mock.png"), fig_covid_immature, width = 12, height=28*0.618)
fig_covid_naive <- plot_mock(data = as.data.frame(do.call(rbind,
list(covid_mock_tables$naive_B_cell_Healthy,
covid_mock_tables$naive_B_cell_Mild,
covid_mock_tables$naive_B_cell_Moderate,
covid_mock_tables$naive_B_cell_Critical))),
multisample = TRUE,
stratifier_col = "disease",
stratifier_row = "method")
ggsave(file.path(out_dir, "figS7_covid_naive_mock.png"), fig_covid_naive, width = 12, height=28*0.618)
# Figure S9: Lupus sim all ct results ------------------------------------------
lupus_sim_cobra <- map2(lupus_sim_res, lupus_pb_objects,
~ map2(.x, .y, prepare_COBRAData))
## Supplementary figure
lupus_sim_perf <- map_depth(lupus_sim_cobra, 2,
calculate_performance, binary_truth = "status")
lupus_sim_plot_objects <- map_depth(lupus_sim_perf, 2,
prepare_data_for_plot, colorscheme = c(method_cols, truth = "grey35"))
lupus_sim_plot_data <- map(lupus_sim_plot_objects, combine_fdrtpr_tables) |>
bind_rows(.id = "celltype")
lupus_sim_plot_data$replicate <- str_to_title(sub("_", " ", lupus_sim_plot_data$replicate))
lupus_sim_plot_data <- lupus_sim_plot_data[-which(lupus_sim_plot_data$method == "bGLM"),]
## plot
fig_lupus_sim_all <- lupus_sim_plot_data |>
group_by(thr, method, celltype) |>
summarize(across(c(FDR, TPR), mean), .groups = "keep") |>
mutate(samplesize = paste0("n = ", str_sub(celltype, start = -2, end = -1))) |>
mutate(celltype = c("B memory",
"Non-classical \n myeloid",
"T4 naive")[match(str_sub(celltype, start = 1, end = -4),
c("B_mem","ncM","T4_naive"))]) |>
mutate(celltype = factor(celltype, levels = c("Non-classical \n myeloid",
"T4 naive",
"B memory"))) |>
plot_fdrtpr_points() +
base_theme(legend.position = "bottom") +
facet_grid(rows = vars(samplesize),
cols = vars(celltype),
scales="fixed",
labeller = labeller(samplesize = label_func_grid_row,
celltype = label_func_grid_col)) +
theme(strip.text = element_text(size=16))
## save
save_plot(file.path(out_dir, "figS9_lupus_sim_all.png"), fig_lupus_sim_all, width = 12, asp = 0.618*2)
# Figure 2: Lupus sim all ct 5v5 results ---------------------------------------
lupus_sim_plot_data <- map(lupus_sim_plot_objects, combine_fdrtpr_tables) |>
bind_rows(.id = "celltype")
lupus_sim_plot_data$replicate <- str_to_title(sub("_", " ", lupus_sim_plot_data$replicate))
lupus_sim_plot_data <- lupus_sim_plot_data[lupus_sim_plot_data$celltype %in% c("ncM_10", "T4_naive_10", "B_mem_10"),]
## plot
fig_lupus_sim_5v5 <- lupus_sim_plot_data |>
group_by(thr, method, celltype) |>
summarize(across(c(FDR, TPR), mean), .groups = "keep") |>
plot_fdrtpr_points() +
base_theme(legend.position = "bottom") +
facet_wrap(vars(factor(celltype,
levels=c("ncM_10", "T4_naive_10", "B_mem_10"),
labels = c("Panel A: \n Non-classical myeloid",
"Panel B: \n T4 naive",
"Panel C: \n B memory"))),
nrow = 1,
scales="fixed") +
theme(strip.text = element_text(size=16))
## save
save_plot(file.path(out_dir, "fig2_lupus_sim_5v5.png"), fig_lupus_sim_5v5, width = 12, asp = 0.618/1.33)
# Figure S8: Lupus simulation results all ct alternative -----------------------
use_thr <- 0.05
lupus_sim_plot_data_s8 <- lupus_sim_plot_data |>
filter(thr == use_thr)
lupus_sim_plot_data_s8$method <- factor(lupus_sim_plot_data_s8$method,
levels = main_methods)
lupus_sim_plot_data_s8$celltype <- factor(lupus_sim_plot_data_s8$celltype,
levels=c("ncM_10", "T4_naive_10", "B_mem_10"),
labels = c("Non-classical \n myeloid",
"T4 naive",
"B memory"))
fig_lupus_sim_all_alt <- plot_fdr_control(lupus_sim_plot_data_s8, shape_by = "celltype") +
scale_shape_manual(name = "Cell Type", values = 21:23)
save_plot(file.path(out_dir, "figS8_lupus_sim_all_alt.png"), fig_lupus_sim_all_alt, width = 12, asp = 0.618)
# Figure S10: Covid all celltypes sim results------------------------------------
covid_all_sim_res <- map(covid_all_sim_res,
~ rename_methods(.x) |>
map(get_tables, depth = 1) |>
## Bring replicates to top level
transpose() |>
map(combine_tables)
)
covid_cobra_data <- map2(covid_all_sim_res, covid_all_pb_objects,
~ map2(.x, .y, prepare_COBRAData)
)
covid_cobra_perf <- map_depth(covid_cobra_data, 2,
calculate_performance, binary_truth = "status"
)
covid_cobra_objects <- map_depth(covid_cobra_perf, 2,
prepare_data_for_plot, colorscheme = c(method_cols, truth = "grey35")
)
sim_covid_data <- map(covid_cobra_objects, combine_fdrtpr_tables) |>
bind_rows(.id = "celltype")
sim_covid_data$replicate <- str_to_title(sub("_", " ", sim_covid_data$replicate))
fig_covid_sim_all <- sim_covid_data |>
group_by(thr, method, celltype) |>
summarize(across(c(FDR, TPR), mean), .groups = "keep") |>
mutate(disease = factor(gsub("(.*_\\s*(.*$))", "\\2", celltype),
levels = c("Healthy", "Mild", "Moderate", "Critical"))) |>
mutate(celltype = factor(sub("_[^_]+$", "", celltype),
levels = c("class_switched_memory_B_cell",
"immature_B_cell",
"naive_B_cell"),
labels = c("Class switched memory B",
"Immature B",
"Naive B"))) |>
plot_fdrtpr_points() +
base_theme(legend.position = "bottom") +
facet_grid(rows = vars(celltype),
cols = vars(disease),
scales="fixed",
labeller = labeller(celltype = label_func_grid_row,
disease = label_func_grid_col)) +
theme(strip.text = element_text(size=25))
ann_text <- data.frame(FDR = 0.7,
TPR = 0.05,
method = "bGLM",
disease = factor(c("Healthy","Mild","Moderate", "Critical",
"Healthy","Mild","Moderate", "Critical",
"Healthy","Mild","Moderate", "Critical"),
levels = c("Healthy","Mild",
"Moderate", "Critical")),
celltype = factor(c("Class switched memory B", "Class switched memory B", "Class switched memory B", "Class switched memory B",
"Immature B", "Immature B", "Immature B", "Immature B",
"Naive B","Naive B","Naive B","Naive B"),
levels = c("Class switched memory B",
"Immature B",
"Naive B")))
fig_covid_sim_all <- fig_covid_sim_all + geom_text(data = ann_text,
label = c("n = 7v7", "n = 9v8", "n = 12v10", "n = 4v5",
"n = 7v9", "n = 7v9", "n = 8v9", "n = 4v5",
"n = 11v12", "n = 11v9", "n = 13v12", "n = 6v6"),
size = 8,
show.legend = FALSE)
save_plot(file.path(out_dir, "figS10_covid_sim_all.png"), fig_covid_sim_all, width = 22, asp = 0.618*1.33)