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More gracefully handle missing plot configs

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Bryan Roessler
2024-10-05 17:08:01 -04:00
parent 5a819bfa88
commit 08bf4946e0
1 ha cambiato i file con 44 aggiunte e 57 eliminazioni
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101
qhtcp-workflow/apps/r/calculate_interaction_zscores.R
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@@ -7,6 +7,7 @@ suppressMessages({
library("rlang")
library("ggthemes")
library("data.table")
library("grid")
library("gridExtra")
library("future")
library("furrr")
@@ -506,31 +507,6 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs, page_width
grid_layout <- group$grid_layout
plots <- group$plots
# Only handle grid layout if it exists
if (!is.null(grid_layout)) {
# Set grid_ncol to 1 if not specified
if (is.null(grid_layout$ncol)) {
grid_layout$ncol <- 1
}
# If ncol is set but nrow is not, calculate nrow dynamically based on num_plots
if (!is.null(grid_layout$ncol) && is.null(grid_layout$nrow)) {
num_plots <- length(plots)
nrow <- ceiling(num_plots / grid_layout$ncol)
# message("No nrow provided, automatically using nrow = ", nrow)
grid_layout$nrow <- nrow
}
# Fill missing spots with nullGrob() if necessary
total_spots <- grid_layout$nrow * grid_layout$ncol
num_plots <- length(plots)
if (num_plots < total_spots) {
message("Filling ", total_spots - num_plots, " empty spots with nullGrob()")
plots <- c(plots, replicate(total_spots - num_plots, nullGrob(), simplify = FALSE))
}
}
for (i in seq_along(plots)) {
config <- plots[[i]]
df <- config$df
@@ -546,9 +522,9 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs, page_width
# Print rows being filtered out
if (nrow(out_of_bounds_df) > 0) {
message("Filtered: ", config$title, "using y-limits: [", config$ylim_vals[1], ", ", config$ylim_vals[2], "]")
message("Filtered: ", config$title, " using y-limits: [", config$ylim_vals[1], ", ", config$ylim_vals[2], "]")
message("# of filtered rows outside y-limits (for plotting): ", nrow(out_of_bounds_df))
# print(out_of_bounds_df)
print(out_of_bounds_df)
}
df <- df %>%
@@ -582,15 +558,6 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs, page_width
}
}
# Create a null plot with a "No data" message if no rows remain
# if (nrow(df) == 0) {
# plot <- ggplot() +
# geom_text(aes(0.5, 0.5), label = "No data available", size = 5) +
# theme_void() + ggtitle(config$title)
# } else {
# plot <- ggplot(df, aes_mapping) + theme_publication(legend_position = config$legend_position)
# }
plot <- ggplot(df, aes_mapping) + theme_publication(legend_position = config$legend_position)
# Add appropriate plot layer or helper function based on plot type
@@ -718,18 +685,37 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs, page_width
}
# Print the plots in the current group to the PDF
if (is.null(grid_layout)) {
# Print each plot individually on separate pages if no grid layout is specified
for (plot in static_plots) {
print(plot)
if (!is.null(grid_layout)) {
# Set grid_ncol to 1 if not specified
if (is.null(grid_layout$ncol)) {
grid_layout$ncol <- 1
}
# If ncol is set but nrow is not, calculate nrow dynamically based on num_plots
if (!is.null(grid_layout$ncol) && is.null(grid_layout$nrow)) {
num_plots <- length(static_plots)
nrow <- ceiling(num_plots / grid_layout$ncol)
# message("No nrow provided, automatically using nrow = ", nrow)
grid_layout$nrow <- nrow
}
total_spots <- grid_layout$nrow * grid_layout$ncol
num_plots <- length(static_plots)
if (num_plots < total_spots) {
message("Filling ", total_spots - num_plots, " empty spots with nullGrob()")
static_plots <- c(static_plots, replicate(total_spots - num_plots, nullGrob(), simplify = FALSE))
}
} else {
# Arrange plots in grid layout on a single page
grid.arrange(
grobs = static_plots,
ncol = grid_layout$ncol,
nrow = grid_layout$nrow
)
} else {
# Print individual plots on separate pages if no grid layout
for (plot in static_plots) {
print(plot)
}
}
}
@@ -917,7 +903,7 @@ generate_plate_analysis_plot_configs <- function(variables, df_before = NULL, df
return(list(plots = plot_configs))
}
generate_interaction_plot_configs <- function(df_summary, df_interaction, type) {
generate_interaction_plot_configs <- function(df_summary, df_interactions, type) {
# Define the y-limits for the plots
limits_map <- list(
@@ -982,7 +968,6 @@ generate_interaction_plot_configs <- function(df_summary, df_interaction, type)
}
plot_config$annotations <- annotations
stats_plot_configs <- append(stats_plot_configs, list(plot_config))
} else if (plot_type == "box") {
@@ -995,12 +980,6 @@ generate_interaction_plot_configs <- function(df_summary, df_interaction, type)
}
# Delta interaction plots
if (type == "reference") {
group_vars <- c("OrfRep", "Gene", "num")
} else if (type == "deletion") {
group_vars <- c("OrfRep", "Gene")
}
delta_limits_map <- list(
L = c(-60, 60),
K = c(-60, 60),
@@ -1008,19 +987,26 @@ generate_interaction_plot_configs <- function(df_summary, df_interaction, type)
AUC = c(-6000, 6000)
)
grouped_data <- df_interaction %>%
# Select the data grouping by data type
if (type == "reference") {
group_vars <- c("OrfRep", "Gene", "num")
} else if (type == "deletion") {
group_vars <- c("OrfRep", "Gene")
}
grouped_data <- df_interactions %>%
group_by(across(all_of(group_vars))) %>%
group_split()
for (group_data in grouped_data) {
# Build the plot title
OrfRep <- first(group_data$OrfRep)
Gene <- first(group_data$Gene)
num <- if ("num" %in% names(group_data)) first(group_data$num) else ""
if (type == "reference") {
OrfRepTitle <- paste(OrfRep, Gene, num, sep = "_")
num <- if ("num" %in% names(group_data)) first(group_data$num) else ""
OrfRepTitle <- paste(OrfRep, Gene, num, sep = "_")
} else if (type == "deletion") {
OrfRepTitle <- OrfRep
OrfRepTitle <- OrfRep
}
for (var in names(delta_limits_map)) {
@@ -1049,7 +1035,7 @@ generate_interaction_plot_configs <- function(df_summary, df_interaction, type)
x_label = paste0("[", unique(df_summary$Drug)[1], "]"),
shape = 16,
title = paste(OrfRepTitle, Gene, sep = " "),
title_size = rel(1.3),
title_size = rel(1.4),
coord_cartesian = y_limits,
annotations = list(
list(x = 1, y = y_limits[2] - 0.1 * y_span, label = paste(" ZShift =", round(Z_Shift_value, 2))),
@@ -1089,6 +1075,7 @@ generate_interaction_plot_configs <- function(df_summary, df_interaction, type)
chunk_size <- 12
delta_plot_chunks <- split(delta_plot_configs, ceiling(seq_along(delta_plot_configs) / chunk_size))
# TODO, only return first page of plots for testing, remove this later
return(c(
list(list(grid_layout = list(ncol = 2), plots = stats_plot_configs)),
list(list(grid_layout = list(ncol = 2), plots = stats_boxplot_configs)),
@@ -1575,7 +1562,7 @@ main <- function() {
df_deletion <- df_na_stats %>% # formerly X2
filter(OrfRep != strain) %>%
filter(!is.na(L)) %>%
group_by(OrfRep, Gene, conc_num) %>%
group_by(OrfRep, Gene, conc_num, conc_num_factor_factor) %>%
mutate(
max_l_theoretical = max(max_L, na.rm = TRUE),
L = ifelse(L == 0 & !is.na(L) & conc_num > 0, max_l_theoretical, L),