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@@ -509,30 +509,23 @@ calculate_interaction_scores <- function(df, df_bg, type, overlap_threshold = 2)
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generate_and_save_plots <- function(out_dir, filename, plot_configs, page_width = 12, page_height = 8) {
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message("Generating ", filename, ".pdf and ", filename, ".html")
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- # Check if we're dealing with multiple plot groups
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plot_groups <- if ("plots" %in% names(plot_configs)) {
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list(plot_configs) # Single group
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} else {
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plot_configs # Multiple groups
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}
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- # Open the PDF device once for all plots
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pdf(file.path(out_dir, paste0(filename, ".pdf")), width = page_width, height = page_height)
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- # Loop through each plot group
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for (group in plot_groups) {
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static_plots <- list()
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plotly_plots <- list()
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- # Retrieve grid layout if it exists, otherwise skip
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- grid_layout <- group$grid_layout
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- plots <- group$plots
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-
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- for (i in seq_along(plots)) {
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- config <- plots[[i]]
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+ for (i in seq_along(group$plots)) {
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+ config <- group$plots[[i]]
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df <- config$df
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- # Filter points outside of y-limits if specified
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+ # Filter and debug out-of-bounds data
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if (!is.null(config$ylim_vals)) {
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out_of_bounds <- df %>%
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filter(
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@@ -540,14 +533,11 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs, page_width
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.data[[config$y_var]] < config$ylim_vals[1] |
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.data[[config$y_var]] > config$ylim_vals[2]
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)
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-
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- # Print rows being filtered out
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if (nrow(out_of_bounds) > 0) {
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message("Filtered ", nrow(out_of_bounds), " row(s) from '", config$title, "' because ", config$y_var,
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- " is outside of y-limits: [", config$ylim_vals[1], ", ", config$ylim_vals[2], "]:")
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- print(out_of_bounds %>% select(OrfRep, Gene, num, Drug, scan, Plate, Row, Col, conc_num, all_of(config$y_var)), width = 1000)
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+ " is outside of y-limits: [", config$ylim_vals[1], ", ", config$ylim_vals[2], "]:")
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+ # print(out_of_bounds %>% select(OrfRep, Gene, num, Drug, scan, Plate, Row, Col, conc_num, all_of(config$y_var)), width = 1000)
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}
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-
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df <- df %>%
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filter(
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!is.na(.data[[config$y_var]]) &
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@@ -556,13 +546,17 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs, page_width
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)
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}
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- # Filter NAs if specified
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+ # Filter NAs
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if (!is.null(config$filter_na) && config$filter_na) {
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df <- df %>%
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filter(!is.na(.data[[config$y_var]]))
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}
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- # Set up aes mapping based on plot type
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+ if (nrow(df) == 0) {
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+ message("No data available after filtering for plot ", config$title)
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+ next # Skip this plot if no data is available
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+ }
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+
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aes_mapping <- if (config$plot_type == "bar") {
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if (!is.null(config$color_var)) {
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aes(x = .data[[config$x_var]], fill = .data[[config$color_var]], color = .data[[config$color_var]])
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@@ -587,16 +581,14 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs, page_width
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plot <- ggplot(df, aes_mapping) + theme_publication(legend_position = config$legend_position)
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- # Add appropriate plot layer or helper function based on plot type
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plot <- switch(config$plot_type,
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"scatter" = generate_scatter_plot(plot, config),
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"box" = generate_boxplot(plot, config),
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"density" = plot + geom_density(),
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"bar" = plot + geom_bar(),
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- plot # default (unused)
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+ plot # default
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)
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- # Add labels and title
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if (!is.null(config$title)) {
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plot <- plot + ggtitle(config$title)
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if (!is.null(config$title_size)) {
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@@ -607,44 +599,21 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs, page_width
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if (!is.null(config$y_label)) plot <- plot + ylab(config$y_label)
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if (!is.null(config$coord_cartesian)) plot <- plot + coord_cartesian(ylim = config$coord_cartesian)
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- # Add annotations if specified
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- if (!is.null(config$annotations)) {
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- for (annotation in config$annotations) {
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- plot <- plot +
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- annotate(
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- "text",
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- x = ifelse(is.null(annotation$x), 0, annotation$x),
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- y = ifelse(is.null(annotation$y), Inf, annotation$y),
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- label = annotation$label,
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- hjust = ifelse(is.null(annotation$hjust), 0.5, annotation$hjust),
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- vjust = ifelse(is.null(annotation$vjust), 1, annotation$vjust),
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- size = ifelse(is.null(annotation$size), 3, annotation$size),
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- color = ifelse(is.null(annotation$color), "black", annotation$color)
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- )
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- }
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- }
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-
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- # Convert ggplot to plotly for interactive version
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plotly_plot <- suppressWarnings(plotly::ggplotly(plot))
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- # Store both static and interactive versions
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static_plots[[i]] <- plot
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plotly_plots[[i]] <- plotly_plot
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}
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- # Print the plots in the current group to the PDF
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+ grid_layout <- group$grid_layout
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if (!is.null(grid_layout)) {
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- # Set grid_ncol to 1 if not specified
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if (is.null(grid_layout$ncol)) {
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grid_layout$ncol <- 1
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}
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- # If ncol is set but nrow is not, calculate nrow dynamically based on num_plots
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if (!is.null(grid_layout$ncol) && is.null(grid_layout$nrow)) {
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num_plots <- length(static_plots)
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- nrow <- ceiling(num_plots / grid_layout$ncol)
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- # message("No nrow provided, automatically using nrow = ", nrow)
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- grid_layout$nrow <- nrow
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+ grid_layout$nrow <- ceiling(num_plots / grid_layout$ncol)
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}
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total_spots <- grid_layout$nrow * grid_layout$ncol
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@@ -655,24 +624,26 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs, page_width
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static_plots <- c(static_plots, replicate(total_spots - num_plots, nullGrob(), simplify = FALSE))
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}
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- # Print a page of gridded plots
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- grid.arrange(
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- grobs = static_plots,
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- ncol = grid_layout$ncol,
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- nrow = grid_layout$nrow)
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-
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+ tryCatch({
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+ grid.arrange(
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+ grobs = static_plots,
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+ ncol = grid_layout$ncol,
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+ nrow = grid_layout$nrow
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+ )
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+ }, error = function(e) {
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+ message("Error in grid.arrange: ", e$message)
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+ print(static_plots)
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+ })
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+
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} else {
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- # Print individual plots on separate pages if no grid layout
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for (plot in static_plots) {
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print(plot)
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}
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}
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}
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- # Close the PDF device after all plots are done
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dev.off()
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- # Save HTML file with interactive plots if needed
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out_html_file <- file.path(out_dir, paste0(filename, ".html"))
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message("Saving combined HTML file: ", out_html_file)
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htmltools::save_html(
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@@ -681,6 +652,7 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs, page_width
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)
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}
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+
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generate_scatter_plot <- function(plot, config) {
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# Define the points
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@@ -1118,7 +1090,7 @@ generate_interaction_plot_configs <- function(df_summary, df_interactions, type)
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x_breaks = unique(group_data$conc_num_factor_factor),
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x_labels = as.character(unique(group_data$conc_num)),
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ylim_vals = y_limits,
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- filter_na = TRUE,
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+ # filter_na = TRUE,
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lm_line = list(
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intercept = lm_intercept_value,
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slope = lm_slope_value,
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