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Reformat rank plot configs

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This commit is contained in:
Bryan Roessler
2024-09-17 15:06:27 -04:00
parent 012c5e1d69
commit 2ddba41950
1 changed files with 133 additions and 141 deletions
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36
qhtcp-workflow/apps/r/calculate_interaction_zscores.R
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@@ -721,10 +721,9 @@ generate_interaction_plot_configs <- function(df, variables) {
}
generate_rank_plot_configs <- function(df_filtered, variables, is_lm = FALSE) {
# Define SD bands
sd_bands <- c(1, 2, 3)
# Initialize list to store plot configurations
configs <- list()
# SD-based plots for L and K
@@ -741,11 +740,10 @@ generate_rank_plot_configs <- function(df_filtered, variables, is_lm = FALSE) {
y_label <- paste("Avg Z score", variable)
}
# Calculate counts for annotations
num_enhancers <- sum(df_filtered[[zscore_var]] >= sd_band, na.rm = TRUE)
num_suppressors <- sum(df_filtered[[zscore_var]] <= -sd_band, na.rm = TRUE)
# Annotated Plot Configuration
# Annotated plot configuration
configs[[length(configs) + 1]] <- list(
df = df_filtered,
x_var = rank_var,
@@ -795,16 +793,13 @@ generate_rank_plot_configs <- function(df_filtered, variables, is_lm = FALSE) {
# Avg ZScore and Rank Avg ZScore Plots for r, L, K, and AUC
for (variable in variables) {
for (plot_type in c("Avg_Zscore_vs_lm", "Rank_Avg_Zscore_vs_lm")) {
# Define x and y variables based on plot type
# Define specific variables based on plot type
if (plot_type == "Avg_Zscore_vs_lm") {
x_var <- paste0("Avg_Zscore_", variable)
y_var <- paste0("Z_lm_", variable)
title_suffix <- paste("Avg Zscore vs lm", variable)
# Add rectangles configuration for Avg_Zscore_vs_lm
rectangles <- list(
list(
xmin = -2, xmax = 2, ymin = -2, ymax = 2,
list(xmin = -2, xmax = 2, ymin = -2, ymax = 2,
fill = NA, color = "grey20", alpha = 0.1
)
)
@@ -823,10 +818,8 @@ generate_rank_plot_configs <- function(df_filtered, variables, is_lm = FALSE) {
next # Skip this iteration if the fit is perfect
}
# Calculate R-squared
r_squared <- summary(lm_fit)$r.squared
# Plot Configuration
configs[[length(configs) + 1]] <- list(
df = df_filtered,
x_var = x_var,
@@ -853,7 +846,6 @@ generate_rank_plot_configs <- function(df_filtered, variables, is_lm = FALSE) {
)
}
}
return(configs)
}
@@ -1247,16 +1239,16 @@ main <- function() {
)
)
message("Generating quality control plots")
generate_and_save_plots(out_dir_qc, "L_vs_K_before_quality_control", l_vs_k_plots)
generate_and_save_plots(out_dir_qc, "frequency_delta_background", frequency_delta_bg_plots)
generate_and_save_plots(out_dir_qc, "L_vs_K_above_threshold", above_threshold_plots)
generate_and_save_plots(out_dir_qc, "plate_analysis", plate_analysis_plots)
generate_and_save_plots(out_dir_qc, "plate_analysis_boxplots", plate_analysis_boxplots)
generate_and_save_plots(out_dir_qc, "plate_analysis_no_zeros", plate_analysis_no_zeros_plots)
generate_and_save_plots(out_dir_qc, "plate_analysis_no_zeros_boxplots", plate_analysis_no_zeros_boxplots)
generate_and_save_plots(out_dir_qc, "L_vs_K_for_strains_2SD_outside_mean_K", l_outside_2sd_k_plots)
generate_and_save_plots(out_dir_qc, "delta_background_vs_K_for_strains_2sd_outside_mean_K", delta_bg_outside_2sd_k_plots)
# message("Generating quality control plots")
# generate_and_save_plots(out_dir_qc, "L_vs_K_before_quality_control", l_vs_k_plots)
# generate_and_save_plots(out_dir_qc, "frequency_delta_background", frequency_delta_bg_plots)
# generate_and_save_plots(out_dir_qc, "L_vs_K_above_threshold", above_threshold_plots)
# generate_and_save_plots(out_dir_qc, "plate_analysis", plate_analysis_plots)
# generate_and_save_plots(out_dir_qc, "plate_analysis_boxplots", plate_analysis_boxplots)
# generate_and_save_plots(out_dir_qc, "plate_analysis_no_zeros", plate_analysis_no_zeros_plots)
# generate_and_save_plots(out_dir_qc, "plate_analysis_no_zeros_boxplots", plate_analysis_no_zeros_boxplots)
# generate_and_save_plots(out_dir_qc, "L_vs_K_for_strains_2SD_outside_mean_K", l_outside_2sd_k_plots)
# generate_and_save_plots(out_dir_qc, "delta_background_vs_K_for_strains_2sd_outside_mean_K", delta_bg_outside_2sd_k_plots)
# Process background strains
bg_strains <- c("YDL227C")