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Post plotting refactor

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Bryan Roessler
2024-09-07 21:11:32 -04:00
parent 2407a752fc
commit 8f2f8d28f0
1 changed files with 100 additions and 223 deletions
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317
workflow/apps/r/calculate_interaction_zscores5.R
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@@ -159,61 +159,40 @@ update_gene_names <- function(df, sgd_gene_list) {
# Process either deletion and or reference strain(s) # Process either deletion and or reference strain(s)
process_strains <- function(df) { process_strains <- function(df) {
df_strains <- data.frame() # Initialize an empty dataframe to store results message("Processing strains")
for (concentration in unique(df$conc_num)) { df %>%
message("Processing concentration: ", concentration) group_by(conc_num) %>%
df_temp <- df %>% filter(conc_num == concentration) mutate(
max_l_theoretical = max(max_L, na.rm = TRUE),
if (concentration > 0) { L = ifelse(L == 0 & !is.na(L) & conc_num > 0, max_l_theoretical, L),
max_l_theoretical <- df_temp %>% pull(max_L) SM = ifelse(L >= max_l_theoretical & !is.na(L) & conc_num > 0, 1, SM),
L = ifelse(L >= max_l_theoretical & !is.na(L) & conc_num > 0, max_l_theoretical, L)) %>%
df_temp <- df_temp %>% ungroup()
mutate(
L = ifelse(L == 0 & !is.na(L), max_l_theoretical, L), # Replace zero values with max theoretical
SM = ifelse(L >= max_l_theoretical & !is.na(L), 1, SM), # Set SM flag
L = ifelse(L >= max_l_theoretical & !is.na(L), max_l_theoretical, L) # Cap L values
)
}
# Append the results of this concentration to df_strains
df_strains <- bind_rows(df_strains, df_temp)
}
return(df_strains)
} }
# Calculate summary statistics for all variables # Calculate summary statistics for all variables
calculate_summary_stats <- function(df, variables, group_vars = c("conc_num", "conc_num_factor")) { calculate_summary_stats <- function(df, variables, group_vars = c("conc_num", "conc_num_factor")) {
# Replace 0 values with NA
df <- df %>% df <- df %>%
mutate(across(all_of(variables), ~ifelse(. == 0, NA, .))) mutate(across(all_of(variables), ~ ifelse(. == 0, NA, .)))
print("Head of df for summary stats calculations:")
print(head(df))
# Calculate summary statistics, including a single N based on L
summary_stats <- df %>% summary_stats <- df %>%
group_by(across(all_of(group_vars))) %>% group_by(across(all_of(group_vars))) %>%
reframe( summarise(
N = sum(!is.na(L)), # Single N based on L N = sum(!is.na(L)),
across(all_of(variables), list( across(all_of(variables), list(
mean = ~mean(., na.rm = TRUE), mean = ~mean(., na.rm = TRUE),
median = ~median(., na.rm = TRUE), median = ~median(., na.rm = TRUE),
max = ~max(., na.rm = TRUE), max = ~max(., na.rm = TRUE),
min = ~min(., na.rm = TRUE), min = ~min(., na.rm = TRUE),
sd = ~sd(., na.rm = TRUE), sd = ~sd(., na.rm = TRUE),
se = ~sd(., na.rm = TRUE) / sqrt(sum(!is.na(.)) - 1) # TODO unsure why this is - 1 se = ~sd(., na.rm = TRUE) / sqrt(sum(!is.na(.)) - 1)
), .names = "{.fn}_{.col}") ), .names = "{.fn}_{.col}")
) )
print("Summary stats:") df_cleaned <- df %>%
print(head(summary_stats)) select(-any_of(names(summary_stats)))
# Remove existing stats columns from df if they already exist
stat_columns <- setdiff(names(summary_stats), group_vars)
df_cleaned <- df %>% select(-any_of(stat_columns))
# Join the summary stats back to the cleaned original dataframe
df_with_stats <- left_join(df_cleaned, summary_stats, by = group_vars) df_with_stats <- left_join(df_cleaned, summary_stats, by = group_vars)
return(list(summary_stats = summary_stats, df_with_stats = df_with_stats)) return(list(summary_stats = summary_stats, df_with_stats = df_with_stats))
@@ -222,11 +201,6 @@ calculate_summary_stats <- function(df, variables, group_vars = c("conc_num", "c
# Calculate interaction scores # Calculate interaction scores
calculate_interaction_scores <- function(df, max_conc, variables, group_vars = c("OrfRep", "Gene", "num")) { calculate_interaction_scores <- function(df, max_conc, variables, group_vars = c("OrfRep", "Gene", "num")) {
# if (nrow(df) == 0) {
# message("Dataframe is empty after filtering")
# return(NULL) # Or handle the empty dataframe case as needed
# }
# Calculate total concentration variables # Calculate total concentration variables
total_conc_num <- length(unique(df$conc_num)) total_conc_num <- length(unique(df$conc_num))
num_non_removed_concs <- total_conc_num - sum(df$DB, na.rm = TRUE) - 1 num_non_removed_concs <- total_conc_num - sum(df$DB, na.rm = TRUE) - 1
@@ -318,10 +292,11 @@ calculate_interaction_scores <- function(df, max_conc, variables, group_vars = c
print("Calculating interaction scores part 2") print("Calculating interaction scores part 2")
interaction_scores_all <- interaction_scores %>% interaction_scores_all <- interaction_scores %>%
group_by(across(all_of(group_vars))) %>% group_by(across(all_of(group_vars))) %>%
mutate(lm_score_L = max_conc * coef(lm(Delta_L ~ conc_num_factor))[2] + coef(lm(Delta_L ~ conc_num_factor))[1], mutate(
lm_score_K = max_conc * coef(lm(Delta_K ~ conc_num_factor))[2] + coef(lm(Delta_K ~ conc_num_factor))[1], lm_score_L = max_conc * coef(lm(Delta_L ~ conc_num_factor))[2] + coef(lm(Delta_L ~ conc_num_factor))[1],
lm_score_r = max_conc * coef(lm(Delta_r ~ conc_num_factor))[2] + coef(lm(Delta_r ~ conc_num_factor))[1], lm_score_K = max_conc * coef(lm(Delta_K ~ conc_num_factor))[2] + coef(lm(Delta_K ~ conc_num_factor))[1],
lm_score_AUC = max_conc * coef(lm(Delta_AUC ~ conc_num_factor))[2] + coef(lm(Delta_AUC ~ conc_num_factor))[1]) %>% lm_score_r = max_conc * coef(lm(Delta_r ~ conc_num_factor))[2] + coef(lm(Delta_r ~ conc_num_factor))[1],
lm_score_AUC = max_conc * coef(lm(Delta_AUC ~ conc_num_factor))[2] + coef(lm(Delta_AUC ~ conc_num_factor))[1]) %>%
mutate( mutate(
Avg_Zscore_L = sum(Z_Shift_L, na.rm = TRUE) / num_non_removed_concs, Avg_Zscore_L = sum(Z_Shift_L, na.rm = TRUE) / num_non_removed_concs,
Avg_Zscore_K = sum(Z_Shift_K, na.rm = TRUE) / num_non_removed_concs, Avg_Zscore_K = sum(Z_Shift_K, na.rm = TRUE) / num_non_removed_concs,
@@ -338,11 +313,9 @@ calculate_interaction_scores <- function(df, max_conc, variables, group_vars = c
return(list(zscores_calculations = interaction_scores_all, zscores_interactions = interaction_scores)) return(list(zscores_calculations = interaction_scores_all, zscores_interactions = interaction_scores))
} }
generate_plot <- function(df, x_var, y_var = NULL, plot_type = "scatter", color_var = "conc_num", generate_plot <- function(df, x_var, y_var = NULL, plot_type = "scatter", color_var = "conc_num",
title = "", x_label = NULL, y_label = NULL, ylim_vals = NULL) { title = "", x_label = NULL, y_label = NULL, ylim_vals = NULL, annotations = NULL) {
# Initialize plot with dynamic x and color variables
plot <- ggplot(df, aes(x = !!sym(x_var), color = as.factor(!!sym(color_var)))) plot <- ggplot(df, aes(x = !!sym(x_var), color = as.factor(!!sym(color_var))))
if (!is.null(y_var)) { if (!is.null(y_var)) {
@@ -353,12 +326,14 @@ generate_plot <- function(df, x_var, y_var = NULL, plot_type = "scatter", color_
y_sd_col <- paste0("sd_", y_var) y_sd_col <- paste0("sd_", y_var)
plot <- plot + geom_point(shape = 3, size = 0.2) + plot <- plot + geom_point(shape = 3, size = 0.2) +
geom_errorbar(aes(ymin = !!sym(y_mean_col) - !!sym(y_sd_col), geom_errorbar(aes(
ymax = !!sym(y_mean_col) + !!sym(y_sd_col)), width = 0.1) + ymin = !!sym(y_mean_col) - !!sym(y_sd_col),
ymax = !!sym(y_mean_col) + !!sym(y_sd_col)),
width = 0.1) +
geom_point(aes(y = !!sym(y_mean_col)), size = 0.6) geom_point(aes(y = !!sym(y_mean_col)), size = 0.6)
} }
# Add the required plot type (scatter, box, density, bar) # Add the specified plot type
plot <- switch(plot_type, plot <- switch(plot_type,
"box" = plot + geom_boxplot(), "box" = plot + geom_boxplot(),
"density" = plot + geom_density(), "density" = plot + geom_density(),
@@ -366,20 +341,27 @@ generate_plot <- function(df, x_var, y_var = NULL, plot_type = "scatter", color_
plot + geom_point() + geom_smooth(method = "lm", se = FALSE) # Default is scatter plot + geom_point() + geom_smooth(method = "lm", se = FALSE) # Default is scatter
) )
# Apply y-axis limits if specified
if (!is.null(ylim_vals)) { if (!is.null(ylim_vals)) {
plot <- plot + coord_cartesian(ylim = ylim_vals) plot <- plot + coord_cartesian(ylim = ylim_vals)
} }
# Add title and axis labels
plot <- plot + ggtitle(title) + theme_publication() plot <- plot + ggtitle(title) + theme_publication()
if (!is.null(x_label)) plot <- plot + xlab(x_label) if (!is.null(x_label)) plot <- plot + xlab(x_label)
if (!is.null(y_label)) plot <- plot + ylab(y_label) if (!is.null(y_label)) plot <- plot + ylab(y_label)
if (!is.null(annotations)) {
for (annotation in annotations) {
plot <- plot + annotate("text",
x = annotation$x,
y = annotation$y,
label = annotation$label)
}
}
return(plot) return(plot)
} }
generate_and_save_plots <- function(df, output_dir, file_prefix, plot_configs) { generate_and_save_plots <- function(df, output_dir, file_prefix, plot_configs) {
plots <- list() plots <- list()
@@ -391,24 +373,16 @@ generate_and_save_plots <- function(df, output_dir, file_prefix, plot_configs) {
message("Generating plots for dataframe") message("Generating plots for dataframe")
for (config in plot_configs) { for (config in plot_configs) {
# Generate the plot using the configurations
plot <- generate_plot( plot <- generate_plot(
df = df, df = df,
x_var = config$x_var, x_var = config$x_var,
y_var = config$y_var, y_var = config$y_var,
plot_type = config$plot_type, plot_type = config$plot_type,
title = config$title, title = config$title,
ylim_vals = config$ylim_vals ylim_vals = config$ylim_vals,
annotations = config$annotations
) )
# Add custom annotations if provided
if (!is.null(config$annotations)) {
for (annotation in config$annotations) {
plot <- plot + annotate("text", x = annotation$x, y = annotation$y, label = annotation$label)
}
}
# Store the plot with variable name and plot type
plots[[paste0(config$y_var, "_", config$plot_type)]] <- plot plots[[paste0(config$y_var, "_", config$plot_type)]] <- plot
} }
@@ -416,16 +390,12 @@ generate_and_save_plots <- function(df, output_dir, file_prefix, plot_configs) {
save_plots(file_prefix, plots, output_dir) save_plots(file_prefix, plots, output_dir)
} }
# Standardized saving of plots
# Ensure all plots are saved and printed to PDF
save_plots <- function(file_name, plot_list, output_dir) { save_plots <- function(file_name, plot_list, output_dir) {
# Save all plots to a single PDF
pdf(file.path(output_dir, paste0(file_name, ".pdf")), width = 14, height = 9) pdf(file.path(output_dir, paste0(file_name, ".pdf")), width = 14, height = 9)
lapply(plot_list, print) lapply(plot_list, print)
dev.off() dev.off()
# Save each plot as an interactive HTML file
lapply(names(plot_list), function(plot_name) { lapply(names(plot_list), function(plot_name) {
pgg <- tryCatch({ pgg <- tryCatch({
suppressWarnings(ggplotly(plot_list[[plot_name]]) %>% suppressWarnings(ggplotly(plot_list[[plot_name]]) %>%
@@ -436,109 +406,79 @@ save_plots <- function(file_name, plot_list, output_dir) {
}) })
if (!is.null(pgg)) { if (!is.null(pgg)) {
saveWidget(pgg, file = file.path(output_dir, paste0(file_name, "_", plot_name, ".html")), selfcontained = TRUE) saveWidget(pgg,
file = file.path(output_dir, paste0(file_name, "_", plot_name, ".html")),
selfcontained = TRUE)
} }
}) })
} }
interaction_plot_configs <- function(df, variables) {
plot_configs <- list()
interaction_plot_configs <- function(df, variable) { for (variable in variables) {
ylim_vals <- switch(variable, # Define the y-limits based on the variable being plotted
"L" = c(-65, 65), ylim_vals <- switch(variable,
"K" = c(-65, 65), "L" = c(-65, 65),
"r" = c(-0.65, 0.65), "K" = c(-65, 65),
"AUC" = c(-6500, 6500), "r" = c(-0.65, 0.65),
NULL "AUC" = c(-6500, 6500)
)
wt_sd <- paste0("WT_sd_", variable)
delta_var <- paste0("Delta_", variable)
z_shift <- paste0("Z_Shift_", variable)
z_lm <- paste0("Z_lm_", variable)
list(
x_var = "conc_num_factor",
y_var = delta_var,
plot_type = "scatter",
title = paste("Scatter plot for", variable),
ylim_vals = ylim_vals,
annotations = list(
list(x = 1, y = max(ylim_vals) * 0.7, label = paste("ZShift =", round(df[[z_shift]], 2))),
list(x = 1, y = max(ylim_vals) * 0.5, label = paste("Z lm Score =", round(df[[z_lm]], 2))),
list(x = 1, y = min(ylim_vals) * 0.7, label = paste("NG =", df$NG)),
list(x = 1, y = min(ylim_vals) * 0.85, label = paste("DB =", df$DB)),
list(x = 1, y = min(ylim_vals) * 1.1, label = paste("SM =", df$SM))
) )
)
}
generate_interaction_plots <- function(df, output_file) { # Dynamically generate the column names for standard deviation and delta
message("Generating interaction plots") wt_sd_col <- paste0("WT_sd_", variable)
delta_var <- paste0("Delta_", variable)
z_shift <- paste0("Z_Shift_", variable)
z_lm <- paste0("Z_lm_", variable)
# Variables to be plotted # Set annotations for ZShift, Z lm Score, NG, DB, SM
variables <- c("L", "K", "r", "AUC") annotations <- list(
ylims <- list( list(x = 1, y = ifelse(variable == "L", 45, ifelse(variable == "K", 45,
L = c(0, 160), ifelse(variable == "r", 0.45, 4500))), label = paste("ZShift =", round(df[[z_shift]], 2))),
K = c(-20, 160), list(x = 1, y = ifelse(variable == "L", 25, ifelse(variable == "K", 25,
r = c(0, 1), ifelse(variable == "r", 0.25, 2500))), label = paste("lm ZScore =", round(df[[z_lm]], 2))),
AUC = c(0, 12500) list(x = 1, y = ifelse(variable == "L", -25, ifelse(variable == "K", -25,
) ifelse(variable == "r", -0.25, -2500))), label = paste("NG =", df$NG)),
list(x = 1, y = ifelse(variable == "L", -35, ifelse(variable == "K", -35,
ifelse(variable == "r", -0.35, -3500))), label = paste("DB =", df$DB)),
list(x = 1, y = ifelse(variable == "L", -45, ifelse(variable == "K", -45,
ifelse(variable == "r", -0.45, -4500))), label = paste("SM =", df$SM))
)
plot_list <- list() # Define the configuration for each variable (plot type, limits, labels)
plot_configs[[variable]] <- list(
# Generate plots for each variable using the existing plotting function
for (var in variables) {
plot <- generate_plot(
df = df,
x_var = "conc_num_factor", x_var = "conc_num_factor",
y_var = var, y_var = delta_var,
plot_type = "scatter", plot_type = "scatter",
title = paste("Scatter RF for", var, "with SD"), title = paste(df$OrfRep[1], df$Gene[1], sep = " "),
ylim_vals = ylims[[var]] ylim_vals = ylim_vals,
) + annotations = annotations,
annotate("text", x = -0.25, y = ifelse(var == "L", 10, ifelse(var == "K", -5, 0.9)), label = "NG") + error_bar = list(
annotate("text", x = -0.25, y = ifelse(var == "L", 5, ifelse(var == "K", -12.5, 0.8)), label = "DB") + ymin = 0 - (2 * df[[wt_sd_col]]),
annotate("text", x = -0.25, y = ifelse(var == "L", 0, ifelse(var == "K", -20, 0.7)), label = "SM") + ymax = 0 + (2 * df[[wt_sd_col]])
annotate("text", x = unique(df$conc_num_factor), y = ifelse(var == "L", 10, ifelse(var == "K", -5, 0.9)), label = df$NG) + ),
annotate("text", x = unique(df$conc_num_factor), y = ifelse(var == "L", 5, ifelse(var == "K", -12.5, 0.8)), label = df$DB) + x_breaks = unique(df$conc_num_factor),
annotate("text", x = unique(df$conc_num_factor), y = ifelse(var == "L", 0, ifelse(var == "K", -20, 0.7)), label = df$SM) x_labels = unique(as.character(df$conc_num)),
x_label = unique(df$Drug[1])
plot_list[[var]] <- plot )
} }
# Save plots in a PDF return(plot_configs)
pdf(output_file, width = 16, height = 16)
grid.arrange(
plot_list$L, plot_list$K,
plot_list$r, plot_list$AUC,
ncol = 2, nrow = 2
)
dev.off()
} }
generate_cpp_correlation_plots <- function(df_na_rm, lm_list, output_dir) { correlation_plot_configs <- function(df, lm_list, lm_summaries) {
lm_summaries <- lapply(lm_list, summary) lapply(seq_along(lm_list), function(i) {
# Define plot titles and annotation based on R-squared values from the lm_list
plot_configs <- lapply(seq_along(lm_list), function(i) {
r_squared <- round(lm_summaries[[i]]$r.squared, 3) r_squared <- round(lm_summaries[[i]]$r.squared, 3)
plot_titles <- c("Interaction L vs. Interaction K", "Interaction L vs. Interaction r", "Interaction L vs. Interaction AUC",
"Interaction K vs. Interaction r", "Interaction K vs. Interaction AUC", "Interaction r vs. Interaction AUC")
list( list(
x_var = names(lm_list)[i][1], x_var = names(lm_list)[i][1],
y_var = names(lm_list)[i][2], y_var = names(lm_list)[i][2],
plot_type = "scatter", plot_type = "scatter",
title = plot_titles[i], title = paste("Correlation between", names(lm_list)[i][1], "and", names(lm_list)[i][2]),
annotations = list(list(x = 0, y = 0, label = paste("R-squared =", r_squared))) annotations = list(list(x = 0, y = 0, label = paste("R-squared =", r_squared)))
) )
}) })
# Generate and save the plots using the new system
generate_and_save_plots(df_na_rm, output_dir, "Correlation_CPPs", plot_configs)
} }
# Adjust missing values and calculate ranks # Adjust missing values and calculate ranks
adjust_missing_and_rank <- function(df, variables) { adjust_missing_and_rank <- function(df, variables) {
@@ -553,7 +493,6 @@ adjust_missing_and_rank <- function(df, variables) {
return(df) return(df)
} }
generate_plots <- function(df, x_var, y_vars, plot_type, color_var = "conc_num", title_prefix = "", generate_plots <- function(df, x_var, y_vars, plot_type, color_var = "conc_num", title_prefix = "",
x_label = NULL, y_label = NULL, ylim_vals = NULL, output_dir, file_prefix = "plot") { x_label = NULL, y_label = NULL, ylim_vals = NULL, output_dir, file_prefix = "plot") {
plot_list <- list() plot_list <- list()
@@ -590,40 +529,6 @@ generate_plots <- function(df, x_var, y_vars, plot_type, color_var = "conc_num",
save_plots(file_prefix, plot_list, output_dir) save_plots(file_prefix, plot_list, output_dir)
} }
# Function to generate rank plots for the provided dataframe
# create_ranked_plots <- function(df, output_dir) {
# # List of variables for which we need to generate rank plots
# variables <- c("L", "K", "r", "AUC")
# # Adjust missing values and calculate ranks
# df_adjusted <- adjust_missing_and_rank(df, variables)
# # Generate rank plots
# for (var in variables) {
# plot_rank_avg <- generate_plot(
# df = df_adjusted,
# x_var = paste0(var, "_Rank"),
# y_var = paste0("Avg_Zscore_", var),
# plot_type = "scatter",
# title = paste("Average Z score vs Rank for", var)
# )
# plot_rank_lm <- generate_plot(
# df = df_adjusted,
# x_var = paste0(var, "_Rank_lm"),
# y_var = paste0("Z_lm_", var),
# plot_type = "scatter",
# title = paste("Interaction Z score vs Rank for", var)
# )
# save_plots(paste0("RankPlots_", var), list(
# plot_rank_avg = plot_rank_avg,
# plot_rank_lm = plot_rank_lm
# ), output_dir)
# }
# }
main <- function() { main <- function() {
lapply(names(args$experiments), function(exp_name) { lapply(names(args$experiments), function(exp_name) {
exp <- args$experiments[[exp_name]] exp <- args$experiments[[exp_name]]
@@ -807,8 +712,10 @@ main <- function() {
write.csv(zscores_interactions, file = file.path(out_dir, "ZScores_Interaction.csv"), row.names = FALSE) write.csv(zscores_interactions, file = file.path(out_dir, "ZScores_Interaction.csv"), row.names = FALSE)
# Create interaction plots # Create interaction plots
generate_interaction_plots(df_reference, output_file = file.path(output_dir, "RF_InteractionPlots.pdf")) deletion_plot_configs <- interaction_plot_configs(df_reference, variables)
generate_interaction_plots(df_deletion, output_file = file.path(output_dir, "InteractionPlots.pdf")) generate_and_save_plots(zscores_calculations, out_dir, "RF_InteractionPlots", deletion_plot_configs)
deletion_plot_configs <- interaction_plot_configs(df_deletion, variables)
generate_and_save_plots(zscores_calculations, out_dir, "InteractionPlots", deletion_plot_configs)
# Define conditions for enhancers and suppressors # Define conditions for enhancers and suppressors
# TODO Add to study config file? # TODO Add to study config file?
@@ -861,24 +768,13 @@ main <- function() {
# Interaction plots for reference strain # Interaction plots for reference strain
variables <- c("L", "K", "r", "AUC") variables <- c("L", "K", "r", "AUC")
reference_plot_configs <- lapply(variables, function(var) { reference_plot_configs <- interaction_plot_configs(df_reference, variables)
interaction_plot_configs(zscores_calculations_reference, var) generate_and_save_plots(zscores_calculation_reference, out_dir, "RF_InteractionPlots", reference_plot_configs)
})
generate_and_save_plots(zscores_calculations_reference, out_dir, "RF_InteractionPlots", reference_plot_configs)
# Interaction plots for deletion strains
deletion_plot_configs <- lapply(variables, function(var) {
interaction_plot_configs(zscores_calculations, var)
})
deletion_plot_configs <- interaction_plot_configs(df_deletion, variables)
generate_and_save_plots(zscores_calculations, out_dir, "InteractionPlots", deletion_plot_configs) generate_and_save_plots(zscores_calculations, out_dir, "InteractionPlots", deletion_plot_configs)
# Apply filtering to remove NA values before further analysis # Correlation plots
zscores_interactions_filtered <- zscores_interactions %>%
filter(!is.na(Z_lm_L) | !is.na(Avg_Zscore_L))
lm_list <- list( lm_list <- list(
lm(Z_lm_K ~ Z_lm_L, data = zscores_interactions_filtered), lm(Z_lm_K ~ Z_lm_L, data = zscores_interactions_filtered),
lm(Z_lm_r ~ Z_lm_L, data = zscores_interactions_filtered), lm(Z_lm_r ~ Z_lm_L, data = zscores_interactions_filtered),
@@ -888,26 +784,10 @@ main <- function() {
lm(Z_lm_AUC ~ Z_lm_r, data = zscores_interactions_filtered) lm(Z_lm_AUC ~ Z_lm_r, data = zscores_interactions_filtered)
) )
# Generate cpp correlation plots lm_summaries <- lapply(lm_list, summary)
correlation_plot_config <- list( correlation_plot_config <- correlation_plot_configs(zscores_interactions_filtered, lm_list, lm_summaries)
list(x_var = "Z_lm_L", y_var = "Z_lm_K", plot_type = "scatter", title = "Correlation between Z_lm_L and Z_lm_K",
annotations = list(list(x = 0, y = 0, label = paste("R-squared =", round(lm_summaries[[1]]$r.squared, 3)))), ),
list(x_var = "Z_lm_L", y_var = "Z_lm_r", plot_type = "scatter", title = "Correlation between Z_lm_L and Z_lm_r",
annotations = list(list(x = 0, y = 0, label = paste("R-squared =", round(lm_summaries[[1]]$r.squared, 3)))), ),
list(x_var = "Z_lm_L", y_var = "Z_lm_AUC", plot_type = "scatter", title = "Correlation between Z_lm_L and Z_lm_AUC",
annotations = list(list(x = 0, y = 0, label = paste("R-squared =", round(lm_summaries[[1]]$r.squared, 3)))), ),
list(x_var = "Z_lm_K", y_var = "Z_lm_r", plot_type = "scatter", title = "Correlation between Z_lm_K and Z_lm_r",
annotations = list(list(x = 0, y = 0, label = paste("R-squared =", round(lm_summaries[[1]]$r.squared, 3)))), ),
list(x_var = "Z_lm_K", y_var = "Z_lm_AUC", plot_type = "scatter", title = "Correlation between Z_lm_K and Z_lm_AUC",
annotations = list(list(x = 0, y = 0, label = paste("R-squared =", round(lm_summaries[[1]]$r.squared, 3)))), ),
list(x_var = "Z_lm_r", y_var = "Z_lm_AUC", plot_type = "scatter", title = "Correlation between Z_lm_r and Z_lm_AUC",
annotations = list(list(x = 0, y = 0, label = paste("R-squared =", round(lm_summaries[[1]]$r.squared, 3)))), )
)
generate_and_save_plots(zscores_interactions_filtered, output_dir, "CorrelationPlots", correlation_plot_config) generate_and_save_plots(zscores_interactions_filtered, output_dir, "CorrelationPlots", correlation_plot_config)
# Generate cpp correlation plots
#generate_cpp_correlation_plots(zscores_interactions_filtered, lm_list, out_dir)
# Generate ranked plots # Generate ranked plots
rank_plot_config <- list( rank_plot_config <- list(
list(x_var = "L_Rank", y_var = "Avg_Zscore_L", plot_type = "scatter", title = "Rank vs Avg Z score for L"), list(x_var = "L_Rank", y_var = "Avg_Zscore_L", plot_type = "scatter", title = "Rank vs Avg Z score for L"),
@@ -917,9 +797,6 @@ main <- function() {
) )
# Generate and save rank plots using the existing plotting framework # Generate and save rank plots using the existing plotting framework
generate_and_save_plots(zscores_interactions_filtered, output_dir, "RankPlots", rank_plot_config) generate_and_save_plots(zscores_interactions_filtered, output_dir, "RankPlots", rank_plot_config)
}) })
}) })
} }