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Before old rank plot removal

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This commit is contained in:
Bryan Roessler
2024-09-07 19:31:52 -04:00
parent fcc6cdd2d4
commit 2407a752fc
1 changed files with 224 additions and 164 deletions
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386
workflow/apps/r/calculate_interaction_zscores5.R
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@@ -9,6 +9,7 @@ suppressMessages({
}) })
options(warn = 2, max.print = 1000) options(warn = 2, max.print = 1000)
options(width = 10000)
# Set the memory limit to 30GB (30 * 1024 * 1024 * 1024 bytes) # Set the memory limit to 30GB (30 * 1024 * 1024 * 1024 bytes)
soft_limit <- 30 * 1024 * 1024 * 1024 soft_limit <- 30 * 1024 * 1024 * 1024
@@ -156,7 +157,7 @@ update_gene_names <- function(df, sgd_gene_list) {
return(df) return(df)
} }
# Process strains (deletion and reference) # 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 df_strains <- data.frame() # Initialize an empty dataframe to store results
@@ -187,6 +188,9 @@ calculate_summary_stats <- function(df, variables, group_vars = c("conc_num", "c
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 # 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))) %>%
@@ -195,13 +199,16 @@ calculate_summary_stats <- function(df, variables, group_vars = c("conc_num", "c
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 = ~ifelse(all(is.na(.)), NA, max(., na.rm = TRUE)), max = ~max(., na.rm = TRUE),
min = ~ifelse(all(is.na(.)), NA, min(., na.rm = TRUE)), min = ~min(., na.rm = TRUE),
sd = ~ifelse(sum(!is.na(.)) > 1, sd(., na.rm = TRUE), 0), # If N == 1, sd is set to 0 sd = ~sd(., na.rm = TRUE),
se = ~ifelse(sum(!is.na(.)) > 1, sd(., na.rm = TRUE) / sqrt(sum(!is.na(.)) - 1), 0) # If N == 1, se is set to 0 se = ~sd(., na.rm = TRUE) / sqrt(sum(!is.na(.)) - 1) # TODO unsure why this is - 1
), .names = "{.fn}_{.col}") ), .names = "{.fn}_{.col}")
) )
print("Summary stats:")
print(head(summary_stats))
# Remove existing stats columns from df if they already exist # Remove existing stats columns from df if they already exist
stat_columns <- setdiff(names(summary_stats), group_vars) stat_columns <- setdiff(names(summary_stats), group_vars)
df_cleaned <- df %>% select(-any_of(stat_columns)) df_cleaned <- df %>% select(-any_of(stat_columns))
@@ -215,10 +222,10 @@ 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) { # if (nrow(df) == 0) {
message("Dataframe is empty after filtering") # message("Dataframe is empty after filtering")
return(NULL) # Or handle the empty dataframe case as needed # 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))
@@ -304,6 +311,9 @@ calculate_interaction_scores <- function(df, max_conc, variables, group_vars = c
) %>% ) %>%
ungroup() ungroup()
print("Interaction scores:")
print(head(interaction_scores))
# Calculate linear models and interaction scores per gene # Calculate linear models and interaction scores per gene
print("Calculating interaction scores part 2") print("Calculating interaction scores part 2")
interaction_scores_all <- interaction_scores %>% interaction_scores_all <- interaction_scores %>%
@@ -329,6 +339,107 @@ calculate_interaction_scores <- function(df, max_conc, variables, group_vars = c
} }
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) {
# Initialize plot with dynamic x and color variables
plot <- ggplot(df, aes(x = !!sym(x_var), color = as.factor(!!sym(color_var))))
if (!is.null(y_var)) {
plot <- plot + aes(y = !!sym(y_var))
# Add scatter points with summary stats if `y_var` is present
y_mean_col <- paste0("mean_", y_var)
y_sd_col <- paste0("sd_", y_var)
plot <- plot + geom_point(shape = 3, size = 0.2) +
geom_errorbar(aes(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)
}
# Add the required plot type (scatter, box, density, bar)
plot <- switch(plot_type,
"box" = plot + geom_boxplot(),
"density" = plot + geom_density(),
"bar" = plot + geom_bar(stat = "identity"),
plot + geom_point() + geom_smooth(method = "lm", se = FALSE) # Default is scatter
)
# Apply y-axis limits if specified
if (!is.null(ylim_vals)) {
plot <- plot + coord_cartesian(ylim = ylim_vals)
}
# Add title and axis labels
plot <- plot + ggtitle(title) + theme_publication()
if (!is.null(x_label)) plot <- plot + xlab(x_label)
if (!is.null(y_label)) plot <- plot + ylab(y_label)
return(plot)
}
generate_and_save_plots <- function(df, output_dir, file_prefix, plot_configs) {
plots <- list()
if (nrow(df) == 0) {
message("The dataframe is empty, skipping plots")
return()
}
message("Generating plots for dataframe")
for (config in plot_configs) {
# Generate the plot using the configurations
plot <- generate_plot(
df = df,
x_var = config$x_var,
y_var = config$y_var,
plot_type = config$plot_type,
title = config$title,
ylim_vals = config$ylim_vals
)
# Add custom annotations if provided
if (!is.null(config$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
}
# Save plots to PDF and HTML
save_plots(file_prefix, plots, output_dir)
}
# Ensure all plots are saved and printed to PDF
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)
lapply(plot_list, print)
dev.off()
# Save each plot as an interactive HTML file
lapply(names(plot_list), function(plot_name) {
pgg <- tryCatch({
suppressWarnings(ggplotly(plot_list[[plot_name]]) %>%
layout(legend = list(orientation = "h")))
}, error = function(e) {
message("Error generating plot: ", plot_name)
return(NULL)
})
if (!is.null(pgg)) {
saveWidget(pgg, file = file.path(output_dir, paste0(file_name, "_", plot_name, ".html")), selfcontained = TRUE)
}
})
}
interaction_plot_configs <- function(df, variable) { interaction_plot_configs <- function(df, variable) {
@@ -361,102 +472,6 @@ interaction_plot_configs <- function(df, variable) {
) )
} }
generate_plot <- function(df, x_var, y_var = NULL, plot_type, color_var = "conc_num",
title, x_label = NULL, y_label = NULL, ylim_vals = NULL) {
# Use tidy evaluation with aes() and !!sym() for dynamic column names
plot <- ggplot(df, aes(x = !!sym(x_var), color = as.factor(!!sym(color_var))))
if (!is.null(y_var)) {
plot <- plot + aes(y = !!sym(y_var))
}
# Set up the plot based on the requested plot type
plot <- switch(plot_type,
"scatter" = plot + geom_point() + geom_smooth(method = "lm", se = FALSE),
"box" = plot + geom_boxplot(),
"density" = plot + geom_density(),
"bar" = plot + geom_bar(),
plot # Default: return the plot as is
)
if (!is.null(ylim_vals)) {
plot <- plot + coord_cartesian(ylim = ylim_vals)
}
# Add titles and labels if available
plot <- plot + ggtitle(title) + theme_publication()
if (!is.null(x_label)) plot <- plot + xlab(x_label)
if (!is.null(y_label)) plot <- plot + ylab(y_label)
return(plot)
}
generate_and_save_plots <- function(df, output_dir, file_prefix, plot_configs) {
plots <- list()
if (nrow(df) == 0) {
message("The \"", deparse(substitute(df)), "\" dataframe is empty, skipping plots")
return()
}
message("Generating plots for \"", deparse(substitute(df)), "\" dataframe")
for (config in plot_configs) {
plot <- generate_plot(
df = df,
x_var = config$x_var,
y_var = config$y_var,
plot_type = config$plot_type,
title = config$title,
ylim_vals = config$ylim_vals
)
# If custom annotations for interaction plots are required
if (!is.null(config$annotations)) {
for (annotation in config$annotations) {
plot <- plot +
annotate(
"text", x = annotation$x, y = annotation$y, label = annotation$label
)
}
}
# Store the plot with the variable name and plot type
plots[[paste0(config$y_var, "_", config$plot_type)]] <- plot
}
save_plots(file_prefix, plots, output_dir)
}
# Ensure all plots are saved and printed to PDF
save_plots <- function(file_name, plot_list, output_dir) {
# Save to PDF
pdf(file.path(output_dir, paste0(file_name, ".pdf")), width = 14, height = 9)
lapply(plot_list, function(plot) {
print(plot)
})
dev.off()
# Save to HTML with horizontal legend orientation
lapply(names(plot_list), function(plot_name) {
message("Generating plot: ", plot_name)
pgg <- tryCatch({
suppressWarnings(ggplotly(plot_list[[plot_name]]) %>%
layout(legend = list(orientation = "h")))
}, error = function(e) {
message("Error in plot: ", plot_name, "\n", e)
return(NULL)
})
if (!is.null(pgg)) {
saveWidget(pgg,
file = file.path(output_dir,
paste0(file_name, "_", plot_name, ".html")),
selfcontained = TRUE)
}
})
}
generate_interaction_plots <- function(df, output_file) { generate_interaction_plots <- function(df, output_file) {
message("Generating interaction plots") message("Generating interaction plots")
@@ -503,21 +518,27 @@ generate_interaction_plots <- function(df, output_file) {
generate_cpp_correlation_plots <- function(df_na_rm, lm_list, output_dir) { generate_cpp_correlation_plots <- function(df_na_rm, lm_list, output_dir) {
lm_summaries <- lapply(lm_list, summary) lm_summaries <- lapply(lm_list, summary)
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")
plot_list <- lapply(seq_along(lm_list), function(i) { # Define plot titles and annotation based on R-squared values from the lm_list
ggplot(df_na_rm, aes_string(x = names(lm_list)[i][1], y = names(lm_list)[i][2])) + plot_configs <- lapply(seq_along(lm_list), function(i) {
geom_point(shape = 3, color = "gray70") + r_squared <- round(lm_summaries[[i]]$r.squared, 3)
geom_smooth(method = "lm", color = "tomato3") + plot_titles <- c("Interaction L vs. Interaction K", "Interaction L vs. Interaction r", "Interaction L vs. Interaction AUC",
ggtitle(plot_titles[i]) + "Interaction K vs. Interaction r", "Interaction K vs. Interaction AUC", "Interaction r vs. Interaction AUC")
annotate("text", x = 0, y = 0, label = paste("R-squared = ", round(lm_summaries[[i]]$r.squared, 3))) +
theme_Publication_legend_right() list(
x_var = names(lm_list)[i][1],
y_var = names(lm_list)[i][2],
plot_type = "scatter",
title = plot_titles[i],
annotations = list(list(x = 0, y = 0, label = paste("R-squared =", r_squared)))
)
}) })
save_plots("Correlation_CPPs", plot_list, output_dir) # 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) {
@@ -570,39 +591,38 @@ generate_plots <- function(df, x_var, y_vars, plot_type, color_var = "conc_num",
} }
# Function to generate rank plots for the provided dataframe # Function to generate rank plots for the provided dataframe
create_ranked_plots <- function(df, output_dir) { # create_ranked_plots <- function(df, output_dir) {
# List of variables for which we need to generate rank plots # # List of variables for which we need to generate rank plots
variables <- c("L", "K", "r", "AUC") # variables <- c("L", "K", "r", "AUC")
# Adjust missing values and calculate ranks # # Adjust missing values and calculate ranks
df_adjusted <- adjust_missing_and_rank(df, variables) # df_adjusted <- adjust_missing_and_rank(df, variables)
# Generate rank plots for Avg_Zscore and Z_lm # # Generate rank plots
for (var in variables) { # for (var in variables) {
plot_rank_avg <- generate_plot( # plot_rank_avg <- generate_plot(
df = df_adjusted, # df = df_adjusted,
x_var = paste0(var, "_Rank"), # x_var = paste0(var, "_Rank"),
y_var = paste0("Avg_Zscore_", var), # y_var = paste0("Avg_Zscore_", var),
plot_type = "scatter", # plot_type = "scatter",
title = paste("Average Z score vs Rank for", var) # title = paste("Average Z score vs Rank for", var)
) # )
plot_rank_lm <- generate_plot( # plot_rank_lm <- generate_plot(
df = df_adjusted, # df = df_adjusted,
x_var = paste0(var, "_Rank_lm"), # x_var = paste0(var, "_Rank_lm"),
y_var = paste0("Z_lm_", var), # y_var = paste0("Z_lm_", var),
plot_type = "scatter", # plot_type = "scatter",
title = paste("Interaction Z score vs Rank for", var) # title = paste("Interaction Z score vs Rank for", var)
) # )
# Save the plots for Avg_Zscore and Z_lm # save_plots(paste0("RankPlots_", var), list(
save_plots(paste0("RankPlots_", var), list( # plot_rank_avg = plot_rank_avg,
plot_rank_avg = plot_rank_avg, # plot_rank_lm = plot_rank_lm
plot_rank_lm = plot_rank_lm # ), output_dir)
), output_dir) # }
} # }
}
main <- function() { main <- function() {
lapply(names(args$experiments), function(exp_name) { lapply(names(args$experiments), function(exp_name) {
@@ -622,8 +642,6 @@ main <- function() {
# QC steps and filtering # QC steps and filtering
df_above_tolerance <- df %>% filter(DB == 1) df_above_tolerance <- df %>% filter(DB == 1)
df_na <- df %>% mutate(across(c(L, r, AUC, K), ~ ifelse(DB == 1, NA, .)))
df_no_zeros <- df_na %>% filter(L > 0)
# Calculate the half-medians for `L` and `K` for rows above tolerance # Calculate the half-medians for `L` and `K` for rows above tolerance
L_half_median <- (median(df_above_tolerance$L, na.rm = TRUE)) / 2 L_half_median <- (median(df_above_tolerance$L, na.rm = TRUE)) / 2
@@ -632,7 +650,24 @@ main <- function() {
# Get the number of rows that are above tolerance # Get the number of rows that are above tolerance
rows_to_remove <- nrow(df_above_tolerance) rows_to_remove <- nrow(df_above_tolerance)
# Additional filtering for non-finite values in df_na # Set L, r, K, and AUC to NA for rows that are above tolerance
df_na <- df %>% mutate(across(c(L, r, AUC, K), ~ ifelse(DB == 1, NA, .)))
# Calculate summary statistics for all strains, including both background and the deletions
message("Calculating summary statistics for all strains")
variables <- c("L", "K", "r", "AUC")
ss <- calculate_summary_stats(df_na, variables, group_vars = c("OrfRep", "conc_num", "conc_num_factor"))
summary_stats <- ss$summary_stats
df_na_stats <- ss$df_with_stats
write.csv(summary_stats, file = file.path(out_dir, "SummaryStats_ALLSTRAINS.csv"), row.names = FALSE)
print("Summary stats:")
print(head(summary_stats), width = 200)
# Remove rows with 0 values in L
df_no_zeros <- df_na %>% filter(L > 0)
# Additional filtering for non-finite values
df_na_filtered <- df_na %>% df_na_filtered <- df_na %>%
filter(if_any(c(L), ~ !is.finite(.))) %>% filter(if_any(c(L), ~ !is.finite(.))) %>%
{ {
@@ -647,7 +682,7 @@ main <- function() {
qc_plot_configs <- list( qc_plot_configs <- list(
list(x_var = "L", y_var = "K", plot_type = "scatter", title = "Raw L vs K before QC", ylim_vals = NULL), list(x_var = "L", y_var = "K", plot_type = "scatter", title = "Raw L vs K before QC", ylim_vals = NULL),
list(x_var = "delta_bg", y_var = NULL, plot_type = "density", title = "Density plot for Delta Background", ylim_vals = NULL), list(x_var = "delta_bg", y_var = NULL, plot_type = "density", title = "Density plot for Delta Background", ylim_vals = NULL),
list(x_var = "delta_bg", y_var = NULL, plot_type = "bar", title = "Bar plot for Delta Background", ylim_vals = NULL) list(x_var = "as.factor(delta_bg)", y_var = NULL, plot_type = "bar", title = "Bar plot for Delta Background", ylim_vals = NULL)
) )
above_tolerance_plot_configs <- list( above_tolerance_plot_configs <- list(
@@ -659,7 +694,16 @@ main <- function() {
) )
no_zeros_plot_configs <- list( no_zeros_plot_configs <- list(
list(x_var = "L", y_var = "K", plot_type = "scatter", title = "L vs K for Non-Zero L Values", ylim_vals = NULL) list(x_var = "scan", y_var = "L", plot_type = "scatter", title = "L vs Scan for Non-Zero L Values"),
list(x_var = "scan", y_var = "K", plot_type = "scatter", title = "K vs Scan for Non-Zero L Values"),
list(x_var = "scan", y_var = "r", plot_type = "scatter", title = "r vs Scan for Non-Zero L Values"),
list(x_var = "scan", y_var = "AUC", plot_type = "scatter", title = "AUC vs Scan for Non-Zero L Values"),
list(x_var = "scan", y_var = "delta_bg", plot_type = "scatter", title = "Delta background vs Scan for Non-Zero L Values"),
list(x_var = "as.factor(scan)", y_var = "L", plot_type = "box", title = "L vs Scan for Non-Zero L Values"),
list(x_var = "as.factor(scan)", y_var = "K", plot_type = "box", title = "K vs Scan for Non-Zero L Values"),
list(x_var = "as.factor(scan)", y_var = "r", plot_type = "box", title = "r vs Scan for Non-Zero L Values"),
list(x_var = "as.factor(scan)", y_var = "AUC", plot_type = "box", title = "AUC vs Scan for Non-Zero L Values"),
list(x_var = "as.factor(scan)", y_var = "delta_bg", plot_type = "box", title = "Delta background vs Scan for Non-Zero L Values")
) )
# Generate and save plots for each QC step # Generate and save plots for each QC step
@@ -672,17 +716,6 @@ main <- function() {
# Clean up # Clean up
rm(df, df_above_tolerance, df_no_zeros) rm(df, df_above_tolerance, df_no_zeros)
# Calculate summary statistics
message("Calculating summary statistics for all strains")
variables <- c("L", "K", "r", "AUC")
ss <- calculate_summary_stats(df_na, variables, group_vars = c("OrfRep", "conc_num", "conc_num_factor"))
summary_stats <- ss$summary_stats
df_na_stats <- ss$df_with_stats
write.csv(summary_stats, file = file.path(out_dir, "SummaryStats_ALLSTRAINS.csv"), row.names = FALSE)
print("Summary stats:")
print(head(summary_stats), width = 200)
# TODO: Originally this filtered L NA's # TODO: Originally this filtered L NA's
# Let's try to avoid for now since stats have already been calculated # Let's try to avoid for now since stats have already been calculated
@@ -695,6 +728,7 @@ main <- function() {
# Summary statistics for within and outside 2SD of K # Summary statistics for within and outside 2SD of K
message("Calculating summary statistics for L within 2SD of K") message("Calculating summary statistics for L within 2SD of K")
# TODO We're omitting the original z_max calculation, not sure if needed?
ss <- calculate_summary_stats(df_na_within_2sd_k, "L", group_vars = c("conc_num", "conc_num_factor")) ss <- calculate_summary_stats(df_na_within_2sd_k, "L", group_vars = c("conc_num", "conc_num_factor"))
l_within_2sd_k_stats <- ss$summary_stats l_within_2sd_k_stats <- ss$summary_stats
df_na_l_within_2sd_k_stats <- ss$df_with_stats df_na_l_within_2sd_k_stats <- ss$df_with_stats
@@ -844,8 +878,7 @@ main <- function() {
zscores_interactions_filtered <- zscores_interactions %>% zscores_interactions_filtered <- zscores_interactions %>%
filter(!is.na(Z_lm_L) | !is.na(Avg_Zscore_L)) filter(!is.na(Z_lm_L) | !is.na(Avg_Zscore_L))
# Generate summary and correlation plots
generate_summary_plots(df, out_dir)
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),
@@ -856,10 +889,37 @@ main <- function() {
) )
# Generate cpp correlation plots # Generate cpp correlation plots
generate_cpp_correlation_plots(zscores_interactions_filtered, lm_list, out_dir) correlation_plot_config <- list(
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 cpp correlation plots
#generate_cpp_correlation_plots(zscores_interactions_filtered, lm_list, out_dir)
# Generate ranked plots # Generate ranked plots
create_ranked_plots(zscores_interactions_filtered, out_dir) 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 = "K_Rank", y_var = "Avg_Zscore_K", plot_type = "scatter", title = "Rank vs Avg Z score for K"),
list(x_var = "r_Rank", y_var = "Avg_Zscore_r", plot_type = "scatter", title = "Rank vs Avg Z score for r"),
list(x_var = "AUC_Rank", y_var = "Avg_Zscore_AUC", plot_type = "scatter", title = "Rank vs Avg Z score for AUC")
)
# Generate and save rank plots using the existing plotting framework
generate_and_save_plots(zscores_interactions_filtered, output_dir, "RankPlots", rank_plot_config)
}) })
}) })
} }