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Try separating interaction dfs

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This commit is contained in:
Bryan Roessler
2024-10-03 12:43:03 -04:00
parent f6958a0126
commit 4b273db78d
1 changed files with 207 additions and 201 deletions
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282
qhtcp-workflow/apps/r/calculate_interaction_zscores.R
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@@ -160,10 +160,6 @@ load_and_filter_data <- function(easy_results_file, sd = 3) {
conc_num_factor_factor = as.factor(conc_num)
)
# Set the max concentration across the whole dataframe
df <- df %>%
mutate(max_conc = max(df$conc_num_factor, na.rm = TRUE))
return(df)
}
@@ -215,7 +211,9 @@ calculate_summary_stats <- function(df, variables, group_vars) {
return(list(summary_stats = summary_stats, df_with_stats = df_joined))
}
calculate_interaction_scores <- function(df, df_bg, group_vars, max_conc, overlap_threshold = 2) {
calculate_interaction_scores <- function(df, df_bg, group_vars) {
max_conc <- max(as.numeric(df$conc_num_factor), na.rm = TRUE)
# Include background statistics per concentration
bg_stats <- df_bg %>%
@@ -382,45 +380,16 @@ calculate_interaction_scores <- function(df, df_bg, group_vars, max_conc, overla
R_Squared_r = first(R_Squared_r),
R_Squared_AUC = first(R_Squared_AUC),
# Include Drug
Drug = first(Drug),
.groups = "drop"
)
# Create the final calculations and interactions dataframes with required columns
calculations_df <- calculations %>%
select(
all_of(group_vars),
conc_num, conc_num_factor, conc_num_factor_factor,
N, NG, DB, SM,
mean_L, mean_K, mean_r, mean_AUC,
Raw_Shift_L, Raw_Shift_K, Raw_Shift_r, Raw_Shift_AUC,
Z_Shift_L, Z_Shift_K, Z_Shift_r, Z_Shift_AUC,
WT_L, WT_K, WT_r, WT_AUC,
WT_sd_L, WT_sd_K, WT_sd_r, WT_sd_AUC,
Exp_L, Exp_K, Exp_r, Exp_AUC,
Delta_L, Delta_K, Delta_r, Delta_AUC,
Zscore_L, Zscore_K, Zscore_r, Zscore_AUC
)
interactions_df <- interactions %>%
select(
all_of(group_vars),
NG, DB, SM,
Avg_Zscore_L, Avg_Zscore_K, Avg_Zscore_r, Avg_Zscore_AUC,
Z_lm_L, Z_lm_K, Z_lm_r, Z_lm_AUC,
Raw_Shift_L, Raw_Shift_K, Raw_Shift_r, Raw_Shift_AUC,
Z_Shift_L, Z_Shift_K, Z_Shift_r, Z_Shift_AUC,
R_Squared_L, R_Squared_K, R_Squared_r, R_Squared_AUC
)
# Create full_data by joining calculations_df and interactions_df
full_data <- calculations_df %>%
left_join(interactions_df, by = group_vars, suffix = c("", "_interaction"))
# Return the dataframes
# Return the dataframes without creating full_data
return(list(
calculations = calculations_df,
interactions = interactions_df,
full_data = full_data
calculations = calculations,
interactions = interactions
))
}
@@ -491,40 +460,29 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs) {
"red"
}
y_mean_prefix <- if (!is.null(config$error_bar_params$y_mean_prefix)) {
config$error_bar_params$y_mean_prefix
} else {
"mean_"
}
y_mean_col <- paste0(y_mean_prefix, config$y_var)
# Dynamically set y_sd_col based on the provided prefix in error_bar_params
y_sd_prefix <- if (!is.null(config$error_bar_params$y_sd_prefix)) {
config$error_bar_params$y_sd_prefix
} else {
"sd_"
}
y_sd_col <- paste0(y_sd_prefix, config$y_var)
if (!is.null(config$error_bar_params$center_point)) {
plot <- plot + geom_point(aes(
x = .data[[config$x_var]],
y = first(.data[[y_mean_col]])),
color = error_bar_color,
shape = 16)
}
# Use error_bar_params if provided, otherwise calculate from mean and sd
if (!is.null(config$error_bar_params$ymin) && !is.null(config$error_bar_params$ymax)) {
plot <- plot + geom_errorbar(aes(
# Check if ymin and ymax are constants or column names
if (is.numeric(config$error_bar_params$ymin) && is.numeric(config$error_bar_params$ymax)) {
plot <- plot + geom_errorbar(aes(x = .data[[config$x_var]]),
ymin = config$error_bar_params$ymin,
ymax = config$error_bar_params$ymax),
ymax = config$error_bar_params$ymax,
color = error_bar_color)
} else {
plot <- plot + geom_errorbar(aes(
ymin = first(.data[[y_mean_col]]) - first(.data[[y_sd_col]]),
ymax = first(.data[[y_mean_col]]) + first(.data[[y_sd_col]])),
color = error_bar_color)
x = .data[[config$x_var]],
ymin = .data[[config$error_bar_params$ymin]],
ymax = .data[[config$error_bar_params$ymax]]
), color = error_bar_color)
}
} else {
# Original code for calculating from mean and sd
y_mean_col <- paste0("mean_", config$y_var)
y_sd_col <- paste0("sd_", config$y_var)
plot <- plot + geom_errorbar(aes(
x = .data[[config$x_var]],
ymin = .data[[y_mean_col]] - .data[[y_sd_col]],
ymax = .data[[y_mean_col]] + .data[[y_sd_col]]
), color = error_bar_color)
}
}
@@ -756,7 +714,7 @@ generate_plate_analysis_plot_configs <- function(variables, df_before = NULL, df
return(list(plots = plot_configs))
}
generate_interaction_plot_configs <- function(df, type) {
generate_interaction_plot_configs <- function(df, df_calculations, df_interactions, type) {
# Define the y-limits for the plots
limits_map <- list(
@@ -770,7 +728,7 @@ generate_interaction_plot_configs <- function(df, type) {
stats_boxplot_configs <- list()
delta_plot_configs <- list()
# Overall statistics plots
# Overall statistics plots (use df)
OrfRep <- first(df$OrfRep) # this should correspond to the reference strain
for (plot_type in c("scatter", "box")) {
@@ -814,9 +772,9 @@ generate_interaction_plot_configs <- function(df, type) {
for (x_val in unique(df$conc_num_factor_factor)) {
current_df <- df %>% filter(.data[[plot_config$x_var]] == x_val)
annotations <- append(annotations, list(
list(x = x_val, y = y_limits[1] + 0.1 * y_span, label = first(current_df$NG, default = 0)),
list(x = x_val, y = y_limits[1] + 0.05 * y_span, label = first(current_df$DB, default = 0)),
list(x = x_val, y = y_limits[1], label = first(current_df$SM, default = 0))
list(x = x_val, y = y_limits[1] + 0.1 * y_span, label = sum(current_df$NG, na.rm = TRUE)),
list(x = x_val, y = y_limits[1] + 0.05 * y_span, label = sum(current_df$DB, na.rm = TRUE)),
list(x = x_val, y = y_limits[1], label = sum(current_df$SM, na.rm = TRUE))
))
}
@@ -836,7 +794,7 @@ generate_interaction_plot_configs <- function(df, type) {
}
}
# Delta interaction plots
# Delta interaction plots (use df_calculations and df_interactions)
if (type == "reference") {
group_vars <- c("OrfRep", "Gene", "num")
} else if (type == "deletion") {
@@ -850,7 +808,7 @@ generate_interaction_plot_configs <- function(df, type) {
AUC = c(-6000, 6000)
)
grouped_data <- df %>%
grouped_data <- df_calculations %>%
group_by(across(all_of(group_vars))) %>%
group_split()
@@ -865,6 +823,11 @@ generate_interaction_plot_configs <- function(df, type) {
OrfRepTitle <- OrfRep
}
# Get corresponding interaction row
interaction_row <- df_interactions %>%
filter(if_all(all_of(group_vars), ~ . == first(.))) %>%
slice(1)
for (var in names(delta_limits_map)) {
y_limits <- delta_limits_map[[var]]
y_span <- y_limits[2] - y_limits[1]
@@ -872,17 +835,17 @@ generate_interaction_plot_configs <- function(df, type) {
# Error bars
WT_sd_value <- first(group_data[[paste0("WT_sd_", var)]], default = 0)
# Z_Shift and lm values
Z_Shift_value <- round(first(group_data[[paste0("Z_Shift_", var)]], default = 0), 2)
Z_lm_value <- round(first(group_data[[paste0("Z_lm_", var)]], default = 0), 2)
R_squared_value <- round(first(group_data[[paste0("R_Squared_", var)]], default = 0), 2)
# Z_Shift and lm values from interaction_row
Z_Shift_value <- round(first(interaction_row[[paste0("Z_Shift_", var)]], default = 0), 2)
Z_lm_value <- round(first(interaction_row[[paste0("Z_lm_", var)]], default = 0), 2)
R_squared_value <- round(first(interaction_row[[paste0("R_Squared_", var)]], default = 0), 2)
# NG, DB, SM values
NG_value <- first(group_data$NG, default = 0)
DB_value <- first(group_data$DB, default = 0)
SM_value <- first(group_data$SM, default = 0)
# NG, DB, SM values from interaction_row
NG_value <- first(interaction_row$NG, default = 0)
DB_value <- first(interaction_row$DB, default = 0)
SM_value <- first(interaction_row$SM, default = 0)
# Use the pre-calculated lm intercept and slope from the dataframe
# Use the pre-calculated lm intercept and slope from group_data
lm_intercept_col <- paste0("lm_intercept_", var)
lm_slope_col <- paste0("lm_slope_", var)
lm_intercept_value <- first(group_data[[lm_intercept_col]], default = 0)
@@ -906,8 +869,9 @@ generate_interaction_plot_configs <- function(df, type) {
),
error_bar = TRUE,
error_bar_params = list(
ymin = 0 - (2 * WT_sd_value),
ymax = 0 + (2 * WT_sd_value),
# Passing constants directly
ymin = -2 * WT_sd_value,
ymax = 2 * WT_sd_value,
color = "black"
),
smooth = TRUE,
@@ -931,34 +895,36 @@ generate_interaction_plot_configs <- function(df, type) {
return(list(
list(grid_layout = list(ncol = 2, nrow = 2), plots = stats_plot_configs),
list(grid_layout = list(ncol = 2, nrow = 2), plots = stats_boxplot_configs),
list(grid_layout = list(ncol = 4, nrow = grid_nrow), plots = delta_plot_configs)
list(grid_layout = list(ncol = grid_ncol, nrow = grid_nrow), plots = delta_plot_configs)
))
}
generate_rank_plot_configs <- function(df, variables, is_lm = FALSE, adjust = FALSE, overlap_color = FALSE) {
sd_bands <- c(1, 2, 3)
plot_configs <- list()
generate_rank_plot_configs <- function(df_interactions, is_lm = FALSE, adjust = FALSE, overlap_color = FALSE) {
variables <- c("L", "K")
plot_configs <- list()
sd_bands <- c(1, 2, 3)
# Adjust (if necessary) and rank columns
variables <- c("L", "K")
for (variable in variables) {
if (adjust) {
df[[paste0("Avg_Zscore_", variable)]] <- ifelse(is.na(df[[paste0("Avg_Zscore_", variable)]]), 0.001, df[[paste0("Avg_Zscore_", variable)]])
df[[paste0("Z_lm_", variable)]] <- ifelse(is.na(df[[paste0("Z_lm_", variable)]]), 0.001, df[[paste0("Z_lm_", variable)]])
df_interactions[[paste0("Avg_Zscore_", variable)]] <-
ifelse(is.na(df_interactions[[paste0("Avg_Zscore_", variable)]]), 0.001, df_interactions[[paste0("Avg_Zscore_", variable)]])
df_interactions[[paste0("Z_lm_", variable)]] <-
ifelse(is.na(df_interactions[[paste0("Z_lm_", variable)]]), 0.001, df_interactions[[paste0("Z_lm_", variable)]])
}
df[[paste0("Rank_", variable)]] <- rank(df[[paste0("Avg_Zscore_", variable)]], na.last = "keep")
df[[paste0("Rank_lm_", variable)]] <- rank(df[[paste0("Z_lm_", variable)]], na.last = "keep")
df_interactions[[paste0("Rank_", variable)]] <- rank(df_interactions[[paste0("Avg_Zscore_", variable)]], na.last = "keep")
df_interactions[[paste0("Rank_lm_", variable)]] <- rank(df_interactions[[paste0("Z_lm_", variable)]], na.last = "keep")
}
# Helper function to create a plot configuration
create_plot_config <- function(variable, rank_var, zscore_var, y_label, sd_band, with_annotations = TRUE) {
num_enhancers <- sum(df[[zscore_var]] >= sd_band, na.rm = TRUE)
num_suppressors <- sum(df[[zscore_var]] <= -sd_band, na.rm = TRUE)
num_enhancers <- sum(df_interactions[[zscore_var]] >= sd_band, na.rm = TRUE)
num_suppressors <- sum(df_interactions[[zscore_var]] <= -sd_band, na.rm = TRUE)
# Default plot config
plot_config <- list(
df = df,
df = df_interactions,
x_var = rank_var,
y_var = zscore_var,
plot_type = "scatter",
@@ -980,13 +946,13 @@ generate_rank_plot_configs <- function(df, variables, is_lm = FALSE, adjust = FA
# Add specific annotations for plots with annotations
plot_config$annotations <- list(
list(
x = median(df[[rank_var]], na.rm = TRUE),
y = max(df[[zscore_var]], na.rm = TRUE) * 0.9,
x = median(df_interactions[[rank_var]], na.rm = TRUE),
y = max(df_interactions[[zscore_var]], na.rm = TRUE) * 0.9,
label = paste("Deletion Enhancers =", num_enhancers)
),
list(
x = median(df[[rank_var]], na.rm = TRUE),
y = min(df[[zscore_var]], na.rm = TRUE) * 0.9,
x = median(df_interactions[[rank_var]], na.rm = TRUE),
y = min(df_interactions[[zscore_var]], na.rm = TRUE) * 0.9,
label = paste("Deletion Suppressors =", num_suppressors)
)
)
@@ -1019,7 +985,7 @@ generate_rank_plot_configs <- function(df, variables, is_lm = FALSE, adjust = FA
return(list(grid_layout = list(ncol = grid_ncol, nrow = grid_nrow), plots = plot_configs))
}
generate_correlation_plot_configs <- function(df, correlation_stats) {
generate_correlation_plot_configs <- function(df_interactions) {
# Define relationships for different-variable correlations
relationships <- list(
list(x = "L", y = "K"),
@@ -1030,6 +996,23 @@ generate_correlation_plot_configs <- function(df, correlation_stats) {
list(x = "r", y = "AUC")
)
correlation_stats <- list()
for (rel in relationships) {
x_var <- paste0("Z_lm_", rel$x)
y_var <- paste0("Z_lm_", rel$y)
lm_fit <- lm(df_interactions[[y_var]] ~ df_interactions[[x_var]])
intercept <- coef(lm_fit)[1]
slope <- coef(lm_fit)[2]
r_squared <- summary(lm_fit)$r.squared
relationship_name <- paste0(rel$x, "_vs_", rel$y)
correlation_stats[[relationship_name]] <- list(
intercept = intercept,
slope = slope,
r_squared = r_squared
)
}
plot_configs <- list()
# Iterate over the option to highlight cyan points (TRUE/FALSE)
@@ -1053,15 +1036,15 @@ generate_correlation_plot_configs <- function(df, correlation_stats) {
# Construct plot config
plot_config <- list(
df = df,
df = df_interactions,
x_var = x_var,
y_var = y_var,
plot_type = "scatter",
title = plot_label,
annotations = list(
list(
x = mean(df[[x_var]], na.rm = TRUE),
y = mean(df[[y_var]], na.rm = TRUE),
x = mean(df_interactions[[x_var]], na.rm = TRUE),
y = mean(df_interactions[[y_var]], na.rm = TRUE),
label = paste("R-squared =", round(r_squared, 3))
)
),
@@ -1371,9 +1354,8 @@ main <- function() {
group_vars = c("OrfRep", "Gene", "num", "conc_num")
)$df_with_stats
reference_results <- calculate_interaction_scores(df_reference_stats, df_bg_stats, group_vars = c("OrfRep", "Gene", "num"))
zscore_calculations_reference <- reference_results$calculations
zscore_interactions_reference <- reference_results$interactions
zscore_interactions_reference_joined <- reference_results$full_data
df_calculations_reference <- reference_results$calculations
df_interactions_reference <- reference_results$interactions
message("Setting missing deletion values to the highest theoretical value at each drug conc for L")
df_deletion <- df_na_stats %>% # formerly X2
@@ -1394,38 +1376,39 @@ main <- function() {
group_vars = c("OrfRep", "Gene", "conc_num")
)$df_with_stats
deletion_results <- calculate_interaction_scores(df_deletion_stats, df_bg_stats, group_vars = c("OrfRep", "Gene"))
zscore_calculations <- deletion_results$calculations
zscore_interactions <- deletion_results$interactions
zscore_interactions_joined <- deletion_results$full_data
df_calculations <- deletion_results$calculations
df_interactions <- deletion_results$interactions
# Writing Z-Scores to file
write.csv(zscore_calculations_reference, file = file.path(out_dir, "zscore_calculations_reference.csv"), row.names = FALSE)
write.csv(zscore_calculations, file = file.path(out_dir, "zscore_calculations.csv"), row.names = FALSE)
write.csv(zscore_interactions_reference, file = file.path(out_dir, "zscore_interactions_reference.csv"), row.names = FALSE)
write.csv(zscore_interactions, file = file.path(out_dir, "zscore_interactions.csv"), row.names = FALSE)
write.csv(df_calculations_reference, file = file.path(out_dir, "zscore_calculations_reference.csv"), row.names = FALSE)
write.csv(df_interactions_reference, file = file.path(out_dir, "zscore_interactions_reference.csv"), row.names = FALSE)
write.csv(df_calculations, file = file.path(out_dir, "zscore_calculations.csv"), row.names = FALSE)
write.csv(df_interactions, file = file.path(out_dir, "zscore_interactions.csv"), row.names = FALSE)
# Create interaction plots
message("Generating reference interaction plots")
reference_plot_configs <- generate_interaction_plot_configs(zscore_interactions_reference_joined, "reference")
reference_plot_configs <- generate_interaction_plot_configs(
df_reference_stats, df_calculations_reference, df_interactions_reference, "reference")
generate_and_save_plots(out_dir, "interaction_plots_reference", reference_plot_configs)
message("Generating deletion interaction plots")
deletion_plot_configs <- generate_interaction_plot_configs(zscore_interactions_joined, "deletion")
deletion_plot_configs <- generate_interaction_plot_configs(
df_deletion_stats, df_calculations, df_interactions, "deletion")
generate_and_save_plots(out_dir, "interaction_plots", deletion_plot_configs)
# Define conditions for enhancers and suppressors
# TODO Add to study config?
threshold <- 2
enhancer_condition_L <- zscore_interactions$Avg_Zscore_L >= threshold
suppressor_condition_L <- zscore_interactions$Avg_Zscore_L <= -threshold
enhancer_condition_K <- zscore_interactions$Avg_Zscore_K >= threshold
suppressor_condition_K <- zscore_interactions$Avg_Zscore_K <= -threshold
enhancer_condition_L <- df_interactions$Avg_Zscore_L >= threshold
suppressor_condition_L <- df_interactions$Avg_Zscore_L <= -threshold
enhancer_condition_K <- df_interactions$Avg_Zscore_K >= threshold
suppressor_condition_K <- df_interactions$Avg_Zscore_K <= -threshold
# Subset data
enhancers_L <- zscore_interactions[enhancer_condition_L, ]
suppressors_L <- zscore_interactions[suppressor_condition_L, ]
enhancers_K <- zscore_interactions[enhancer_condition_K, ]
suppressors_K <- zscore_interactions[suppressor_condition_K, ]
enhancers_L <- df_interactions[enhancer_condition_L, ]
suppressors_L <- df_interactions[suppressor_condition_L, ]
enhancers_K <- df_interactions[enhancer_condition_K, ]
suppressors_K <- df_interactions[suppressor_condition_K, ]
# Save enhancers and suppressors
message("Writing enhancer/suppressor csv files")
@@ -1435,8 +1418,8 @@ main <- function() {
write.csv(suppressors_K, file = file.path(out_dir, "zscore_interactions_deletion_suppressors_K.csv"), row.names = FALSE)
# Combine conditions for enhancers and suppressors
enhancers_and_suppressors_L <- zscore_interactions[enhancer_condition_L | suppressor_condition_L, ]
enhancers_and_suppressors_K <- zscore_interactions[enhancer_condition_K | suppressor_condition_K, ]
enhancers_and_suppressors_L <- df_interactions[enhancer_condition_L | suppressor_condition_L, ]
enhancers_and_suppressors_K <- df_interactions[enhancer_condition_K | suppressor_condition_K, ]
# Save combined enhancers and suppressors
write.csv(enhancers_and_suppressors_L,
@@ -1446,10 +1429,10 @@ main <- function() {
# Handle linear model based enhancers and suppressors
lm_threshold <- 2 # TODO add to study config?
enhancers_lm_L <- zscore_interactions[zscore_interactions$Z_lm_L >= lm_threshold, ]
suppressors_lm_L <- zscore_interactions[zscore_interactions$Z_lm_L <= -lm_threshold, ]
enhancers_lm_K <- zscore_interactions[zscore_interactions$Z_lm_K >= lm_threshold, ]
suppressors_lm_K <- zscore_interactions[zscore_interactions$Z_lm_K <= -lm_threshold, ]
enhancers_lm_L <- df_interactions[df_interactions$Z_lm_L >= lm_threshold, ]
suppressors_lm_L <- df_interactions[df_interactions$Z_lm_L <= -lm_threshold, ]
enhancers_lm_K <- df_interactions[df_interactions$Z_lm_K >= lm_threshold, ]
suppressors_lm_K <- df_interactions[df_interactions$Z_lm_K <= -lm_threshold, ]
# Save linear model based enhancers and suppressors
message("Writing linear model enhancer/suppressor csv files")
@@ -1464,7 +1447,7 @@ main <- function() {
message("Generating rank plots")
rank_plot_configs <- generate_rank_plot_configs(
df = zscore_interactions_joined,
df_interactions,
is_lm = FALSE,
adjust = TRUE
)
@@ -1473,16 +1456,37 @@ main <- function() {
message("Generating ranked linear model plots")
rank_lm_plot_configs <- generate_rank_plot_configs(
df = zscore_interactions_joined,
df_interactions,
is_lm = TRUE,
adjust = TRUE
)
generate_and_save_plots(out_dir = out_dir, filename = "rank_plots_lm",
plot_configs = rank_lm_plot_configs)
overlap_threshold <- 2
df_interactions_filtered <- df_interactions %>%
filter(!is.na(Z_lm_L) & !is.na(Avg_Zscore_L)) %>%
mutate(
Overlap = case_when(
Z_lm_L >= overlap_threshold & Avg_Zscore_L >= overlap_threshold ~ "Deletion Enhancer Both",
Z_lm_L <= -overlap_threshold & Avg_Zscore_L <= -overlap_threshold ~ "Deletion Suppressor Both",
Z_lm_L >= overlap_threshold & Avg_Zscore_L <= overlap_threshold ~ "Deletion Enhancer lm only",
Z_lm_L <= overlap_threshold & Avg_Zscore_L >= overlap_threshold ~ "Deletion Enhancer Avg Zscore only",
Z_lm_L <= -overlap_threshold & Avg_Zscore_L >= -overlap_threshold ~ "Deletion Suppressor lm only",
Z_lm_L >= -overlap_threshold & Avg_Zscore_L <= -overlap_threshold ~ "Deletion Suppressor Avg Zscore only",
Z_lm_L >= overlap_threshold & Avg_Zscore_L <= -overlap_threshold ~ "Deletion Enhancer lm, Deletion Suppressor Avg Z score",
Z_lm_L <= -overlap_threshold & Avg_Zscore_L >= overlap_threshold ~ "Deletion Suppressor lm, Deletion Enhancer Avg Z score",
TRUE ~ "No Effect"
),
lm_R_squared_L = summary(lm(Z_lm_L ~ Avg_Zscore_L))$r.squared,
lm_R_squared_K = summary(lm(Z_lm_K ~ Avg_Zscore_K))$r.squared,
lm_R_squared_r = summary(lm(Z_lm_r ~ Avg_Zscore_r))$r.squared,
lm_R_squared_AUC = summary(lm(Z_lm_AUC ~ Avg_Zscore_AUC))$r.squared
)
message("Generating filtered ranked plots")
rank_plot_filtered_configs <- generate_rank_plot_configs(
df = zscore_interactions_filtered,
df_interactions_filtered,
is_lm = FALSE,
adjust = FALSE,
overlap_color = TRUE
@@ -1494,7 +1498,7 @@ main <- function() {
message("Generating filtered ranked linear model plots")
rank_plot_lm_filtered_configs <- generate_rank_plot_configs(
df = zscore_interactions_filtered,
df_interactions_filtered,
is_lm = TRUE,
adjust = FALSE,
overlap_color = TRUE
@@ -1505,7 +1509,9 @@ main <- function() {
plot_configs = rank_plot_lm_filtered_configs)
message("Generating correlation curve parameter pair plots")
correlation_plot_configs <- generate_correlation_plot_configs(zscore_interactions_filtered)
correlation_plot_configs <- generate_correlation_plot_configs(
df_interactions_filtered
)
generate_and_save_plots(
out_dir = out_dir,
filename = "correlation_cpps",