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Refactor interaction plots to handle both reference and deletion scoring

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This commit is contained in:
Bryan Roessler
2024-09-29 17:39:17 -04:00
parent f005155d08
commit c18f70a08a
1 changed files with 203 additions and 128 deletions
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323
qhtcp-workflow/apps/r/calculate_interaction_zscores.R
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@@ -150,8 +150,9 @@ load_and_filter_data <- function(easy_results_file, sd = 3) {
SM = 0,
OrfRep = if_else(ORF == "YDL227C", "YDL227C", OrfRep), # should these be hardcoded?
conc_num = as.numeric(gsub("[^0-9\\.]", "", Conc)),
conc_num_factor = factor(as.numeric(factor(conc_num)) - 1),
conc_num_factor_num = as.numeric(conc_num_factor)
conc_num_factor_new = factor(conc_num),
conc_num_factor_zeroed = factor(as.numeric(conc_num_factor2) - 1),
conc_num_factor = as.numeric(conc_num_factor_zeroed) # for legacy purposes, neither conc_num nor a factor
)
return(df)
@@ -250,10 +251,10 @@ calculate_interaction_scores <- function(df, max_conc, bg_stats,
Zscore_AUC = Delta_AUC / WT_sd_AUC,
# Fit linear models and store in list-columns
gene_lm_L = list(lm(Delta_L ~ conc_num_factor_num, data = pick(everything()))),
gene_lm_K = list(lm(Delta_K ~ conc_num_factor_num, data = pick(everything()))),
gene_lm_r = list(lm(Delta_r ~ conc_num_factor_num, data = pick(everything()))),
gene_lm_AUC = list(lm(Delta_AUC ~ conc_num_factor_num, data = pick(everything()))),
gene_lm_L = list(lm(Delta_L ~ conc_num_factor_zeroed_num, data = pick(everything()))),
gene_lm_K = list(lm(Delta_K ~ conc_num_factor_zeroed_num, data = pick(everything()))),
gene_lm_r = list(lm(Delta_r ~ conc_num_factor_zeroed_num, data = pick(everything()))),
gene_lm_AUC = list(lm(Delta_AUC ~ conc_num_factor_zeroed_num, data = pick(everything()))),
# Extract coefficients using purrr::map_dbl
lm_intercept_L = map_dbl(gene_lm_L, ~ coef(.x)[1]),
@@ -293,12 +294,12 @@ calculate_interaction_scores <- function(df, max_conc, bg_stats,
)
calculations <- calculations %>%
mutate(
Z_lm_L = (lm_Score_L - lm_means_sds$lm_mean_L) / lm_means_sds$lm_sd_L,
Z_lm_K = (lm_Score_K - lm_means_sds$lm_mean_K) / lm_means_sds$lm_sd_K,
Z_lm_r = (lm_Score_r - lm_means_sds$lm_mean_r) / lm_means_sds$lm_sd_r,
Z_lm_AUC = (lm_Score_AUC - lm_means_sds$lm_mean_AUC) / lm_means_sds$lm_sd_AUC
)
mutate(
Z_lm_L = (lm_Score_L - lm_means_sds$lm_mean_L) / lm_means_sds$lm_sd_L,
Z_lm_K = (lm_Score_K - lm_means_sds$lm_mean_K) / lm_means_sds$lm_sd_K,
Z_lm_r = (lm_Score_r - lm_means_sds$lm_mean_r) / lm_means_sds$lm_sd_r,
Z_lm_AUC = (lm_Score_AUC - lm_means_sds$lm_mean_AUC) / lm_means_sds$lm_sd_AUC
)
# Summarize some of the stats
interactions <- calculations %>%
@@ -346,7 +347,8 @@ calculate_interaction_scores <- function(df, max_conc, bg_stats,
"Exp_L", "Exp_K", "Exp_r", "Exp_AUC",
"Delta_L", "Delta_K", "Delta_r", "Delta_AUC",
"Zscore_L", "Zscore_K", "Zscore_r", "Zscore_AUC",
"NG", "SM", "DB")
"NG", "SM", "DB"
)
interactions <- interactions %>%
select(
@@ -357,30 +359,49 @@ calculate_interaction_scores <- function(df, max_conc, bg_stats,
"Avg_Zscore_L", "Avg_Zscore_K", "Avg_Zscore_r", "Avg_Zscore_AUC",
"lm_Score_L", "lm_Score_K", "lm_Score_r", "lm_Score_AUC",
"R_Squared_L", "R_Squared_K", "R_Squared_r", "R_Squared_AUC",
"Z_lm_L", "Z_lm_K", "Z_lm_r", "Z_lm_AUC")
"Z_lm_L", "Z_lm_K", "Z_lm_r", "Z_lm_AUC"
)
# Clean the original dataframe by removing overlapping columns
cleaned_df <- df %>%
select(-any_of(
setdiff(intersect(names(df), names(interactions)),
setdiff(intersect(names(df), names(calculations)),
c("OrfRep", "Gene", "num", "conc_num", "conc_num_factor"))))
interactions_joined <- left_join(cleaned_df, interactions, by = c("OrfRep", "Gene", "num", "conc_num", "conc_num_factor"))
# Join the original dataframe with calculations
df_with_calculations <- left_join(cleaned_df, calculations, by = c("OrfRep", "Gene", "num", "conc_num", "conc_num_factor"))
# Remove overlapping columns between df_with_calculations and interactions
df_with_calculations_clean <- df_with_calculations %>%
select(-any_of(
setdiff(intersect(names(df_with_calculations), names(interactions)),
c("OrfRep", "Gene", "num", "conc_num", "conc_num_factor"))))
# Join with interactions to create the full dataset
full_data <- left_join(df_with_calculations_clean, interactions, by = c("OrfRep", "Gene", "num", "conc_num", "conc_num_factor"))
return(list(
calculations = calculations,
interactions = interactions,
interactions_joined = interactions_joined))
full_data = full_data
))
}
generate_and_save_plots <- function(out_dir, filename, plot_configs) {
message("Generating ", filename, ".pdf and ", filename, ".html")
# Iterate through the plot_configs (which contain both plots and grid_layout)
for (config_group in plot_configs) {
plot_list <- config_group$plots
grid_nrow <- config_group$grid_layout$nrow
grid_ncol <- config_group$grid_layout$ncol
# Set defaults if nrow or ncol are not provided
if (is.null(grid_nrow) || is.null(grid_ncol)) {
num_plots <- length(plot_list)
grid_nrow <- ifelse(is.null(grid_nrow), 1, grid_nrow)
grid_ncol <- ifelse(is.null(grid_ncol), num_plots, grid_ncol)
}
# Prepare lists to collect static and interactive plots
static_plots <- list()
plotly_plots <- list()
@@ -419,11 +440,11 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs) {
# Use appropriate helper function based on plot type
plot <- switch(config$plot_type,
"scatter" = generate_scatter_plot(plot, config),
"box" = generate_box_plot(plot, config),
"density" = plot + geom_density(),
"bar" = plot + geom_bar(),
plot # default case if no type matches
"scatter" = generate_scatter_plot(plot, config),
"box" = generate_box_plot(plot, config),
"density" = plot + geom_density(),
"bar" = plot + geom_bar(),
plot # default case if no type matches
)
# Add title and labels
@@ -462,9 +483,9 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs) {
# Convert to plotly object and suppress warnings here
plotly_plot <- suppressWarnings({
if (length(tooltip_vars) > 0) {
ggplotly(plot, tooltip = tooltip_vars)
plotly::ggplotly(plot, tooltip = tooltip_vars)
} else {
ggplotly(plot, tooltip = "none")
plotly::ggplotly(plot, tooltip = "none")
}
})
@@ -483,8 +504,7 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs) {
grid.arrange(grobs = static_plots, ncol = grid_ncol, nrow = grid_nrow)
dev.off()
# Combine and save interactive HTML plot(s)
combined_plot <- subplot(
combined_plot <- plotly::subplot(
plotly_plots,
nrows = grid_nrow,
ncols = grid_ncol,
@@ -492,7 +512,8 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs) {
)
# Save combined HTML plot(s)
saveWidget(combined_plot, file = file.path(out_dir, paste0(filename, ".html")), selfcontained = TRUE)
html_file <- file.path(out_dir, paste0(filename, ".html"))
saveWidget(combined_plot, file = html_file, selfcontained = TRUE)
}
}
@@ -635,8 +656,10 @@ generate_scatter_plot <- function(plot, config) {
}
generate_box_plot <- function(plot, config) {
plot <- plot + geom_boxplot()
# Convert x_var to a factor within aes mapping
plot <- plot + geom_boxplot(aes(x = factor(.data[[config$x_var]])))
# Apply scale_x_discrete for breaks, labels, and axis label if provided
if (!is.null(config$x_breaks) && !is.null(config$x_labels) && !is.null(config$x_label)) {
plot <- plot + scale_x_discrete(
name = config$x_label,
@@ -681,7 +704,7 @@ generate_plate_analysis_plot_configs <- function(variables, stages = c("before",
plot_type = plot_type,
title = paste("Plate analysis by Drug Conc for", var, stage, "quality control"),
error_bar = error_bar,
color_var = "conc_num",
color_var = "conc_num_factor_zeroed",
position = position,
size = 0.2
)
@@ -691,75 +714,127 @@ generate_plate_analysis_plot_configs <- function(variables, stages = c("before",
return(plots)
}
generate_interaction_plot_configs <- function(df, limits_map = NULL, stats_df = NULL) {
generate_interaction_plot_configs <- function(df, limits_map = NULL, plot_type = "reference") {
# Define limits if not provided
if (is.null(limits_map)) {
limits_map <- list(
L = c(0, 130),
K = c(-20, 160),
r = c(0, 1),
AUC = c(0, 12500)
AUC = c(0, 12500),
Delta_L = c(-60, 60),
Delta_K = c(-60, 60),
Delta_r = c(-0.6, 0.6),
Delta_AUC = c(-6000, 6000)
)
}
# Ensure proper grouping by OrfRep, Gene, and num
df_filtered <- df %>%
filter(
!is.na(L) & L >= limits_map$L[1] & L <= limits_map$L[2],
!is.na(K) & K >= limits_map$K[1] & K <= limits_map$K[2],
!is.na(r) & r >= limits_map$r[1] & r <= limits_map$r[2],
!is.na(AUC) & AUC >= limits_map$AUC[1] & AUC <= limits_map$AUC[2]
) %>%
group_by(OrfRep, Gene, num) # Group by OrfRep, Gene, and num
# Define grouping variables and filter data based on plot type
if (plot_type == "reference") {
group_vars <- c("OrfRep", "Gene", "num")
df_filtered <- df %>%
mutate(
OrfRepCombined = paste(OrfRep, Gene, num, sep = "_")
)
} else if (plot_type == "deletion") {
group_vars <- c("OrfRep", "Gene")
df_filtered <- df %>%
mutate(
OrfRepCombined = paste(OrfRep, Gene, sep = "_") # Compare by OrfRep and Gene for deletion
)
}
scatter_configs <- list()
box_configs <- list()
# Create a list to store all configs
configs <- list()
# Generate scatter and box plots for each variable (L, K, r, AUC)
for (var in names(limits_map)) {
scatter_configs[[length(scatter_configs) + 1]] <- list(
# Generate the first 8 scatter/box plots for L, K, r, AUC (shared between reference and deletion)
overall_vars <- c("L", "K", "r", "AUC")
for (var in overall_vars) {
y_limits <- limits_map[[var]]
config <- list(
df = df_filtered,
x_var = "conc_num", # X-axis variable
y_var = var, # Y-axis variable (Delta_L, Delta_K, Delta_r, Delta_AUC)
plot_type = "scatter",
x_var = "conc_num_factor_new",
y_var = var,
x_label = unique(df_filtered$Drug)[1],
title = sprintf("Scatter RF for %s with SD", var),
coord_cartesian = limits_map[[var]], # Set limits for Y-axis
annotations = list(
list(x = -0.25, y = 10, label = "NG"),
list(x = -0.25, y = 5, label = "DB"),
list(x = -0.25, y = 0, label = "SM")
),
grid_layout = list(ncol = 4, nrow = 3)
)
box_configs[[length(box_configs) + 1]] <- list(
df = df_filtered,
x_var = "conc_num", # X-axis variable
y_var = var, # Y-axis variable (Delta_L, Delta_K, Delta_r, Delta_AUC)
plot_type = "box",
title = sprintf("Boxplot RF for %s with SD", var),
coord_cartesian = limits_map[[var]],
grid_layout = list(ncol = 4, nrow = 3)
coord_cartesian = y_limits,
error_bar = TRUE,
x_breaks = unique(df_filtered$conc_num_factor_new),
x_labels = as.character(unique(df_filtered$conc_num)),
grid_layout = list(ncol = 2, nrow = 2)
)
configs <- append(configs, list(config))
}
# Combine scatter and box plots into grids
configs <- list(
list(
grid_layout = list(nrow = 2, ncol = 2), # Scatter plots in a 2x2 grid (for the 8 plots)
plots = scatter_configs[1:4]
),
list(
grid_layout = list(nrow = 2, ncol = 2), # Box plots in a 2x2 grid (for the 8 plots)
plots = box_configs
),
list(
grid_layout = list(nrow = 3, ncol = 4), # Delta_ plots in a 3x4 grid
plots = scatter_configs
),
list(
grid_layout = list(nrow = 3, ncol = 4), # Delta_ box plots in a 3x4 grid
plots = box_configs
)
)
# Generate Delta comparison plots (4x3 grid for deletion and reference)
unique_groups <- df_filtered %>% select(all_of(group_vars)) %>% distinct()
for (i in seq_len(nrow(unique_groups))) {
group <- unique_groups[i, ]
group_data <- df_filtered %>% filter(across(all_of(group_vars), ~ . == group[[cur_column()]]))
OrfRep <- as.character(group$OrfRep)
Gene <- if ("Gene" %in% names(group)) as.character(group$Gene) else ""
num <- if ("num" %in% names(group)) as.character(group$num) else ""
OrfRepCombined <- paste(OrfRep, Gene, num, sep = "_")
# Generate plots for Delta variables
delta_vars <- c("Delta_L", "Delta_K", "Delta_r", "Delta_AUC")
for (var in delta_vars) {
y_limits <- limits_map[[var]]
upper_y <- y_limits[2]
lower_y <- y_limits[1]
y_span <- upper_y - lower_y
# Get WT_sd_var for error bar calculations
WT_sd_var <- paste0("WT_sd_", sub("Delta_", "", var))
WT_sd_value <- group_data[[WT_sd_var]][1]
error_bar_ymin <- 0 - (2 * WT_sd_value)
error_bar_ymax <- 0 + (2 * WT_sd_value)
# Set annotations (Z_Shifts, lm Z-Scores, NG, DB, SM values)
Z_Shift_var <- paste0("Z_Shift_", sub("Delta_", "", var))
Z_lm_var <- paste0("Z_lm_", sub("Delta_", "", var))
Z_Shift_value <- round(group_data[[Z_Shift_var]][1], 2)
Z_lm_value <- round(group_data[[Z_lm_var]][1], 2)
NG_value <- group_data$NG[1]
DB_value <- group_data$DB[1]
SM_value <- group_data$SM[1]
annotations <- list(
list(x = 1, y = upper_y - 0.2 * y_span, label = paste("ZShift =", Z_Shift_value)),
list(x = 1, y = upper_y - 0.3 * y_span, label = paste("lm ZScore =", Z_lm_value)),
list(x = 1, y = lower_y + 0.2 * y_span, label = paste("NG =", NG_value)),
list(x = 1, y = lower_y + 0.1 * y_span, label = paste("DB =", DB_value)),
list(x = 1, y = lower_y, label = paste("SM =", SM_value))
)
# Create configuration for each Delta plot
config <- list(
df = group_data,
plot_type = "scatter",
x_var = "conc_num",
y_var = var,
x_label = unique(group_data$Drug)[1],
title = paste(OrfRep, Gene, sep = " "),
coord_cartesian = y_limits,
annotations = annotations,
error_bar = TRUE,
error_bar_params = list(
ymin = error_bar_ymin,
ymax = error_bar_ymax
),
lm_smooth = TRUE,
x_breaks = unique(group_data$conc_num_factor_new),
x_labels = as.character(unique(group_data$conc_num)),
ylim_vals = y_limits,
grid_layout = list(ncol = 4, nrow = 3) # Adjust grid layout for gene-gene comparisons
)
configs <- append(configs, list(config))
}
}
return(configs)
}
@@ -826,14 +901,14 @@ generate_rank_plot_configs <- function(df, variables, is_lm = FALSE, adjust = FA
annotations = list(
list(
x = median(df_ranked[[rank_var]], na.rm = TRUE),
y = 10,
y = max(df_ranked[[zscore_var]], na.rm = TRUE) * 0.9,
label = paste("Deletion Enhancers =", num_enhancers),
hjust = 0.5,
vjust = 1
),
list(
x = median(df_ranked[[rank_var]], na.rm = TRUE),
y = -10,
y = min(df_ranked[[zscore_var]], na.rm = TRUE) * 0.9,
label = paste("Deletion Suppressors =", num_suppressors),
hjust = 0.5,
vjust = 0
@@ -870,7 +945,7 @@ generate_rank_plot_configs <- function(df, variables, is_lm = FALSE, adjust = FA
}
}
# Avg ZScore and Rank Avg ZScore Plots for r, L, K, and AUC
# Avg ZScore and Rank Avg ZScore Plots for variables
for (variable in variables) {
for (plot_type in c("Avg Zscore vs lm", "Rank Avg Zscore vs lm")) {
@@ -894,9 +969,22 @@ generate_rank_plot_configs <- function(df, variables, is_lm = FALSE, adjust = FA
# Fit the linear model
lm_model <- lm(as.formula(paste(y_var, "~", x_var)), data = df_ranked)
# Extract intercept and slope from the model coefficients
# Extract intercept, slope, and R-squared from the model coefficients
intercept <- coef(lm_model)[1]
slope <- coef(lm_model)[2]
r_squared <- summary(lm_model)$r.squared
# Annotations: include R-squared in the plot
annotations <- list(
list(
x = mean(range(df_ranked[[x_var]], na.rm = TRUE)),
y = mean(range(df_ranked[[y_var]], na.rm = TRUE)),
label = paste("R-squared =", round(r_squared, 2)),
hjust = 0.5,
vjust = 1,
size = 5
)
)
configs[[length(configs) + 1]] <- list(
df = df_ranked,
@@ -904,22 +992,7 @@ generate_rank_plot_configs <- function(df, variables, is_lm = FALSE, adjust = FA
y_var = y_var,
plot_type = "scatter",
title = title,
annotations = list(
list(
x = median(df_ranked[[rank_var]], na.rm = TRUE),
y = 10,
label = paste("Deletion Enhancers =", num_enhancers),
hjust = 0.5,
vjust = 1
),
list(
x = median(df_ranked[[rank_var]], na.rm = TRUE),
y = -10,
label = paste("Deletion Suppressors =", num_suppressors),
hjust = 0.5,
vjust = 0
)
),
annotations = annotations,
shape = 3,
size = 0.25,
smooth = TRUE,
@@ -936,7 +1009,7 @@ generate_rank_plot_configs <- function(df, variables, is_lm = FALSE, adjust = FA
return(configs)
}
generate_correlation_plot_configs <- function(df) {
generate_correlation_plot_configs <- function(df, highlight_cyan = FALSE) {
# Define relationships for plotting
relationships <- list(
list(x = "Z_lm_L", y = "Z_lm_K", label = "Interaction L vs. Interaction K"),
@@ -953,6 +1026,9 @@ generate_correlation_plot_configs <- function(df) {
# Fit linear model
lm_model <- lm(as.formula(paste(rel$y, "~", rel$x)), data = df)
lm_summary <- summary(lm_model)
intercept <- coef(lm_model)[1]
slope <- coef(lm_model)[2]
r_squared <- lm_summary$r.squared
# Construct plot configuration
config <- list(
@@ -965,18 +1041,18 @@ generate_correlation_plot_configs <- function(df) {
y_label = paste("z-score", gsub("Z_lm_", "", rel$y)),
annotations = list(
list(
x = Inf,
y = Inf,
label = paste("R-squared =", round(lm_summary$r.squared, 3)),
hjust = 1.1,
vjust = 2,
size = 4,
x = mean(range(df[[rel$x]], na.rm = TRUE)),
y = mean(range(df[[rel$y]], na.rm = TRUE)),
label = paste("R-squared =", round(r_squared, 3)),
hjust = 0.5,
vjust = 1,
size = 5,
color = "black"
)
),
smooth = TRUE,
smooth_color = "tomato3",
lm_line = list(intercept = coef(lm_model)[1], slope = coef(lm_model)[2]),
lm_line = list(intercept = intercept, slope = slope),
legend_position = "right",
shape = 3,
size = 0.5,
@@ -987,8 +1063,7 @@ generate_correlation_plot_configs <- function(df) {
fill = NA, color = "grey20", alpha = 0.1
)
),
cyan_points = TRUE,
grid_layout = list(ncol = 2, nrow = 2)
cyan_points = highlight_cyan, # Toggle cyan point highlighting
)
configs[[length(configs) + 1]] <- config
@@ -1023,7 +1098,7 @@ main <- function() {
df_no_zeros <- df_na %>% filter(L > 0) # formerly X_noZero
# Save some constants
max_conc <- max(df$conc_num_factor_num)
max_conc <- max(df$conc_num_factor_zeroed_num)
l_half_median <- (median(df_above_tolerance$L, na.rm = TRUE)) / 2
k_half_median <- (median(df_above_tolerance$K, na.rm = TRUE)) / 2
@@ -1106,7 +1181,7 @@ main <- function() {
plot_type = "scatter",
delta_bg_point = TRUE,
title = "Raw L vs K before quality control",
color_var = "conc_num",
color_var = "conc_num_factor_zeroed",
error_bar = FALSE,
legend_position = "right"
)
@@ -1119,7 +1194,7 @@ main <- function() {
y_var = NULL,
plot_type = "density",
title = "Density plot for Delta Background by [Drug] (All Data)",
color_var = "conc_num",
color_var = "conc_num_factor_zeroed",
x_label = "Delta Background",
y_label = "Density",
error_bar = FALSE,
@@ -1130,7 +1205,7 @@ main <- function() {
y_var = NULL,
plot_type = "bar",
title = "Bar plot for Delta Background by [Drug] (All Data)",
color_var = "conc_num",
color_var = "conc_num_factor_zeroed",
x_label = "Delta Background",
y_label = "Count",
error_bar = FALSE,
@@ -1146,7 +1221,7 @@ main <- function() {
delta_bg_point = TRUE,
title = paste("Raw L vs K for strains above Delta Background threshold of",
round(df_above_tolerance$delta_bg_tolerance[[1]], 3), "or above"),
color_var = "conc_num",
color_var = "conc_num_factor_zeroed",
position = "jitter",
annotations = list(
list(
@@ -1194,7 +1269,7 @@ main <- function() {
plot_type = "scatter",
delta_bg_point = TRUE,
title = "Raw L vs K for strains falling outside 2SD of the K mean at each Conc",
color_var = "conc_num",
color_var = "conc_num_factor_zeroed",
position = "jitter",
legend_position = "right"
)
@@ -1208,7 +1283,7 @@ main <- function() {
plot_type = "scatter",
gene_point = TRUE,
title = "Delta Background vs K for strains falling outside 2SD of the K mean at each Conc",
color_var = "conc_num",
color_var = "conc_num_factor_zeroed",
position = "jitter",
legend_position = "right"
)
@@ -1305,7 +1380,7 @@ main <- function() {
reference_results <- calculate_interaction_scores(df_reference_stats, max_conc, 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$interactions_joined
zscore_interactions_reference_joined <- reference_results$full_data
message("Calculating deletion strain(s) interactions scores")
df_deletion_stats <- calculate_summary_stats(
@@ -1316,7 +1391,7 @@ main <- function() {
deletion_results <- calculate_interaction_scores(df_deletion_stats, max_conc, bg_stats, group_vars = c("OrfRep"))
zscore_calculations <- deletion_results$calculations
zscore_interactions <- deletion_results$interactions
zscore_interactions_joined <- deletion_results$interactions_joined
zscore_interactions_joined <- deletion_results$full_data
# Writing Z-Scores to file
write.csv(zscore_calculations_reference, file = file.path(out_dir, "zscore_calculations_reference.csv"), row.names = FALSE)
@@ -1326,11 +1401,11 @@ main <- function() {
# Create interaction plots
message("Generating reference interaction plots")
reference_plot_configs <- generate_interaction_plot_configs(zscore_interactions_reference_joined)
reference_plot_configs <- generate_interaction_plot_configs(zscore_interactions_reference_joined, plot_type = "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_plot_configs <- generate_interaction_plot_configs(zscore_interactions_joined, plot_type = "deletion")
generate_and_save_plots(out_dir, "interaction_plots", deletion_plot_configs)
# Define conditions for enhancers and suppressors