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Flatten means and sds for error bars

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This commit is contained in:
Bryan Roessler
2024-10-05 03:04:28 -04:00
parent 876970924b
commit 25a5c7ff23
1 changed files with 120 additions and 66 deletions
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184
qhtcp-workflow/apps/r/calculate_interaction_zscores.R
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@@ -345,7 +345,7 @@ calculate_interaction_scores <- function(df, df_bg, group_vars, overlap_threshol
ungroup() ungroup()
# For interaction plot error bars # For interaction plot error bars
delta_means <- calculations %>% delta_means_sds <- calculations %>%
group_by(across(all_of(group_vars))) %>% group_by(across(all_of(group_vars))) %>%
summarise( summarise(
mean_Delta_L = mean(Delta_L, na.rm = TRUE), mean_Delta_L = mean(Delta_L, na.rm = TRUE),
@@ -356,12 +356,11 @@ calculate_interaction_scores <- function(df, df_bg, group_vars, overlap_threshol
sd_Delta_K = sd(Delta_K, na.rm = TRUE), sd_Delta_K = sd(Delta_K, na.rm = TRUE),
sd_Delta_r = sd(Delta_r, na.rm = TRUE), sd_Delta_r = sd(Delta_r, na.rm = TRUE),
sd_Delta_AUC = sd(Delta_AUC, na.rm = TRUE), sd_Delta_AUC = sd(Delta_AUC, na.rm = TRUE),
.groups = "drop" .groups = "drop"
) )
calculations <- calculations %>% calculations <- calculations %>%
left_join(delta_means, by = group_vars) left_join(delta_means_sds, by = group_vars)
# Summary statistics for lm scores # Summary statistics for lm scores
lm_means_sds <- calculations %>% lm_means_sds <- calculations %>%
@@ -436,13 +435,12 @@ calculate_interaction_scores <- function(df, df_bg, group_vars, overlap_threshol
NG = first(NG), NG = first(NG),
DB = first(DB), DB = first(DB),
SM = first(SM), SM = first(SM),
.groups = "drop" .groups = "drop"
) )
# Add overlap threshold categories based on Z-lm and Avg-Z scores # Add overlap threshold categories based on Z-lm and Avg-Z scores
interactions <- interactions %>% interactions <- interactions %>%
filter(!is.na(Z_lm_L)) %>% filter(!is.na(Z_lm_L) | !is.na(Avg_Zscore_L)) %>%
mutate( mutate(
Overlap = case_when( 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 Enhancer Both",
@@ -603,7 +601,7 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs, page_width
plot <- ggplot(df, aes_mapping) + theme_publication(legend_position = config$legend_position) plot <- ggplot(df, aes_mapping) + theme_publication(legend_position = config$legend_position)
# Add appropriate plot layer based on plot type # Add appropriate plot layer or helper function based on plot type
plot <- switch(config$plot_type, plot <- switch(config$plot_type,
"scatter" = generate_scatter_plot(plot, config), "scatter" = generate_scatter_plot(plot, config),
"box" = generate_boxplot(plot, config), "box" = generate_boxplot(plot, config),
@@ -642,8 +640,19 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs, page_width
# Add error bars if specified # Add error bars if specified
if (!is.null(config$error_bar) && config$error_bar) { if (!is.null(config$error_bar) && config$error_bar) {
y_mean_col <- paste0("mean_", config$y_var) # Check if custom columns are provided for y_mean and y_sd, or use the defaults
y_sd_col <- paste0("sd_", config$y_var) y_mean_col <- if (!is.null(config$error_bar_params$y_mean_col)) {
config$error_bar_params$y_mean_col
} else {
paste0("mean_", config$y_var)
}
y_sd_col <- if (!is.null(config$error_bar_params$y_sd_col)) {
config$error_bar_params$y_sd_col
} else {
paste0("sd_", config$y_var)
}
# If color_var is provided and no fixed error bar color is set, use aes() to map color dynamically # If color_var is provided and no fixed error bar color is set, use aes() to map color dynamically
if (!is.null(config$color_var) && is.null(config$error_bar_params$color)) { if (!is.null(config$color_var) && is.null(config$error_bar_params$color)) {
plot <- plot + geom_errorbar( plot <- plot + geom_errorbar(
@@ -651,7 +660,7 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs, page_width
x = .data[[config$x_var]], x = .data[[config$x_var]],
ymin = .data[[y_mean_col]] - .data[[y_sd_col]], ymin = .data[[y_mean_col]] - .data[[y_sd_col]],
ymax = .data[[y_mean_col]] + .data[[y_sd_col]], ymax = .data[[y_mean_col]] + .data[[y_sd_col]],
color = .data[[config$color_var]] # Dynamic color from the data color = .data[[config$color_var]]
) )
) )
} else { } else {
@@ -662,7 +671,17 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs, page_width
ymin = .data[[y_mean_col]] - .data[[y_sd_col]], ymin = .data[[y_mean_col]] - .data[[y_sd_col]],
ymax = .data[[y_mean_col]] + .data[[y_sd_col]] ymax = .data[[y_mean_col]] + .data[[y_sd_col]]
), ),
color = config$error_bar_params$color # Fixed color color = config$error_bar_params$color
)
}
# Add the center point if the option is provided
if (!is.null(config$error_bar_params$center_point)) {
plot <- plot + geom_point(aes(
x = .data[[config$x_var]],
y = .data[[y_mean_col]]),
color = config$error_bar_params$color,
shape = 16
) )
} }
} }
@@ -731,6 +750,10 @@ generate_scatter_plot <- function(plot, config) {
) )
} }
if (!is.null(config$gray_points) && config$gray_points) {
plot <- plot + geom_point(shape = 3, color = "gray70", size = 1)
}
# Add Smooth Line if specified # Add Smooth Line if specified
if (!is.null(config$smooth) && config$smooth) { if (!is.null(config$smooth) && config$smooth) {
smooth_color <- if (!is.null(config$smooth_color)) config$smooth_color else "blue" smooth_color <- if (!is.null(config$smooth_color)) config$smooth_color else "blue"
@@ -742,14 +765,17 @@ generate_scatter_plot <- function(plot, config) {
slope = config$lm_line$slope, slope = config$lm_line$slope,
color = smooth_color color = smooth_color
) )
} else {
plot <- plot +
geom_smooth(
method = "lm",
se = FALSE,
color = smooth_color
)
} }
# For now I want to try and hardcode it
# else {
# plot <- plot +
# geom_smooth(
# method = "lm",
# se = FALSE,
# color = smooth_color
# )
# }
} }
# Add SD Bands if specified # Add SD Bands if specified
@@ -878,7 +904,7 @@ generate_plate_analysis_plot_configs <- function(variables, df_before = NULL, df
return(list(plots = plot_configs)) return(list(plots = plot_configs))
} }
generate_interaction_plot_configs <- function(df, type) { generate_interaction_plot_configs <- function(df_summary, df_interaction, type) {
# Define the y-limits for the plots # Define the y-limits for the plots
limits_map <- list( limits_map <- list(
@@ -893,7 +919,7 @@ generate_interaction_plot_configs <- function(df, type) {
delta_plot_configs <- list() delta_plot_configs <- list()
# Overall statistics plots # Overall statistics plots
OrfRep <- first(df$OrfRep) # this should correspond to the reference strain OrfRep <- first(df_summary$OrfRep) # this should correspond to the reference strain
for (plot_type in c("scatter", "box")) { for (plot_type in c("scatter", "box")) {
@@ -903,15 +929,15 @@ generate_interaction_plot_configs <- function(df, type) {
# Common plot configuration # Common plot configuration
plot_config <- list( plot_config <- list(
df = df, df = df_summary,
plot_type = plot_type, plot_type = plot_type,
x_var = "conc_num_factor_factor", x_var = "conc_num_factor_factor",
y_var = var, y_var = var,
shape = 16, shape = 16,
x_label = unique(df$Drug)[1], x_label = paste0("[", unique(df_summary$Drug)[1], "]"),
coord_cartesian = y_limits, coord_cartesian = y_limits,
x_breaks = unique(df$conc_num_factor_factor), x_breaks = unique(df_summary$conc_num_factor_factor),
x_labels = as.character(unique(df$conc_num)) x_labels = as.character(unique(df_summary$conc_num))
) )
# Add specific configurations for scatter and box plots # Add specific configurations for scatter and box plots
@@ -920,23 +946,25 @@ generate_interaction_plot_configs <- function(df, type) {
plot_config$error_bar <- TRUE plot_config$error_bar <- TRUE
plot_config$error_bar_params <- list( plot_config$error_bar_params <- list(
color = "red", color = "red",
center_point = TRUE center_point = TRUE,
y_mean_col = paste0("mean_mean_", var),
y_sd_col = paste0("mean_sd_", var)
) )
plot_config$position <- "jitter" plot_config$position <- "jitter"
# Cannot figure out how to place these properly for discrete x-axis so let's be hacky # Cannot figure out how to place these properly for discrete x-axis so let's be hacky
annotations <- list( annotations <- list(
list(x = 0.25, y = y_limits[1] + 0.1 * y_span, label = " NG:"), list(x = 0.25, y = y_limits[1] + 0.08 * y_span, label = " NG =", size = 4),
list(x = 0.25, y = y_limits[1] + 0.05 * y_span, label = " DB:"), list(x = 0.25, y = y_limits[1] + 0.04 * y_span, label = " DB =", size = 4),
list(x = 0.25, y = y_limits[1], label = " SM:") list(x = 0.25, y = y_limits[1], label = " SM =", size = 4)
) )
for (x_val in unique(df$conc_num_factor_factor)) { for (x_val in unique(df_summary$conc_num_factor_factor)) {
current_df <- df %>% filter(.data[[plot_config$x_var]] == x_val) current_df <- df_summary %>% filter(.data[[plot_config$x_var]] == x_val)
annotations <- append(annotations, list( annotations <- append(annotations, list(
list(x = x_val, y = y_limits[1] + 0.1 * y_span, label = first(current_df$NG, default = 0)), list(x = x_val, y = y_limits[1] + 0.08 * y_span, label = first(current_df$NG, default = 0), size = 4),
list(x = x_val, y = y_limits[1] + 0.05 * y_span, label = first(current_df$DB, default = 0)), list(x = x_val, y = y_limits[1] + 0.04 * y_span, label = first(current_df$DB, default = 0), size = 4),
list(x = x_val, y = y_limits[1], label = first(current_df$SM, default = 0)) list(x = x_val, y = y_limits[1], label = first(current_df$SM, default = 0), size = 4)
)) ))
} }
@@ -945,7 +973,7 @@ generate_interaction_plot_configs <- function(df, type) {
stats_plot_configs <- append(stats_plot_configs, list(plot_config)) stats_plot_configs <- append(stats_plot_configs, list(plot_config))
} else if (plot_type == "box") { } else if (plot_type == "box") {
plot_config$title <- sprintf("%s Boxplot RF for %s with SD", OrfRep, var) plot_config$title <- sprintf("%s Box RF for %s with SD", OrfRep, var)
plot_config$position <- "dodge" plot_config$position <- "dodge"
stats_boxplot_configs <- append(stats_boxplot_configs, list(plot_config)) stats_boxplot_configs <- append(stats_boxplot_configs, list(plot_config))
@@ -967,7 +995,7 @@ generate_interaction_plot_configs <- function(df, type) {
AUC = c(-6000, 6000) AUC = c(-6000, 6000)
) )
grouped_data <- df %>% grouped_data <- df_interaction %>%
group_by(across(all_of(group_vars))) %>% group_by(across(all_of(group_vars))) %>%
group_split() group_split()
@@ -1005,9 +1033,10 @@ generate_interaction_plot_configs <- function(df, type) {
plot_type = "scatter", plot_type = "scatter",
x_var = "conc_num_factor_factor", x_var = "conc_num_factor_factor",
y_var = paste0("Delta_", var), y_var = paste0("Delta_", var),
x_label = unique(group_data$Drug)[1], x_label = paste0("[", unique(df_summary$Drug)[1], "]"),
shape = 16,
title = paste(OrfRepTitle, Gene, sep = " "), title = paste(OrfRepTitle, Gene, sep = " "),
title_size = rel(1.2), title_size = rel(1.3),
coord_cartesian = y_limits, coord_cartesian = y_limits,
annotations = list( annotations = list(
list(x = 1, y = y_limits[2] - 0.2 * y_span, label = paste("ZShift =", Z_Shift_value)), list(x = 1, y = y_limits[2] - 0.2 * y_span, label = paste("ZShift =", Z_Shift_value)),
@@ -1021,16 +1050,17 @@ generate_interaction_plot_configs <- function(df, type) {
error_bar_params = list( error_bar_params = list(
ymin = 0 - (2 * WT_sd_value), ymin = 0 - (2 * WT_sd_value),
ymax = 0 + (2 * WT_sd_value), ymax = 0 + (2 * WT_sd_value),
color = "black" color = "gray"
), ),
smooth = TRUE,
x_breaks = unique(group_data$conc_num_factor_factor), x_breaks = unique(group_data$conc_num_factor_factor),
x_labels = as.character(unique(group_data$conc_num)), x_labels = as.character(unique(group_data$conc_num)),
ylim_vals = y_limits, ylim_vals = y_limits,
y_filter = FALSE, y_filter = FALSE,
smooth = TRUE,
lm_line = list( lm_line = list(
intercept = lm_intercept_value, intercept = lm_intercept_value,
slope = lm_slope_value slope = lm_slope_value,
color = "blue"
) )
) )
delta_plot_configs <- append(delta_plot_configs, list(plot_config)) delta_plot_configs <- append(delta_plot_configs, list(plot_config))
@@ -1082,7 +1112,6 @@ generate_rank_plot_configs <- function(df, is_lm = FALSE, adjust = FALSE, overla
fill_negative = "orange", fill_negative = "orange",
alpha_positive = 0.3, alpha_positive = 0.3,
alpha_negative = 0.3, alpha_negative = 0.3,
annotations = NULL,
shape = 3, shape = 3,
size = 0.1, size = 0.1,
y_label = y_label, y_label = y_label,
@@ -1183,7 +1212,8 @@ generate_correlation_plot_configs <- function(df) {
shape = 3, shape = 3,
size = 0.5, size = 0.5,
color_var = "Overlap", color_var = "Overlap",
cyan_points = highlight_cyan # Include cyan points or not based on the loop cyan_points = highlight_cyan, # include cyan points or not based on the loop
gray_points = TRUE
) )
plot_configs <- append(plot_configs, list(plot_config)) plot_configs <- append(plot_configs, list(plot_config))
@@ -1226,10 +1256,10 @@ main <- function() {
) )
message("Calculating summary statistics before quality control") message("Calculating summary statistics before quality control")
df_stats <- calculate_summary_stats( df_stats <- calculate_summary_stats( # formerly X_stats_ALL
df = df, df = df,
variables = c("L", "K", "r", "AUC", "delta_bg"), variables = c("L", "K", "r", "AUC", "delta_bg"),
group_vars = c("conc_num"))$df_with_stats group_vars = c("conc_num", "conc_num_factor_factor"))$df_with_stats
frequency_delta_bg_plot_configs <- list( frequency_delta_bg_plot_configs <- list(
plots = list( plots = list(
@@ -1295,7 +1325,7 @@ main <- function() {
ss <- calculate_summary_stats( ss <- calculate_summary_stats(
df = df_na, df = df_na,
variables = c("L", "K", "r", "AUC", "delta_bg"), variables = c("L", "K", "r", "AUC", "delta_bg"),
group_vars = c("conc_num")) group_vars = c("conc_num", "conc_num_factor_factor"))
df_na_ss <- ss$summary_stats df_na_ss <- ss$summary_stats
df_na_stats <- ss$df_with_stats # formerly X_stats_ALL df_na_stats <- ss$df_with_stats # formerly X_stats_ALL
write.csv(df_na_ss, file = file.path(out_dir, "summary_stats_all_strains.csv"), row.names = FALSE) write.csv(df_na_ss, file = file.path(out_dir, "summary_stats_all_strains.csv"), row.names = FALSE)
@@ -1307,7 +1337,7 @@ main <- function() {
df_no_zeros_stats <- calculate_summary_stats( df_no_zeros_stats <- calculate_summary_stats(
df = df_no_zeros, df = df_no_zeros,
variables = c("L", "K", "r", "AUC", "delta_bg"), variables = c("L", "K", "r", "AUC", "delta_bg"),
group_vars = c("conc_num") group_vars = c("conc_num", "conc_num_factor_factor")
)$df_with_stats )$df_with_stats
message("Filtering by 2SD of K") message("Filtering by 2SD of K")
@@ -1318,14 +1348,15 @@ main <- function() {
message("Calculating summary statistics for L within 2SD of K") message("Calculating summary statistics for L within 2SD of K")
# TODO We're omitting the original z_max calculation, not sure if needed? # TODO We're omitting the original z_max calculation, not sure if needed?
ss <- calculate_summary_stats(df_na_within_2sd_k, "L", ss <- calculate_summary_stats(df_na_within_2sd_k, "L", # formerly X_stats_BY_L_within_2SD_K
group_vars = c("conc_num"))$summary_stats group_vars = c("conc_num", "conc_num_factor_factor"))$summary_stats
write.csv(ss, write.csv(ss,
file = file.path(out_dir_qc, "max_observed_L_vals_for_spots_within_2SD_K.csv"), file = file.path(out_dir_qc, "max_observed_L_vals_for_spots_within_2SD_K.csv"),
row.names = FALSE) row.names = FALSE)
message("Calculating summary statistics for L outside 2SD of K") message("Calculating summary statistics for L outside 2SD of K")
ss <- calculate_summary_stats(df_na_outside_2sd_k, "L", group_vars = c("conc_num")) ss <- calculate_summary_stats(df_na_outside_2sd_k, "L", # formerly X_stats_BY_L_outside_2SD_K
group_vars = c("conc_num", "conc_num_factor_factor"))
df_na_l_outside_2sd_k_stats <- ss$df_with_stats df_na_l_outside_2sd_k_stats <- ss$df_with_stats
write.csv(ss$summary_stats, write.csv(ss$summary_stats,
file = file.path(out_dir, "max_observed_L_vals_for_spots_outside_2SD_K.csv"), file = file.path(out_dir, "max_observed_L_vals_for_spots_outside_2SD_K.csv"),
@@ -1435,10 +1466,10 @@ main <- function() {
) )
# Parallelize background and quality control plot generation # Parallelize background and quality control plot generation
furrr::future_map(plot_configs, function(config) { # furrr::future_map(plot_configs, function(config) {
generate_and_save_plots(config$out_dir, config$filename, config$plot_configs, # generate_and_save_plots(config$out_dir, config$filename, config$plot_configs,
page_width = config$page_width, page_height = config$page_height) # page_width = config$page_width, page_height = config$page_height)
}, .options = furrr_options(seed = TRUE)) # }, .options = furrr_options(seed = TRUE))
# Loop over background strains # Loop over background strains
# TODO currently only tested against one strain, if we want to do multiple strains we'll # TODO currently only tested against one strain, if we want to do multiple strains we'll
@@ -1460,7 +1491,7 @@ main <- function() {
filter(!is.na(L)) filter(!is.na(L))
message("Calculating background strain summary statistics") message("Calculating background strain summary statistics")
ss_bg <- calculate_summary_stats(df_bg, c("L", "K", "r", "AUC", "delta_bg"), ss_bg <- calculate_summary_stats(df_bg, c("L", "K", "r", "AUC", "delta_bg"), # formerly X_stats_BY
group_vars = c("OrfRep", "Drug", "conc_num", "conc_num_factor_factor")) group_vars = c("OrfRep", "Drug", "conc_num", "conc_num_factor_factor"))
summary_stats_bg <- ss_bg$summary_stats summary_stats_bg <- ss_bg$summary_stats
df_bg_stats <- ss_bg$df_with_stats df_bg_stats <- ss_bg$df_with_stats
@@ -1473,7 +1504,7 @@ main <- function() {
df_reference <- df_na_stats %>% # formerly X2_RF df_reference <- df_na_stats %>% # formerly X2_RF
filter(OrfRep == strain) %>% filter(OrfRep == strain) %>%
filter(!is.na(L)) %>% filter(!is.na(L)) %>%
group_by(OrfRep, Drug, conc_num) %>% group_by(OrfRep, Drug, conc_num, conc_num_factor_factor) %>%
mutate( mutate(
max_l_theoretical = max(max_L, na.rm = TRUE), max_l_theoretical = max(max_L, na.rm = TRUE),
L = ifelse(L == 0 & !is.na(L) & conc_num > 0, max_l_theoretical, L), L = ifelse(L == 0 & !is.na(L) & conc_num > 0, max_l_theoretical, L),
@@ -1482,22 +1513,45 @@ main <- function() {
ungroup() ungroup()
message("Calculating reference strain summary statistics") message("Calculating reference strain summary statistics")
df_reference_stats <- calculate_summary_stats( df_reference_summary_stats <- calculate_summary_stats( # formerly X_stats_X2_RF
df = df_reference, df = df_reference,
variables = c("L", "K", "r", "AUC"), variables = c("L", "K", "r", "AUC"),
group_vars = c("OrfRep", "Gene", "Drug", "num", "conc_num", "conc_num_factor_factor") group_vars = c("OrfRep", "Drug", "conc_num", "conc_num_factor_factor")
)$df_with_stats
# Summarise statistics for error bars
df_reference_summary_stats <- df_reference_summary_stats %>%
group_by(OrfRep, Drug, conc_num, conc_num_factor_factor) %>%
mutate(
mean_mean_L = first(mean_L),
mean_sd_L = first(sd_L),
mean_mean_K = first(mean_K),
mean_sd_K = first(sd_K),
mean_mean_r = first(mean_r),
mean_sd_r = first(sd_r),
mean_mean_AUC = first(mean_AUC),
mean_sd_AUC = first(sd_AUC),
.groups = "drop"
)
message("Calculating reference strain interaction summary statistics") # formerly X_stats_interaction
df_reference_interaction_stats <- calculate_summary_stats(
df = df_reference,
variables = c("L", "K", "r", "AUC"),
group_vars = c("OrfRep", "Gene", "num", "Drug", "conc_num", "conc_num_factor_factor")
)$df_with_stats )$df_with_stats
message("Calculating reference strain interaction scores") message("Calculating reference strain interaction scores")
results <- calculate_interaction_scores(df_reference_stats, df_bg_stats, group_vars = c("OrfRep", "Gene", "Drug", "num")) results <- calculate_interaction_scores(df_reference_interaction_stats,
df_calculations_reference <- results$calculations df_bg_stats, group_vars = c("OrfRep", "Gene", "num", "Drug"))
df_interactions_reference <- results$interactions df_reference_calculations <- results$calculations
df_interactions_reference_joined <- results$full_data df_reference_interactions <- results$interactions
write.csv(df_calculations_reference, file = file.path(out_dir, "zscore_calculations_reference.csv"), row.names = FALSE) df_reference_interactions_joined <- results$full_data
write.csv(df_interactions_reference, file = file.path(out_dir, "zscore_interactions_reference.csv"), row.names = FALSE) write.csv(df_reference_calculations, file = file.path(out_dir, "zscore_calculations_reference.csv"), row.names = FALSE)
write.csv(df_reference_interactions, file = file.path(out_dir, "zscore_interactions_reference.csv"), row.names = FALSE)
message("Generating reference interaction plots") message("Generating reference interaction plots")
reference_plot_configs <- generate_interaction_plot_configs(df_interactions_reference_joined, "reference") reference_plot_configs <- generate_interaction_plot_configs(df_reference_summary_stats, df_reference_interactions_joined, "reference")
generate_and_save_plots(out_dir, "interaction_plots_reference", reference_plot_configs, page_width = 16, page_height = 16) generate_and_save_plots(out_dir, "interaction_plots_reference", reference_plot_configs, page_width = 16, page_height = 16)
message("Setting missing deletion values to the highest theoretical value at each drug conc for L") message("Setting missing deletion values to the highest theoretical value at each drug conc for L")
@@ -1512,7 +1566,7 @@ main <- function() {
L = ifelse(L >= max_l_theoretical & !is.na(L) & conc_num > 0, max_l_theoretical, L)) %>% L = ifelse(L >= max_l_theoretical & !is.na(L) & conc_num > 0, max_l_theoretical, L)) %>%
ungroup() ungroup()
message("Calculating deletion strain(s) summary statistics") message("Calculating deletion strain(s) interaction summary statistics")
df_deletion_stats <- calculate_summary_stats( df_deletion_stats <- calculate_summary_stats(
df = df_deletion, df = df_deletion,
variables = c("L", "K", "r", "AUC"), variables = c("L", "K", "r", "AUC"),
@@ -1528,7 +1582,7 @@ main <- function() {
write.csv(df_interactions, file = file.path(out_dir, "zscore_interactions.csv"), row.names = FALSE) write.csv(df_interactions, file = file.path(out_dir, "zscore_interactions.csv"), row.names = FALSE)
message("Generating deletion interaction plots") message("Generating deletion interaction plots")
deletion_plot_configs <- generate_interaction_plot_configs(df_interactions_joined, "deletion") deletion_plot_configs <- generate_interaction_plot_configs(df_reference_summary_stats, df_interactions_joined, "deletion")
generate_and_save_plots(out_dir, "interaction_plots", deletion_plot_configs, page_width = 16, page_height = 16) generate_and_save_plots(out_dir, "interaction_plots", deletion_plot_configs, page_width = 16, page_height = 16)
message("Writing enhancer/suppressor csv files") message("Writing enhancer/suppressor csv files")