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Add missing calculation columns for Delta_L error bars

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Bryan Roessler
2024-10-04 15:19:05 -04:00
parent 328fe1f116
commit 320338316c
2 changed files with 59 additions and 36 deletions
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92
qhtcp-workflow/apps/r/calculate_interaction_zscores.R
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@@ -347,6 +347,25 @@ calculate_interaction_scores <- function(df, df_bg, group_vars, overlap_threshol
}) %>%
ungroup()
# For interaction plot error bars
delta_means <- calculations %>%
group_by(across(all_of(group_vars))) %>%
summarise(
mean_Delta_L = mean(Delta_L, na.rm = TRUE),
mean_Delta_K = mean(Delta_K, na.rm = TRUE),
mean_Delta_r = mean(Delta_r, na.rm = TRUE),
mean_Delta_AUC = mean(Delta_AUC, na.rm = TRUE),
sd_Delta_L = sd(Delta_L, na.rm = TRUE),
sd_Delta_K = sd(Delta_K, na.rm = TRUE),
sd_Delta_r = sd(Delta_r, na.rm = TRUE),
sd_Delta_AUC = sd(Delta_AUC, na.rm = TRUE),
.groups = "drop"
)
calculations <- calculations %>%
left_join(delta_means, by = group_vars)
# Summary statistics for lm scores
lm_means_sds <- calculations %>%
summarise(
@@ -462,6 +481,7 @@ calculate_interaction_scores <- function(df, df_bg, group_vars, overlap_threshol
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,
mean_Delta_L, mean_Delta_K, mean_Delta_r, mean_Delta_AUC,
Zscore_L, Zscore_K, Zscore_r, Zscore_AUC
)
@@ -548,8 +568,8 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs) {
# Print rows being filtered out
if (nrow(out_of_bounds_df) > 0) {
message("Filtered out rows outside y-limits:")
print(out_of_bounds_df)
message("# of filtered rows outside y-limits (for plotting): ", nrow(out_of_bounds_df))
# print(out_of_bounds_df)
}
# Filter the valid data for plotting
@@ -646,11 +666,11 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs) {
}
# Convert ggplot to plotly for interactive version
plotly_plot <- suppressWarnings(plotly::ggplotly(plot))
# plotly_plot <- suppressWarnings(plotly::ggplotly(plot))
# Store both static and interactive versions
static_plots[[i]] <- plot
plotly_plots[[i]] <- plotly_plot
# plotly_plots[[i]] <- plotly_plot
}
# Print the plots in the current group to the PDF
@@ -987,7 +1007,7 @@ generate_interaction_plot_configs <- function(df, type) {
df = group_data,
plot_type = "scatter",
x_var = "conc_num_factor_factor",
y_var = var,
y_var = paste0("Delta_", var),
x_label = unique(group_data$Drug)[1],
title = paste(OrfRepTitle, Gene, num, sep = " "),
coord_cartesian = y_limits,
@@ -1019,10 +1039,11 @@ generate_interaction_plot_configs <- function(df, type) {
}
}
# Return plot configs
return(list(
list(grid_layout = list(ncol = 2), plots = stats_plot_configs), # nrow will be calculated dynamically
list(grid_layout = list(ncol = 2), plots = stats_boxplot_configs), # nrow will be calculated dynamically
list(grid_layout = list(ncol = 4), plots = delta_plot_configs) # nrow will be calculated dynamically
list(grid_layout = list(ncol = 2), plots = stats_plot_configs),
list(grid_layout = list(ncol = 2), plots = stats_boxplot_configs),
list(grid_layout = list(ncol = 4), plots = delta_plot_configs) # nrow calculated dynamically
))
}
@@ -1464,6 +1485,7 @@ main <- function() {
variables = c("L", "K", "r", "AUC"),
group_vars = c("OrfRep", "Gene", "Drug", "num", "conc_num", "conc_num_factor_factor")
)$df_with_stats
message("Calculating reference strain interaction scores")
results <- calculate_interaction_scores(df_reference_stats, df_bg_stats, group_vars = c("OrfRep", "Gene", "Drug", "num"))
df_calculations_reference <- results$calculations
@@ -1472,37 +1494,37 @@ main <- function() {
write.csv(df_calculations_reference, file = file.path(out_dir, "zscore_calculations_reference.csv"), row.names = FALSE)
write.csv(df_interactions_reference, file = file.path(out_dir, "zscore_interactions_reference.csv"), row.names = FALSE)
# message("Setting missing deletion values to the highest theoretical value at each drug conc for L")
# df_deletion <- df_na_stats %>% # formerly X2
# filter(OrfRep != strain) %>%
# filter(!is.na(L)) %>%
# group_by(OrfRep, Gene, conc_num) %>%
# mutate(
# max_l_theoretical = max(max_L, na.rm = TRUE),
# L = ifelse(L == 0 & !is.na(L) & conc_num > 0, max_l_theoretical, L),
# SM = ifelse(L >= max_l_theoretical & !is.na(L) & conc_num > 0, 1, SM),
# L = ifelse(L >= max_l_theoretical & !is.na(L) & conc_num > 0, max_l_theoretical, L)) %>%
# ungroup()
# message("Calculating deletion strain(s) summary statistics")
# df_deletion_stats <- calculate_summary_stats(
# df = df_deletion,
# variables = c("L", "K", "r", "AUC"),
# group_vars = c("OrfRep", "Gene", "Drug", "conc_num", "conc_num_factor_factor")
# )$df_with_stats
# message("Calculating deletion strain(s) interactions scores")
# results <- calculate_interaction_scores(df_deletion_stats, df_bg_stats, group_vars = c("OrfRep", "Gene", "Drug"))
# df_calculations <- results$calculations
# df_interactions <- results$interactions
# df_interactions_joined <- results$full_data
# write.csv(df_calculations, file = file.path(out_dir, "zscore_calculations.csv"), row.names = FALSE)
# write.csv(df_interactions, file = file.path(out_dir, "zscore_interactions.csv"), row.names = FALSE)
message("Generating reference interaction plots")
reference_plot_configs <- generate_interaction_plot_configs(df_interactions_reference_joined, "reference")
generate_and_save_plots(out_dir, "interaction_plots_reference", reference_plot_configs)
message("Setting missing deletion values to the highest theoretical value at each drug conc for L")
df_deletion <- df_na_stats %>% # formerly X2
filter(OrfRep != strain) %>%
filter(!is.na(L)) %>%
group_by(OrfRep, Gene, conc_num) %>%
mutate(
max_l_theoretical = max(max_L, na.rm = TRUE),
L = ifelse(L == 0 & !is.na(L) & conc_num > 0, max_l_theoretical, L),
SM = ifelse(L >= max_l_theoretical & !is.na(L) & conc_num > 0, 1, SM),
L = ifelse(L >= max_l_theoretical & !is.na(L) & conc_num > 0, max_l_theoretical, L)) %>%
ungroup()
message("Calculating deletion strain(s) summary statistics")
df_deletion_stats <- calculate_summary_stats(
df = df_deletion,
variables = c("L", "K", "r", "AUC"),
group_vars = c("OrfRep", "Gene", "Drug", "conc_num", "conc_num_factor_factor")
)$df_with_stats
message("Calculating deletion strain(s) interactions scores")
results <- calculate_interaction_scores(df_deletion_stats, df_bg_stats, group_vars = c("OrfRep", "Gene", "Drug"))
df_calculations <- results$calculations
df_interactions <- results$interactions
df_interactions_joined <- results$full_data
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)
message("Generating deletion interaction plots")
deletion_plot_configs <- generate_interaction_plot_configs(df_interactions_joined, "deletion")
generate_and_save_plots(out_dir, "interaction_plots", deletion_plot_configs)

1
qhtcp-workflow/qhtcp-workflow
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@@ -1443,6 +1443,7 @@ wrapper calculate_interaction_zscores
# * Plate analysis error bars and some others will be slightly different
# * Can be changed back but better to have plots reflect data, no?
# * Dynamically generate axis limits based on data (if desired)
# * Parallelize interaction plotting
#
# INPUT
#