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Before calculate_interaction_scores() refactor

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Bryan Roessler
2024-09-02 16:59:53 -04:00
parent 4311354325
commit 111909914c
1 changed files with 32 additions and 24 deletions
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56
workflow/apps/r/calculate_interaction_zscores5.R
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@@ -5,10 +5,17 @@ suppressMessages({
library(dplyr)
library(ggthemes)
library(data.table)
library(unix)
})
options(warn = 2, max.print = 1000)
# Set the memory limit to 30GB (30 * 1024 * 1024 * 1024 bytes)
soft_limit <- 30 * 1024 * 1024 * 1024
hard_limit <- 30 * 1024 * 1024 * 1024
rlimit_as(soft_limit, hard_limit)
# Constants for configuration
plot_width <- 14
plot_height <- 9
@@ -224,23 +231,6 @@ generate_and_save_plots <- function(df, output_dir, prefix, variables, include_q
save_plots(prefix, plots, output_dir)
}
# Calculate summary statistics for all variables
calculate_summary_stats <- function(df, variables, group_vars = c("conc_num", "conc_num_factor")) {
# Calculate summary statistics with the grouping columns
summary_stats <- df %>%
group_by(across(all_of(group_vars))) %>%
summarise(across(all_of(variables), list(
mean = ~mean(.x, na.rm = TRUE),
median = ~median(.x, na.rm = TRUE),
max = ~max(.x, na.rm = TRUE),
min = ~min(.x, na.rm = TRUE),
sd = ~sd(.x, na.rm = TRUE),
se = ~sd(.x, na.rm = TRUE) / sqrt(n() - 1)
), .names = "{.col}_{.fn}"))
return(summary_stats)
}
# Ensure all plots are saved and printed to PDF
save_plots <- function(file_name, plot_list, output_dir) {
# Save to PDF
@@ -294,17 +284,34 @@ process_strains <- function(df) {
return(df_strains)
}
# Calculate summary statistics for all variables
calculate_summary_stats <- function(df, variables, group_vars = c("conc_num", "conc_num_factor")) {
# Calculate summary statistics with the grouping columns
summary_stats <- df %>%
group_by(across(all_of(group_vars))) %>%
summarise(across(all_of(variables), list(
mean = ~mean(.x, na.rm = TRUE),
median = ~median(.x, na.rm = TRUE),
max = ~max(.x, na.rm = TRUE),
min = ~min(.x, na.rm = TRUE),
sd = ~sd(.x, na.rm = TRUE),
se = ~sd(.x, na.rm = TRUE) / sqrt(n() - 1)
), .names = "{.col}_{.fn}"))
return(summary_stats)
}
calculate_interaction_scores <- function(df, max_conc, variables, group_vars = c("OrfRep", "Gene", "num")) {
calculate_interaction_scores <- function(df_ref, df, max_conc, variables, group_vars = c("OrfRep", "Gene", "num")) {
# Pull the background means
print("Calculating background means")
l_mean_bg <- df %>% filter(conc_num_factor == 0) %>% pull(L_mean)
k_mean_bg <- df %>% filter(conc_num_factor == 0) %>% pull(K_mean)
r_mean_bg <- df %>% filter(conc_num_factor == 0) %>% pull(r_mean)
auc_mean_bg <- df %>% filter(conc_num_factor == 0) %>% pull(AUC_mean)
l_mean_bg <- df_ref %>% filter(conc_num_factor == 0) %>% pull(L_mean)
k_mean_bg <- df_ref %>% filter(conc_num_factor == 0) %>% pull(K_mean)
r_mean_bg <- df_ref %>% filter(conc_num_factor == 0) %>% pull(r_mean)
auc_mean_bg <- df_ref %>% filter(conc_num_factor == 0) %>% pull(AUC_mean)
# Calculate all necessary statistics and shifts in one step
print("Calculating interaction scores part 1")
interaction_scores_all <- df %>%
group_by(across(all_of(group_vars)), conc_num, conc_num_factor) %>%
summarise(
@@ -344,6 +351,7 @@ calculate_interaction_scores <- function(df, max_conc, variables, group_vars = c
ungroup()
# Calculate linear models and interaction scores
print("Calculating interaction scores part 2")
interaction_scores <- interaction_scores_all %>%
group_by(across(all_of(group_vars))) %>%
summarise(
@@ -648,9 +656,9 @@ main <- function() {
# Calculate interactions
variables <- c("L", "K", "r", "AUC")
message("Calculating reference interaction scores")
reference_results <- calculate_interaction_scores(reference_strain, max_conc, variables)
reference_results <- calculate_interaction_scores(stats_joined, reference_strain, max_conc, variables)
message("Calculating deletion interaction scores")
deletion_results <- calculate_interaction_scores(deletion_strains, max_conc, variables)
deletion_results <- calculate_interaction_scores(stats_joined, deletion_strains, max_conc, variables)
zscores_calculations_reference <- reference_results$zscores_calculations
zscores_interactions_reference <- reference_results$zscores_interactions