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Split out simple vars in interaction scores

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Bryan Roessler
2024-09-05 21:17:56 -04:00
parent 5ba669f880
commit 40e0909a1e
1 changed files with 9 additions and 7 deletions
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14
workflow/apps/r/calculate_interaction_zscores5.R
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@@ -230,17 +230,20 @@ calculate_interaction_scores <- function(df, max_conc, variables, group_vars = c
bg_sd_r <- df %>% filter(conc_num_factor == 0) %>% pull(sd_r) %>% first() bg_sd_r <- df %>% filter(conc_num_factor == 0) %>% pull(sd_r) %>% first()
bg_sd_AUC <- df %>% filter(conc_num_factor == 0) %>% pull(sd_AUC) %>% first() bg_sd_AUC <- df %>% filter(conc_num_factor == 0) %>% pull(sd_AUC) %>% first()
# Calculate interaction scores # First mutate block to calculate NG, DB, SM
print("Calculating interaction scores part 1") print("Calculating interaction scores part 1")
interaction_scores <- df %>% interaction_scores <- df %>%
group_by(across(all_of(group_vars)), conc_num, conc_num_factor) %>% group_by(across(all_of(group_vars)), conc_num, conc_num_factor) %>%
mutate( mutate(
NG = sum(NG, na.rm = TRUE), NG = sum(NG, na.rm = TRUE),
DB = sum(DB, na.rm = TRUE), DB = sum(DB, na.rm = TRUE),
SM = sum(SM, na.rm = TRUE) SM = sum(SM, na.rm = TRUE),
) %>% N = ~sum(!is.na(L)), # Count of non-NA values
)
# Second mutate block to calculate variables and Delta using NG
interaction_scores <- interaction_scores %>%
mutate(across(all_of(variables), list( mutate(across(all_of(variables), list(
N = ~sum(!is.na(.)), # Count of non-NA values
mean = ~mean(., na.rm = TRUE), mean = ~mean(., na.rm = TRUE),
median = ~median(., na.rm = TRUE), median = ~median(., na.rm = TRUE),
max = ~max(., na.rm = TRUE), max = ~max(., na.rm = TRUE),
@@ -296,6 +299,7 @@ calculate_interaction_scores <- function(df, max_conc, variables, group_vars = c
return(list(zscores_calculations = interaction_scores_all, zscores_interactions = interaction_scores)) return(list(zscores_calculations = interaction_scores_all, zscores_interactions = interaction_scores))
} }
interaction_plot_configs <- function(df, variable) { interaction_plot_configs <- function(df, variable) {
ylim_vals <- switch(variable, ylim_vals <- switch(variable,
"L" = c(-65, 65), "L" = c(-65, 65),
@@ -715,8 +719,6 @@ main <- function() {
reference_strain <- process_strains(df_reference) # TODO double-check reference_strain <- process_strains(df_reference) # TODO double-check
deletion_strains <- process_strains(df_deletion) # TODO double-check deletion_strains <- process_strains(df_deletion) # TODO double-check
# TODO we may need to add "num" to grouping vars
# Calculate interactions # Calculate interactions
variables <- c("L", "K", "r", "AUC") variables <- c("L", "K", "r", "AUC")
# We are recalculating some of the data here # We are recalculating some of the data here