Fix dataframe joining dimensionality
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@@ -212,7 +212,7 @@ calculate_summary_stats <- function(df, variables, group_vars) {
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}
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calculate_interaction_scores <- function(df, max_conc, bg_stats, group_vars, overlap_threshold = 2) {
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# Calculate total concentration variables
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total_conc_num <- length(unique(df$conc_num))
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@@ -290,7 +290,7 @@ calculate_interaction_scores <- function(df, max_conc, bg_stats, group_vars, ove
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}) %>%
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ungroup()
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# Continue with the rest of the function as before
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# Summary statistics for lm scores
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lm_means_sds <- calculations %>%
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group_by(across(all_of(group_vars))) %>%
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summarise(
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@@ -314,7 +314,7 @@ calculate_interaction_scores <- function(df, max_conc, bg_stats, group_vars, ove
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Z_lm_r = (lm_Score_r - mean_lm_r) / sd_lm_r,
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Z_lm_AUC = (lm_Score_AUC - mean_lm_AUC) / sd_lm_AUC
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)
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# Build summary stats (interactions)
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interactions <- calculations %>%
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group_by(across(all_of(group_vars))) %>%
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@@ -340,32 +340,51 @@ calculate_interaction_scores <- function(df, max_conc, bg_stats, group_vars, ove
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Z_Shift_L = first(Z_Shift_L),
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Z_Shift_K = first(Z_Shift_K),
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Z_Shift_r = first(Z_Shift_r),
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Z_Shift_AUC = first(Z_Shift_AUC)
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Z_Shift_AUC = first(Z_Shift_AUC),
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) %>%
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arrange(desc(Z_lm_L), desc(NG)) %>%
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ungroup() %>%
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mutate(
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Overlap = case_when(
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Z_lm_L >= overlap_threshold & Avg_Zscore_L >= overlap_threshold ~ "Deletion Enhancer Both",
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Z_lm_L <= -overlap_threshold & Avg_Zscore_L <= -overlap_threshold ~ "Deletion Suppressor Both",
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Z_lm_L >= overlap_threshold & Avg_Zscore_L < overlap_threshold ~ "Deletion Enhancer lm only",
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Z_lm_L < overlap_threshold & Avg_Zscore_L >= overlap_threshold ~ "Deletion Enhancer Avg Zscore only",
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Z_lm_L <= -overlap_threshold & Avg_Zscore_L > -overlap_threshold ~ "Deletion Suppressor lm only",
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Z_lm_L > -overlap_threshold & Avg_Zscore_L <= -overlap_threshold ~ "Deletion Suppressor Avg Zscore only",
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Z_lm_L >= overlap_threshold & Avg_Zscore_L <= -overlap_threshold ~ "Deletion Enhancer lm, Deletion Suppressor Avg Z score",
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Z_lm_L <= -overlap_threshold & Avg_Zscore_L >= overlap_threshold ~ "Deletion Suppressor lm, Deletion Enhancer Avg Z score",
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TRUE ~ "No Effect"
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)
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# NG, DB, SM values
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NG = first(NG),
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DB = first(DB),
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SM = first(SM)
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)
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# Return full data and correlation stats
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# Creating the final calculations and interactions dataframes with only required columns for csv output
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calculations_df <- calculations %>%
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select(
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all_of(group_vars),
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conc_num, conc_num_factor, conc_num_factor_factor,
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N, NG, DB, SM,
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mean_L, median_L, sd_L, se_L,
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mean_K, median_K, sd_K, se_K,
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mean_r, median_r, sd_r, se_r,
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mean_AUC, median_AUC, sd_AUC, se_AUC,
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Raw_Shift_L, Raw_Shift_K, Raw_Shift_r, Raw_Shift_AUC,
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Z_Shift_L, Z_Shift_K, Z_Shift_r, Z_Shift_AUC,
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WT_L, WT_K, WT_r, WT_AUC,
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WT_sd_L, WT_sd_K, WT_sd_r, WT_sd_AUC,
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Exp_L, Exp_K, Exp_r, Exp_AUC,
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Delta_L, Delta_K, Delta_r, Delta_AUC,
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Zscore_L, Zscore_K, Zscore_r, Zscore_AUC
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)
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interactions_df <- interactions %>%
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select(
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all_of(group_vars),
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NG, DB, SM,
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Avg_Zscore_L, Avg_Zscore_K, Avg_Zscore_r, Avg_Zscore_AUC,
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Z_lm_L, Z_lm_K, Z_lm_r, Z_lm_AUC,
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Raw_Shift_L, Raw_Shift_K, Raw_Shift_r, Raw_Shift_AUC,
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Z_Shift_L, Z_Shift_K, Z_Shift_r, Z_Shift_AUC
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)
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# Use left_join to avoid dimension mismatch issues
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full_data <- calculations %>%
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left_join(interactions, by = group_vars)
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# Return full_data and the two required dataframes (calculations and interactions)
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return(list(
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calculations = calculations,
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interactions = interactions,
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calculations = calculations_df,
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interactions = interactions_df,
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full_data = full_data
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))
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}
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