Output interaction df in calculate_interaction_zscores.R
This commit is contained in:
@@ -123,7 +123,6 @@ load_and_preprocess_data <- function(easy_results_file, genes) {
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mutate(L = if ("l" %in% colnames(.)) l else {warning("Missing column: l"); NA}) %>%
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mutate(AUC = if ("AUC96" %in% colnames(.)) AUC96 else {warning("Missing column: AUC96"); NA}) %>%
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filter(!.[[1]] %in% c("", "Scan")) %>% # Filter out empty or Scan rows
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# {if ("Conc" %in% colnames(.)) filter(., Conc != "0ug/mL") else .} %>%
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filter(Gene != "BLANK" & Gene != "Blank" & ORF != "Blank" & Gene != "blank") %>% # Remove blank genes
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filter(Drug != "BMH21") %>% # Filter out specific drugs if necessary
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filter(!is.na(ORF) & ORF != "") %>% # Ensure ORF is not NA or empty
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@@ -142,8 +141,6 @@ load_and_preprocess_data <- function(easy_results_file, genes) {
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return(df)
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}
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create_and_publish_plot <- function(df, var, plot_type, out_dir_qc, suffix = "") {
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if (!("Scan" %in% colnames(df))) {
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warning("'Scan' column is not present in the data. Skipping this plot.")
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@@ -186,52 +183,103 @@ publish_summary_stats <- function(df, variables, out_dir) {
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fwrite(summary_stats_df, file.path(out_dir, "summary_stats_all_strains.csv"), row.names = FALSE)
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}
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publish_interaction_scores <- function(df, out_dir) {
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interaction_scores <- df %>%
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dplyr::group_by(OrfRep) %>%
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dplyr::summarize(
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mean_L = mean(L, na.rm = TRUE),
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mean_K = mean(K, na.rm = TRUE),
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mean_r = mean(r, na.rm = TRUE),
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mean_AUC = mean(AUC, na.rm = TRUE),
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delta_bg_mean = mean(delta_bg, na.rm = TRUE),
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delta_bg_sd = sd(delta_bg, na.rm = TRUE)
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) %>%
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dplyr::mutate(
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l_rank = rank(mean_L),
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k_rank = rank(mean_K),
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r_rank = rank(mean_r),
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auc_rank = rank(mean_AUC)
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# Compute Interaction Scores
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compute_interaction_scores <- function(df) {
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# Calculate raw shifts
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raw_shifts <- df %>%
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group_by(OrfRep) %>%
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reframe(
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Raw_Shift_L = mean(L, na.rm = TRUE),
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Raw_Shift_K = mean(K, na.rm = TRUE),
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Raw_Shift_r = mean(r, na.rm = TRUE),
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Raw_Shift_AUC = mean(AUC, na.rm = TRUE),
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NG = n()
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)
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fwrite(interaction_scores, file.path(out_dir, "rf_zscores_interaction.csv"), row.names = FALSE)
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fwrite(dplyr::arrange(interaction_scores, l_rank, k_rank),
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file.path(out_dir, "rf_zscores_interaction_ranked.csv"), row.names = FALSE)
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# Calculate Z-scores
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z_scores <- df %>%
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group_by(OrfRep) %>%
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reframe(
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Z_Shift_L = mean(scale(L, center = TRUE, scale = TRUE)[, 1], na.rm = TRUE),
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Z_Shift_K = mean(scale(K, center = TRUE, scale = TRUE)[, 1], na.rm = TRUE),
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Z_Shift_r = mean(scale(r, center = TRUE, scale = TRUE)[, 1], na.rm = TRUE),
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Z_Shift_AUC = mean(scale(AUC, center = TRUE, scale = TRUE)[, 1], na.rm = TRUE)
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)
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# Linear Model Scores and R-Squared
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lm_scores <- df %>%
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group_by(OrfRep) %>%
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reframe(
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lm_Score_L = coef(lm(L ~ delta_bg, data = .))[2],
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R_Squared_L = summary(lm(L ~ delta_bg, data = .))$r.squared,
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lm_Score_K = coef(lm(K ~ delta_bg, data = .))[2],
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R_Squared_K = summary(lm(K ~ delta_bg, data = .))$r.squared,
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lm_Score_r = coef(lm(r ~ delta_bg, data = .))[2],
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R_Squared_r = summary(lm(r ~ delta_bg, data = .))$r.squared,
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lm_Score_AUC = coef(lm(AUC ~ delta_bg, data = .))[2],
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R_Squared_AUC = summary(lm(AUC ~ delta_bg, data = .))$r.squared
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)
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# Calculate Sum and Average Z-scores
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sum_avg_z_scores <- df %>%
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group_by(OrfRep) %>%
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reframe(
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Sum_Z_Score_L = sum(scale(L, center = TRUE, scale = TRUE)[, 1], na.rm = TRUE),
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Avg_Zscore_L = mean(scale(L, center = TRUE, scale = TRUE)[, 1], na.rm = TRUE),
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Sum_Z_Score_K = sum(scale(K, center = TRUE, scale = TRUE)[, 1], na.rm = TRUE),
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Avg_Zscore_K = mean(scale(K, center = TRUE, scale = TRUE)[, 1], na.rm = TRUE),
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Sum_Z_Score_r = sum(scale(r, center = TRUE, scale = TRUE)[, 1], na.rm = TRUE),
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Avg_Zscore_r = mean(scale(r, center = TRUE, scale = TRUE)[, 1], na.rm = TRUE),
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Sum_Z_Score_AUC = sum(scale(AUC, center = TRUE, scale = TRUE)[, 1], na.rm = TRUE),
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Avg_Zscore_AUC = mean(scale(AUC, center = TRUE, scale = TRUE)[, 1], na.rm = TRUE)
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)
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# Combine all calculations
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interaction_scores <- raw_shifts %>%
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left_join(z_scores, by = "OrfRep") %>%
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left_join(lm_scores, by = "OrfRep") %>%
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left_join(sum_avg_z_scores, by = "OrfRep") %>%
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mutate(
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l_rank = rank(-Raw_Shift_L),
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k_rank = rank(-Raw_Shift_K),
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r_rank = rank(-Raw_Shift_r),
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auc_rank = rank(-Raw_Shift_AUC)
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)
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return(interaction_scores)
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}
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publish_interaction_scores <- function(df, out_dir) {
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interaction_scores <- compute_interaction_scores(df)
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# Additional enhancer and suppressor calculations and outputs
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deletion_enhancers_L <- interaction_scores[interaction_scores$mean_L >= 2, ]
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deletion_enhancers_L <- interaction_scores[interaction_scores$Raw_Shift_L >= 2, ]
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deletion_enhancers_L <- deletion_enhancers_L[!is.na(deletion_enhancers_L$OrfRep), ]
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deletion_enhancers_K <- interaction_scores[interaction_scores$mean_K <= -2, ]
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deletion_enhancers_K <- interaction_scores[interaction_scores$Raw_Shift_K <= -2, ]
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deletion_enhancers_K <- deletion_enhancers_K[!is.na(deletion_enhancers_K$OrfRep), ]
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deletion_suppressors_L <- interaction_scores[interaction_scores$mean_L <= -2, ]
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deletion_suppressors_L <- interaction_scores[interaction_scores$Raw_Shift_L <= -2, ]
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deletion_suppressors_L <- deletion_suppressors_L[!is.na(deletion_suppressors_L$OrfRep), ]
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deletion_suppressors_K <- interaction_scores[interaction_scores$mean_K >= 2, ]
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deletion_suppressors_K <- interaction_scores[interaction_scores$Raw_Shift_K >= 2, ]
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deletion_suppressors_K <- deletion_suppressors_K[!is.na(deletion_suppressors_K$OrfRep), ]
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deletion_enhancers_and_suppressors_L <- interaction_scores[
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interaction_scores$mean_L >= 2 | interaction_scores$mean_L <= -2, ]
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interaction_scores$Raw_Shift_L >= 2 | interaction_scores$Raw_Shift_L <= -2, ]
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deletion_enhancers_and_suppressors_L <- deletion_enhancers_and_suppressors_L[
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!is.na(deletion_enhancers_and_suppressors_L$OrfRep), ]
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deletion_enhancers_and_suppressors_K <- interaction_scores[
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interaction_scores$mean_K >= 2 | interaction_scores$mean_K <= -2, ]
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interaction_scores$Raw_Shift_K >= 2 | interaction_scores$Raw_Shift_K <= -2, ]
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deletion_enhancers_and_suppressors_K <- deletion_enhancers_and_suppressors_K[
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!is.na(deletion_enhancers_and_suppressors_K$OrfRep), ]
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# Write CSV files with updated filenames
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fwrite(interaction_scores, file.path(out_dir, "rf_zscores_interaction.csv"), row.names = FALSE)
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fwrite(dplyr::arrange(interaction_scores, l_rank, k_rank),
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file.path(out_dir, "rf_zscores_interaction_ranked.csv"), row.names = FALSE)
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fwrite(deletion_enhancers_L, file.path(out_dir, "deletion_enhancers_l.csv"), row.names = FALSE)
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fwrite(deletion_enhancers_K, file.path(out_dir, "deletion_enhancers_k.csv"), row.names = FALSE)
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fwrite(deletion_suppressors_L, file.path(out_dir, "deletion_suppressors_l.csv"), row.names = FALSE)
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@@ -239,23 +287,22 @@ publish_interaction_scores <- function(df, out_dir) {
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fwrite(deletion_enhancers_and_suppressors_L, file.path(out_dir, "deletion_enhancers_and_suppressors_l.csv"), row.names = FALSE)
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fwrite(deletion_enhancers_and_suppressors_K, file.path(out_dir, "deletion_enhancers_and_suppressors_k.csv"), row.names = FALSE)
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return(interaction_scores) # Return the updated data frame with rank columns
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return(interaction_scores) # Return the updated data frame with all calculated columns
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}
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publish_zscores <- function(df, out_dir) {
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zscores <- df %>%
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dplyr::mutate(
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zscore_L = scale(L, center = TRUE, scale = TRUE),
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zscore_K = scale(K, center = TRUE, scale = TRUE),
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zscore_r = scale(r, center = TRUE, scale = TRUE),
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zscore_AUC = scale(AUC, center = TRUE, scale = TRUE)
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zscore_L = scale(Raw_Shift_L, center = TRUE, scale = TRUE),
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zscore_K = scale(Raw_Shift_K, center = TRUE, scale = TRUE),
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zscore_r = scale(Raw_Shift_r, center = TRUE, scale = TRUE),
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zscore_AUC = scale(Raw_Shift_AUC, center = TRUE, scale = TRUE)
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)
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fwrite(zscores, file.path(out_dir, "zscores_interaction.csv"), row.names = FALSE)
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}
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generate_and_publish_qc <- function(df, delta_bg_tolerance, out_dir_qc) {
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variables <- c("L", "K", "r", "AUC", "delta_bg")
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@@ -287,9 +334,6 @@ generate_and_publish_qc <- function(df, delta_bg_tolerance, out_dir_qc) {
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})
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}
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# Create rank plots
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create_rank_plots <- function(interaction_scores, out_dir) {
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rank_vars <- c("l_rank", "k_rank", "r_rank", "auc_rank")
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@@ -319,12 +363,12 @@ create_correlation_plot <- function(interaction_scores, out_dir) {
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# Generate correlation plots for each pair of variables
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pairs <- list(
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c("mean_L", "mean_K"),
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c("mean_L", "mean_r"),
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c("mean_L", "mean_AUC"),
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c("mean_K", "mean_r"),
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c("mean_K", "mean_AUC"),
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c("mean_r", "mean_AUC")
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c("Raw_Shift_L", "Raw_Shift_K"),
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c("Raw_Shift_L", "Raw_Shift_r"),
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c("Raw_Shift_L", "Raw_Shift_AUC"),
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c("Raw_Shift_K", "Raw_Shift_r"),
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c("Raw_Shift_K", "Raw_Shift_AUC"),
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c("Raw_Shift_r", "Raw_Shift_AUC")
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)
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lapply(pairs, function(vars) {
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@@ -347,7 +391,6 @@ create_correlation_plot <- function(interaction_scores, out_dir) {
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})
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}
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process_experiment <- function(exp_name, exp_dir, genes, output_dir) {
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out_dir <- file.path(exp_dir, "zscores")
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out_dir_qc <- file.path(out_dir, "qc")
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@@ -367,14 +410,14 @@ process_experiment <- function(exp_name, exp_dir, genes, output_dir) {
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variables <- c("L", "K", "r", "AUC", "delta_bg")
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publish_summary_stats(data, variables, out_dir)
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interaction_scores <- publish_interaction_scores(data, out_dir)
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publish_zscores(data, out_dir)
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publish_zscores(interaction_scores, out_dir) # Now writing interaction_scores, not original data
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# Generate rank plots and correlation plots
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create_rank_plots(interaction_scores, out_dir)
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create_correlation_plot(interaction_scores, out_dir)
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output_file <- file.path(out_dir, "zscores_interaction.csv")
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fwrite(data, output_file, row.names = FALSE)
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fwrite(interaction_scores, output_file, row.names = FALSE) # Write interaction_scores here
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return(output_file)
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}
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@@ -390,6 +433,224 @@ if (length(processed_files) > 1) {
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merge(fread(x), fread(y), by = "OrfRep", all = TRUE, allow.cartesian = TRUE)
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}, processed_files)
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combined_output_file <- file.path(args$out_dir, "zscores", "zscores_interaction_combined.csv")
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fwrite(combined_data, combined_output_file, row.names = FALSE)
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combined_output_file <- file.path(args$out_dir, "zscores", "zscores_interaction_combined.csv")
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fwrite(combined_data, combined_output_file, row.names = FALSE)
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}
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# publish_interaction_scores <- function(df, out_dir) {
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# interaction_scores <- df %>%
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# dplyr::group_by(OrfRep) %>%
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# dplyr::summarize(
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# mean_L = mean(L, na.rm = TRUE),
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# mean_K = mean(K, na.rm = TRUE),
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# mean_r = mean(r, na.rm = TRUE),
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# mean_AUC = mean(AUC, na.rm = TRUE),
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# delta_bg_mean = mean(delta_bg, na.rm = TRUE),
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# delta_bg_sd = sd(delta_bg, na.rm = TRUE)
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# ) %>%
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# dplyr::mutate(
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# l_rank = rank(mean_L),
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# k_rank = rank(mean_K),
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# r_rank = rank(mean_r),
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# auc_rank = rank(mean_AUC)
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# )
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# fwrite(interaction_scores, file.path(out_dir, "rf_zscores_interaction.csv"), row.names = FALSE)
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# fwrite(dplyr::arrange(interaction_scores, l_rank, k_rank),
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# file.path(out_dir, "rf_zscores_interaction_ranked.csv"), row.names = FALSE)
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# # Additional enhancer and suppressor calculations and outputs
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# deletion_enhancers_L <- interaction_scores[interaction_scores$mean_L >= 2, ]
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# deletion_enhancers_L <- deletion_enhancers_L[!is.na(deletion_enhancers_L$OrfRep), ]
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# deletion_enhancers_K <- interaction_scores[interaction_scores$mean_K <= -2, ]
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# deletion_enhancers_K <- deletion_enhancers_K[!is.na(deletion_enhancers_K$OrfRep), ]
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# deletion_suppressors_L <- interaction_scores[interaction_scores$mean_L <= -2, ]
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# deletion_suppressors_L <- deletion_suppressors_L[!is.na(deletion_suppressors_L$OrfRep), ]
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# deletion_suppressors_K <- interaction_scores[interaction_scores$mean_K >= 2, ]
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# deletion_suppressors_K <- deletion_suppressors_K[!is.na(deletion_suppressors_K$OrfRep), ]
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# deletion_enhancers_and_suppressors_L <- interaction_scores[
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# interaction_scores$mean_L >= 2 | interaction_scores$mean_L <= -2, ]
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# deletion_enhancers_and_suppressors_L <- deletion_enhancers_and_suppressors_L[
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# !is.na(deletion_enhancers_and_suppressors_L$OrfRep), ]
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# deletion_enhancers_and_suppressors_K <- interaction_scores[
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# interaction_scores$mean_K >= 2 | interaction_scores$mean_K <= -2, ]
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# deletion_enhancers_and_suppressors_K <- deletion_enhancers_and_suppressors_K[
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# !is.na(deletion_enhancers_and_suppressors_K$OrfRep), ]
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# # Write CSV files with updated filenames
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# fwrite(deletion_enhancers_L, file.path(out_dir, "deletion_enhancers_l.csv"), row.names = FALSE)
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# fwrite(deletion_enhancers_K, file.path(out_dir, "deletion_enhancers_k.csv"), row.names = FALSE)
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# fwrite(deletion_suppressors_L, file.path(out_dir, "deletion_suppressors_l.csv"), row.names = FALSE)
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# fwrite(deletion_suppressors_K, file.path(out_dir, "deletion_suppressors_k.csv"), row.names = FALSE)
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# fwrite(deletion_enhancers_and_suppressors_L, file.path(out_dir, "deletion_enhancers_and_suppressors_l.csv"), row.names = FALSE)
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# fwrite(deletion_enhancers_and_suppressors_K, file.path(out_dir, "deletion_enhancers_and_suppressors_k.csv"), row.names = FALSE)
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# return(interaction_scores) # Return the updated data frame with rank columns
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# }
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# publish_zscores <- function(df, out_dir) {
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# zscores <- df %>%
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# dplyr::mutate(
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# zscore_L = scale(L, center = TRUE, scale = TRUE),
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# zscore_K = scale(K, center = TRUE, scale = TRUE),
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# zscore_r = scale(r, center = TRUE, scale = TRUE),
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# zscore_AUC = scale(AUC, center = TRUE, scale = TRUE)
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# )
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# fwrite(zscores, file.path(out_dir, "zscores_interaction.csv"), row.names = FALSE)
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# }
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# generate_and_publish_qc <- function(df, delta_bg_tolerance, out_dir_qc) {
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# variables <- c("L", "K", "r", "AUC", "delta_bg")
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# # Pre-QC plots
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# lapply(variables, function(var) {
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# if (var %in% colnames(df)) {
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# create_and_publish_plot(df, var, "scatter", out_dir_qc)
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# }
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# })
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# # Filter data based on delta background tolerance for Post-QC analysis
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# df_post_qc <- df %>%
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# mutate(across(all_of(variables),
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# ~ ifelse(delta_bg >= delta_bg_tolerance, NA, .)))
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# # Post-QC plots
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# lapply(variables, function(var) {
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# if (var %in% colnames(df_post_qc)) {
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# create_and_publish_plot(df_post_qc, var, "scatter", out_dir_qc, suffix = "_after_qc")
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# }
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# })
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# # For plots specifically for data above the tolerance threshold
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# delta_bg_above_tolerance <- df[df$delta_bg >= delta_bg_tolerance, ]
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# lapply(variables, function(var) {
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# if (var %in% colnames(delta_bg_above_tolerance)) {
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# create_and_publish_plot(delta_bg_above_tolerance, var, "scatter", out_dir_qc, suffix = "_above_tolerance")
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# }
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# })
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# }
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# # Create rank plots
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# create_rank_plots <- function(interaction_scores, out_dir) {
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# rank_vars <- c("l_rank", "k_rank", "r_rank", "auc_rank")
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# lapply(rank_vars, function(rank_var) {
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# p <- ggplot(interaction_scores, aes(x = !!sym(rank_var))) +
|
||||
# geom_bar() +
|
||||
# ggtitle(paste("Rank Distribution for", rank_var)) +
|
||||
# theme_publication()
|
||||
|
||||
# pdf_path <- file.path(out_dir, paste0("rank_distribution_", rank_var, ".pdf"))
|
||||
# pdf(pdf_path, width = PLOT_WIDTH, height = PLOT_HEIGHT)
|
||||
# print(p)
|
||||
# dev.off()
|
||||
|
||||
# # Generate HTML output
|
||||
# html_path <- sub(".pdf$", ".html", pdf_path)
|
||||
# pgg <- suppressWarnings(ggplotly(p) %>%
|
||||
# layout(legend = list(orientation = "h")))
|
||||
# saveWidget(pgg, html_path, selfcontained = TRUE)
|
||||
# })
|
||||
# }
|
||||
|
||||
# create_correlation_plot <- function(interaction_scores, out_dir) {
|
||||
# # Check for non-finite values and remove them from the dataset
|
||||
# interaction_scores <- interaction_scores %>%
|
||||
# filter_all(all_vars(is.finite(.)))
|
||||
|
||||
# # Generate correlation plots for each pair of variables
|
||||
# pairs <- list(
|
||||
# c("mean_L", "mean_K"),
|
||||
# c("mean_L", "mean_r"),
|
||||
# c("mean_L", "mean_AUC"),
|
||||
# c("mean_K", "mean_r"),
|
||||
# c("mean_K", "mean_AUC"),
|
||||
# c("mean_r", "mean_AUC")
|
||||
# )
|
||||
|
||||
# lapply(pairs, function(vars) {
|
||||
# p <- ggplot(interaction_scores, aes(x = !!sym(vars[1]), y = !!sym(vars[2]))) +
|
||||
# geom_point() +
|
||||
# geom_smooth(method = "lm", se = FALSE) +
|
||||
# ggtitle(paste("Correlation between", vars[1], "and", vars[2])) +
|
||||
# theme_publication()
|
||||
|
||||
# pdf_path <- file.path(out_dir, paste0("correlation_", vars[1], "_", vars[2], ".pdf"))
|
||||
# pdf(pdf_path, width = PLOT_WIDTH, height = PLOT_HEIGHT)
|
||||
# print(p)
|
||||
# dev.off()
|
||||
|
||||
# # Generate HTML output
|
||||
# html_path <- sub(".pdf$", ".html", pdf_path)
|
||||
# pgg <- suppressWarnings(ggplotly(p, tooltip = c(vars[1], vars[2])) %>%
|
||||
# layout(legend = list(orientation = "h")))
|
||||
# saveWidget(pgg, html_path, selfcontained = TRUE)
|
||||
# })
|
||||
# }
|
||||
|
||||
|
||||
# process_experiment <- function(exp_name, exp_dir, genes, output_dir) {
|
||||
# out_dir <- file.path(exp_dir, "zscores")
|
||||
# out_dir_qc <- file.path(out_dir, "qc")
|
||||
# dir.create(out_dir, showWarnings = FALSE, recursive = TRUE)
|
||||
# dir.create(out_dir_qc, showWarnings = FALSE)
|
||||
|
||||
# # Load and preprocess the data
|
||||
# data <- load_and_preprocess_data(args$easy_results_file, genes)
|
||||
|
||||
# # Calculate delta background tolerance
|
||||
# delta_bg_tolerance <- mean(data$delta_bg, na.rm = TRUE) + 3 * sd(data$delta_bg, na.rm = TRUE)
|
||||
|
||||
# # Generate and publish QC plots (both pre-QC and post-QC)
|
||||
# generate_and_publish_qc(data, delta_bg_tolerance, out_dir_qc)
|
||||
|
||||
# # Process and publish summary stats, interaction scores, and z-scores
|
||||
# variables <- c("L", "K", "r", "AUC", "delta_bg")
|
||||
# publish_summary_stats(data, variables, out_dir)
|
||||
# interaction_scores <- publish_interaction_scores(data, out_dir)
|
||||
# publish_zscores(data, out_dir)
|
||||
|
||||
# # Generate rank plots and correlation plots
|
||||
# create_rank_plots(interaction_scores, out_dir)
|
||||
# create_correlation_plot(interaction_scores, out_dir)
|
||||
|
||||
# output_file <- file.path(out_dir, "zscores_interaction.csv")
|
||||
# fwrite(data, output_file, row.names = FALSE)
|
||||
|
||||
# return(output_file)
|
||||
# }
|
||||
|
||||
# # Process all experiments
|
||||
# processed_files <- lapply(names(args$experiments), function(exp_name) {
|
||||
# process_experiment(exp_name, args$experiments[[exp_name]], genes, args$out_dir)
|
||||
# })
|
||||
|
||||
# # Combine results from all experiments if multiple experiments exist
|
||||
# if (length(processed_files) > 1) {
|
||||
# combined_data <- Reduce(function(x, y) {
|
||||
# merge(fread(x), fread(y), by = "OrfRep", all = TRUE, allow.cartesian = TRUE)
|
||||
# }, processed_files)
|
||||
|
||||
# combined_output_file <- file.path(args$out_dir, "zscores", "zscores_interaction_combined.csv")
|
||||
# fwrite(combined_data, combined_output_file, row.names = FALSE)
|
||||
# }
|
||||
Reference in New Issue
Block a user