Implement calculate_interaction_zscores.R
This commit is contained in:
@@ -1,10 +1,10 @@
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suppressMessages({
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if (!require("ggplot2")) stop("Package ggplot2 is required but not installed.")
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if (!require("plotly")) stop("Package plotly is required but not installed.")
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if (!require("htmlwidgets")) stop("Package htmlwidgets is required but not installed.")
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if (!require("dplyr")) stop("Package dplyr is required but not installed.")
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if (!require("ggthemes")) stop("Package ggthemes is required but not installed.")
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if (!require("plyr")) stop("Package plyr is required but not installed.")
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library("ggplot2")
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library("plotly")
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library("htmlwidgets")
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library("dplyr")
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library("ggthemes")
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library("data.table")
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})
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# Constants for configuration
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@@ -14,9 +14,10 @@ BASE_SIZE <- 14
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options(warn = 2, max.print = 100)
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# Function to parse arguments
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parse_arguments <- function() {
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args <- if (interactive()) {
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c(
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if (interactive()) {
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args <- c(
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"/home/bryan/documents/develop/scripts/hartmanlab/workflow/out/20240116_jhartman2_DoxoHLD/20240116_jhartman2_DoxoHLD",
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3,
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"/home/bryan/documents/develop/scripts/hartmanlab/workflow/apps/r/SGD_features.tab",
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@@ -24,14 +25,10 @@ parse_arguments <- function() {
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"/home/bryan/documents/develop/scripts/hartmanlab/workflow/out/20240116_jhartman2_DoxoHLD/20240822_jhartman2_DoxoHLD/exp1",
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"Experiment 1: Doxo versus HLD",
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"/home/bryan/documents/develop/scripts/hartmanlab/workflow/out/20240116_jhartman2_DoxoHLD/20240822_jhartman2_DoxoHLD/exp2",
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"Experiment 2: HLD versus Doxo",
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"/home/bryan/documents/develop/scripts/hartmanlab/workflow/out/20240116_jhartman2_DoxoHLD/20240822_jhartman2_DoxoHLD/exp3",
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"Experiment 3: HLD versus WT",
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"/home/bryan/documents/develop/scripts/hartmanlab/workflow/out/20240116_jhartman2_DoxoHLD/20240822_jhartman2_DoxoHLD/exp4",
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"Experiment 4: Doxo versus WT"
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"Experiment 2: HLD versus Doxo"
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)
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} else {
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commandArgs(trailingOnly = TRUE)
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args <- commandArgs(trailingOnly = TRUE)
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}
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paths <- normalizePath(file.path(args[seq(5, length(args), by = 2)]), mustWork = FALSE)
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@@ -49,7 +46,9 @@ parse_arguments <- function() {
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args <- parse_arguments()
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dir.create(args$out_dir, showWarnings = FALSE)
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# Ensure main output directories exist
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dir.create(file.path(args$out_dir, "zscores"), showWarnings = FALSE)
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dir.create(file.path(args$out_dir, "zscores", "qc"), showWarnings = FALSE)
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# Define themes and scales
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theme_publication <- function(base_size = BASE_SIZE, base_family = "sans") {
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@@ -110,18 +109,26 @@ sgd_genes <- function(sgd_gene_list) {
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genes <- sgd_genes(args$sgd_gene_list)
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load_and_preprocess_data <- function(easy_results_file, genes) {
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# Attempt to read the file and handle any errors that occur
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df <- tryCatch({
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read.delim(easy_results_file, skip = 2, as.is = TRUE, row.names = 1, strip.white = TRUE)
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}, error = function(e) {
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stop("Error reading file: ", easy_results_file, "\n", e$message)
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}) %>%
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filter(!.[[1]] %in% c("", "Scan")) # Fixed syntax
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numeric_columns <- c("Col", "Row", "l", "K", "r", "Scan", "AUC96", "LstBackgrd", "X1stBackgrd")
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df[numeric_columns[numeric_columns %in% colnames(df)]] <-
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lapply(df[numeric_columns[numeric_columns %in% colnames(df)]], as.numeric)
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})
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# Filter out non-data rows
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df <- df %>%
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filter(!.[[1]] %in% c("", "Scan"))
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# Convert specified columns to numeric
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numeric_columns <- c("Col", "Row", "l", "K", "r", "Scan", "AUC96", "LstBackgrd", "X1stBackgrd")
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df[numeric_columns[numeric_columns %in% colnames(df)]] <-
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lapply(df[numeric_columns[numeric_columns %in% colnames(df)]], as.numeric)
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# Further filter and mutate the data
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df <- df %>%
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filter(!(Scan %in% c("", "Scan"))) %>%
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{if ("Conc" %in% colnames(.)) filter(., Conc != "0ug/mL") else .} %>%
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mutate(
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L = if ("l" %in% colnames(.)) l else {warning("Missing column: l"); NA},
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AUC = if ("AUC96" %in% colnames(.)) AUC96 else {warning("Missing column: AUC96"); NA},
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@@ -131,17 +138,20 @@ load_and_preprocess_data <- function(easy_results_file, genes) {
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GeneName = vapply(ORF, function(orf) {
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gene_name <- genes %>% filter(ORF == orf) %>% pull(GeneName)
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ifelse(is.null(gene_name) || gene_name == "" || length(gene_name) == 0, orf, gene_name)
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}, character(1)) # Ensures a character vector is returned
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}, character(1))
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)
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# Check if the dataframe is empty after filtering
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if (nrow(df) == 0) warning("Dataframe is empty after filtering")
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return(df)
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}
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# Plot creation and publication
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create_and_publish_plot <- function(df, var, plot_type, out_dir_qc, suffix = "") {
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plot_func <- if (plot_type == "scatter") geom_point else geom_boxplot
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filtered_df <- filter(df, is.finite(.data[[var]]))
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filtered_df <- df[is.finite(df[[var]]), ]
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p <- ggplot(filtered_df, aes(Scan, .data[[var]], color = as.factor(conc_num))) +
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plot_func(shape = 3, size = 0.2, position = "jitter") +
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@@ -161,27 +171,27 @@ create_and_publish_plot <- function(df, var, plot_type, out_dir_qc, suffix = "")
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saveWidget(pgg, html_path, selfcontained = TRUE)
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}
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# Summary statistics publication
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publish_summary_stats <- function(df, variables, out_dir) {
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stats_list <- lapply(variables, function(var) {
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df %>%
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dplyr::group_by(OrfRep, conc_num) %>%
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dplyr::summarize(
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N = dplyr::n(), # Ensure that the correct version of n() is used
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N = dplyr::n(),
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mean_val = mean(.data[[var]], na.rm = TRUE),
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sd_val = sd(.data[[var]], na.rm = TRUE),
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se_val = sd_val / sqrt(N)
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)
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})
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summary_stats_df <- dplyr::bind_rows(stats_list, .id = "variable")
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write.csv(summary_stats_df, file.path(out_dir, "summary_stats_all_strains.csv"), row.names = FALSE)
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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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# Interaction scores publication
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publish_interaction_scores <- function(df, out_dir) {
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interaction_scores <- df %>%
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group_by(OrfRep) %>%
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summarize(
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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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@@ -189,147 +199,75 @@ publish_interaction_scores <- function(df, out_dir) {
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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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mutate(
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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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write.csv(interaction_scores, file.path(out_dir, "rf_zscores_interaction.csv"), row.names = FALSE)
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write.csv(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(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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}
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# Z-scores publication
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publish_zscores <- function(df, out_dir) {
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zscores <- df %>%
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mutate(
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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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write.csv(zscores, file.path(out_dir, "zscores_interaction.csv"), row.names = FALSE)
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fwrite(zscores, file.path(out_dir, "zscores_interaction.csv"), row.names = FALSE)
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}
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# QC generation and publication
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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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lapply(variables, create_and_publish_plot, df = df, plot_type = "scatter", out_dir_qc = out_dir_qc)
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delta_bg_above_tolerance <- filter(df, delta_bg >= delta_bg_tolerance)
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delta_bg_above_tolerance <- df[df$delta_bg >= delta_bg_tolerance, ]
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lapply(variables, create_and_publish_plot, df = delta_bg_above_tolerance,
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plot_type = "scatter", out_dir_qc = out_dir_qc, suffix = "_above_tolerance")
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}
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process_exp_dir <- function(exp_dir, exp_name, genes, easy_results_file) {
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# Process experiments
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process_experiment <- function(exp_name, exp_dir, sgd_genes, output_dir) {
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out_dir <- file.path(exp_dir, "zscores")
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out_dir_qc <- file.path(exp_dir, "qc")
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dir.create(out_dir, showWarnings = FALSE)
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out_dir_qc <- file.path(out_dir, "qc")
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dir.create(out_dir, showWarnings = FALSE, recursive = TRUE)
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dir.create(out_dir_qc, showWarnings = FALSE)
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df <- load_and_preprocess_data(easy_results_file, genes)
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delta_bg_tolerance <- mean(df$delta_bg, na.rm = TRUE) + 3 * sd(df$delta_bg, na.rm = TRUE)
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generate_and_publish_qc(df, delta_bg_tolerance, out_dir_qc)
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data <- load_and_preprocess_data(args$easy_results_file, sgd_genes)
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delta_bg_tolerance <- mean(data$delta_bg, na.rm = TRUE) + 3 * sd(data$delta_bg, na.rm = TRUE)
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generate_and_publish_qc(data, delta_bg_tolerance, out_dir_qc)
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variables <- c("L", "K", "r", "AUC", "delta_bg")
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publish_summary_stats(df, variables, out_dir)
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publish_interaction_scores(df, out_dir)
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publish_zscores(df, out_dir)
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publish_summary_stats(data, variables, out_dir)
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publish_interaction_scores(data, out_dir)
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publish_zscores(data, out_dir)
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list(
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zscores_file = file.path(out_dir, "zscores_interaction.csv"),
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exp_name = exp_name
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)
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output_file <- file.path(out_dir, "zscores_interaction.csv")
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fwrite(data, output_file, row.names = FALSE)
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return(output_file)
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}
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processed_experiments <- lapply(names(args$experiments), function(exp_name) {
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process_exp_dir(args$experiments[[exp_name]], exp_name, genes, args$easy_results_file)
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# Process all experiments
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processed_files <- lapply(names(args$experiments), function(exp_name) {
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process_experiment(exp_name, args$experiments[[exp_name]], genes, args$out_dir)
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})
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zscores_files <- sapply(processed_experiments, `[[`, "zscores_file")
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exp_names <- sapply(processed_experiments, `[[`, "exp_name")
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combine_zscores <- function(zscores_files) {
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if (length(zscores_files) < 2) stop("Not enough experiments to compare, exiting script")
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# Combine results from all experiments if multiple experiments exist
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if (length(processed_files) > 1) {
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combined_data <- Reduce(function(x, y) {
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merge(fread(x), fread(y), by = "OrfRep", all = TRUE, allow.cartesian = TRUE)
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}, processed_files)
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joined_data <- read.csv(file = zscores_files[1], stringsAsFactors = FALSE)
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for (file in zscores_files[-1]) {
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temp_data <- read.csv(file = file, stringsAsFactors = FALSE)
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joined_data <- plyr::join(joined_data, temp_data, by = "OrfRep", type = "full")
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}
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joined_data
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combined_output_file <- file.path(args$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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process_combined_zscores <- function(joined_data, sd, out_dir, exp_names) {
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ordered_data <- joined_data %>%
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select(contains("OrfRep"), matches("Gene"),
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contains("z_lm_k"), contains("z_shift_k"),
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contains("z_lm_l"), contains("z_shift_l")) %>%
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arrange(contains("OrfRep"))
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clean_headers <- function(data, suffixes) {
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suffixes_to_remove <- paste0("Gene.", seq_len(suffixes))
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select(data, -all_of(suffixes_to_remove))
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}
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headSel <- clean_headers(ordered_data, length(zscores_files) - 1)
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headSel2 <- clean_headers(select(joined_data, contains("OrfRep"), matches("Gene")), length(zscores_files) - 1)
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fill_na_in_columns <- function(data) {
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for (header in colnames(data)) {
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if (grepl("Shift", header)) {
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data[[header]][is.na(data[[header]])] <- 0.001
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} else if (grepl("Z_lm_", header)) {
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data[[header]][is.na(data[[header]])] <- 0.0001
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}
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}
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data
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}
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headSel <- fill_na_in_columns(headSel)
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filter_by_sd <- function(data, sd) {
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if (sd == 0) return(data)
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z_lm_cols <- select(data, contains("z_lm_"))
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filter_vector <- rowSums(abs(z_lm_cols) >= sd) > 0
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data[filter_vector, ]
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}
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REMcRdy <- filter_by_sd(select(headSel, contains("OrfRep"), matches("Gene"), contains("z_lm_")), sd)
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shiftOnly <- filter_by_sd(select(headSel, contains("OrfRep"), matches("Gene"), contains("z_shift")), sd)
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reorder_columns <- function(data1, data2) {
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combined_data <- data1
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for (i in 3:ncol(data1)) {
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combined_data <- cbind(combined_data, data2[i], data1[i])
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}
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combined_data
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}
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combI <- reorder_columns(headSel2, shiftOnly)
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write.csv(REMcRdy, file.path(out_dir, "REMcRdy_lm_only.csv"), row.names = FALSE, quote = FALSE)
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write.csv(shiftOnly, file.path(out_dir, "Shift_only.csv"), row.names = FALSE, quote = FALSE)
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relabel_headers <- function(headers, exp_names) {
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new_labels <- headers
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for (i in seq_along(headers)) {
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suffix <- sub("^.*\\.(\\d+)$", "\\1", headers[i])
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if (suffix %in% seq_along(exp_names)) {
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exp_name <- exp_names[as.numeric(suffix)]
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new_labels[i] <- gsub(paste0(".", suffix), paste0("_", exp_name), headers[i])
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}
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}
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new_labels
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}
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colnames(shiftOnly) <- relabel_headers(colnames(shiftOnly), exp_names)
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colnames(REMcRdy) <- relabel_headers(colnames(REMcRdy), exp_names)
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write.csv(REMcRdy, file.path(out_dir, "REMcRdy_lm_only.csv"), row.names = FALSE, quote = FALSE)
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write.csv(shiftOnly, file.path(out_dir, "Shift_only.csv"), row.names = FALSE, quote = FALSE)
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}
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joined_data <- combine_zscores(zscores_files)
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process_combined_zscores(joined_data, args$sd, args$out_dir, exp_names)
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@@ -1,238 +0,0 @@
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suppressMessages({
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library("ggplot2")
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library("plotly")
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library("htmlwidgets")
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library("dplyr")
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library("ggthemes")
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})
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# Constants for configuration
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PLOT_WIDTH <- 14
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PLOT_HEIGHT <- 9
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BASE_SIZE <- 14
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# BACKUP_SUFFIX <- paste0(".backup_", Sys.Date())
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options(warn = 2, max.print = 100)
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# Parse arguments
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parse_arguments <- function() {
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if (interactive()) {
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args <- c(
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"1",
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"3",
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"/home/bryan/documents/develop/scripts/hartmanlab/workflow/out/20240116_jhartman2_DoxoHLD/StudyInfo.csv",
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"/home/bryan/documents/develop/scripts/hartmanlab/workflow/apps/r/SGD_features.tab",
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"/home/bryan/documents/develop/scripts/hartmanlab/workflow/out/20240116_jhartman2_DoxoHLD/easy/20240116_jhartman2/results_std.txt",
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"/home/bryan/documents/develop/scripts/hartmanlab/workflow/out/20240116_jhartman2_DoxoHLD/Exp1/zscores"
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)
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} else {
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args <- commandArgs(trailingOnly = TRUE)
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}
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list(
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exp_number = as.numeric(args[1]),
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delta_bg_factor = as.numeric(args[2]),
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study_info_file = normalizePath(file.path(args[3]), mustWork = FALSE),
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sgd_gene_list = normalizePath(file.path(args[4]), mustWork = FALSE),
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input_file = normalizePath(file.path(args[5]), mustWork = FALSE),
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out_dir = normalizePath(file.path(args[6]), mustWork = FALSE),
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out_dir_qc = normalizePath(file.path(args[6], "qc"), mustWork = FALSE)
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)
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}
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args <- parse_arguments()
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dir.create(args$out_dir, showWarnings = FALSE)
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dir.create(args$out_dir_qc, showWarnings = FALSE)
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# Define themes and scales
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theme_publication <- function(base_size = BASE_SIZE, base_family = "sans") {
|
||||
theme_foundation(base_size = base_size, base_family = base_family) +
|
||||
theme(
|
||||
plot.title = element_text(face = "bold", size = rel(1.2), hjust = 0.5),
|
||||
text = element_text(),
|
||||
panel.background = element_rect(colour = NA),
|
||||
plot.background = element_rect(colour = NA),
|
||||
panel.border = element_rect(colour = NA),
|
||||
axis.title = element_text(face = "bold", size = rel(1)),
|
||||
axis.title.y = element_text(angle = 90, vjust = 2),
|
||||
axis.title.x = element_text(vjust = -0.2),
|
||||
axis.line = element_line(colour = "black"),
|
||||
panel.grid.major = element_line(colour = "#f0f0f0"),
|
||||
panel.grid.minor = element_blank(),
|
||||
legend.key = element_rect(colour = NA),
|
||||
legend.position = "bottom",
|
||||
legend.direction = "horizontal",
|
||||
plot.margin = unit(c(10, 5, 5, 5), "mm"),
|
||||
strip.background = element_rect(colour = "#f0f0f0", fill = "#f0f0f0"),
|
||||
strip.text = element_text(face = "bold")
|
||||
)
|
||||
}
|
||||
|
||||
theme_publication_legend_right <- function(base_size = BASE_SIZE, base_family = "sans") {
|
||||
theme_publication(base_size, base_family) +
|
||||
theme(
|
||||
legend.position = "right",
|
||||
legend.direction = "vertical",
|
||||
legend.key.size = unit(0.5, "cm"),
|
||||
legend.spacing = unit(0, "cm"),
|
||||
legend.title = element_text(face = "italic")
|
||||
)
|
||||
}
|
||||
|
||||
scale_fill_publication <- function(...) {
|
||||
discrete_scale("fill", "Publication", manual_pal(values = c(
|
||||
"#386cb0", "#fdb462", "#7fc97f", "#ef3b2c", "#662506",
|
||||
"#a6cee3", "#fb9a99", "#984ea3", "#ffff33"
|
||||
)), ...)
|
||||
}
|
||||
|
||||
scale_colour_publication <- function(...) {
|
||||
discrete_scale("colour", "Publication", manual_pal(values = c(
|
||||
"#386cb0", "#fdb462", "#7fc97f", "#ef3b2c", "#662506",
|
||||
"#a6cee3", "#fb9a99", "#984ea3", "#ffff33"
|
||||
)), ...)
|
||||
}
|
||||
|
||||
# Load and preprocess data
|
||||
load_and_preprocess_data <- function(input_file) {
|
||||
df <- tryCatch({
|
||||
read.delim(input_file, skip = 2, as.is = TRUE, row.names = 1, strip.white = TRUE)
|
||||
}, error = function(e) {
|
||||
stop("Error reading file: ", input_file, "\n", e$message)
|
||||
})
|
||||
|
||||
df <- df[!df[[1]] %in% c("", "Scan"), ]
|
||||
|
||||
numeric_columns <- c("Col", "Row", "l", "K", "r", "Scan", "AUC96", "LstBackgrd", "X1stBackgrd")
|
||||
df[numeric_columns[numeric_columns %in% colnames(df)]] <- lapply(df[numeric_columns[numeric_columns %in% colnames(df)]], as.numeric)
|
||||
|
||||
df$L <- if ("l" %in% colnames(df)) df$l else {warning("Missing column: l"); NA}
|
||||
df$AUC <- if ("AUC96" %in% colnames(df)) df$AUC96 else {warning("Missing column: AUC96"); NA}
|
||||
df$conc_num <- if ("Conc" %in% colnames(df)) as.numeric(gsub("[^0-9\\.]", "", df$Conc)) else NA
|
||||
df$delta_bg <- if (all(c("X1stBackgrd", "LstBackgrd") %in% colnames(df))) df$LstBackgrd - df$X1stBackgrd else {warning("Missing columns for delta_bg calculation"); NA}
|
||||
|
||||
if (nrow(df) == 0) warning("Dataframe is empty after filtering")
|
||||
df
|
||||
}
|
||||
|
||||
df <- load_and_preprocess_data(args$input_file)
|
||||
|
||||
delta_bg_tolerance <- mean(df$delta_bg, na.rm = TRUE) + args$delta_bg_factor * sd(df$delta_bg, na.rm = TRUE)
|
||||
message("Exp", args$exp_number, ": The delta_bg_factor is: ", args$delta_bg_factor)
|
||||
message("Exp", args$exp_number, ": The delta_bg_tolerance is ", delta_bg_tolerance)
|
||||
|
||||
df <- df %>%
|
||||
mutate(OrfRep = if_else(ORF == "YDL227C", "YDL227C", OrfRep)) %>%
|
||||
filter(!Gene %in% c("BLANK", "Blank", "blank"), Drug != "BMH21")
|
||||
|
||||
# Create plot
|
||||
create_plot <- function(df, var, plot_type) {
|
||||
filtered_df <- df %>% filter(is.finite(.data[[var]]))
|
||||
p <- ggplot(filtered_df, aes(Scan, .data[[var]], color = as.factor(conc_num))) +
|
||||
ggtitle(paste("Plate analysis by Drug Conc for", var, "before quality control")) +
|
||||
theme_publication()
|
||||
|
||||
if (plot_type == "scatter") {
|
||||
p <- p +
|
||||
geom_point(shape = 3, size = 0.2, position = "jitter") +
|
||||
stat_summary(fun = mean, geom = "point", size = 0.6) +
|
||||
stat_summary(fun.data = mean_sdl, fun.args = list(mult = 1), geom = "errorbar")
|
||||
} else {
|
||||
p <- p + geom_boxplot()
|
||||
}
|
||||
|
||||
return(p)
|
||||
}
|
||||
|
||||
# Publish plot to PDF and HTML (Plotly)
|
||||
publish_plot <- function(plot, plot_path) {
|
||||
# if (file.exists(plot_path)) {
|
||||
# file.rename(plot_path, paste0(plot_path, BACKUP_SUFFIX))
|
||||
# }
|
||||
|
||||
pdf(plot_path, width = PLOT_WIDTH, height = PLOT_HEIGHT)
|
||||
print(plot)
|
||||
dev.off()
|
||||
|
||||
pgg <- suppressWarnings(ggplotly(plot,
|
||||
tooltip = c("L", "K", "ORF", "Gene", "delta_bg")) %>%
|
||||
layout(legend = list(orientation = "h")))
|
||||
saveWidget(pgg, sub(".pdf$", ".html", plot_path), selfcontained = TRUE)
|
||||
}
|
||||
|
||||
# Publish multiple plots
|
||||
publish_multiple_plots <- function(df, variables, plot_type, out_dir_qc, suffix = "") {
|
||||
lapply(variables, function(var) {
|
||||
plot <- create_plot(df, var, plot_type)
|
||||
publish_plot(plot, file.path(out_dir_qc, paste0("plate_analysis_", var, suffix, ".pdf")))
|
||||
})
|
||||
}
|
||||
|
||||
# Calculate and publish summary statistics
|
||||
publish_summary_stats <- function(df, variables, out_dir) {
|
||||
stats_list <- lapply(variables, function(var) {
|
||||
df %>%
|
||||
group_by(OrfRep, conc_num) %>%
|
||||
summarize(
|
||||
N = n(),
|
||||
mean_val = mean(.data[[var]], na.rm = TRUE),
|
||||
sd_val = sd(.data[[var]], na.rm = TRUE),
|
||||
se_val = sd_val / sqrt(N)
|
||||
)
|
||||
})
|
||||
summary_stats_df <- bind_rows(stats_list, .id = "variable")
|
||||
write.csv(summary_stats_df, file.path(out_dir, "summary_stats_all_strains.csv"), row.names = FALSE)
|
||||
}
|
||||
|
||||
# Calculate and publish interaction scores
|
||||
publish_interaction_scores <- function(df, out_dir) {
|
||||
interaction_scores <- df %>%
|
||||
group_by(OrfRep) %>%
|
||||
summarize(
|
||||
mean_L = mean(L, na.rm = TRUE),
|
||||
mean_K = mean(K, na.rm = TRUE),
|
||||
mean_r = mean(r, na.rm = TRUE),
|
||||
mean_AUC = mean(AUC, na.rm = TRUE),
|
||||
delta_bg_mean = mean(delta_bg, na.rm = TRUE),
|
||||
delta_bg_sd = sd(delta_bg, na.rm = TRUE)
|
||||
) %>%
|
||||
mutate(
|
||||
l_rank = rank(mean_L),
|
||||
k_rank = rank(mean_K),
|
||||
r_rank = rank(mean_r),
|
||||
auc_rank = rank(mean_AUC)
|
||||
)
|
||||
|
||||
write.csv(interaction_scores, file.path(out_dir, "rf_zscores_interaction.csv"), row.names = FALSE)
|
||||
write.csv(interaction_scores %>%
|
||||
arrange(l_rank, k_rank), file.path(out_dir, "rf_zscores_interaction_ranked.csv"), row.names = FALSE)
|
||||
}
|
||||
|
||||
# Publish z-scores
|
||||
publish_zscores <- function(df, out_dir) {
|
||||
zscores <- df %>%
|
||||
mutate(
|
||||
zscore_L = scale(L, center = TRUE, scale = TRUE),
|
||||
zscore_K = scale(K, center = TRUE, scale = TRUE),
|
||||
zscore_r = scale(r, center = TRUE, scale = TRUE),
|
||||
zscore_AUC = scale(AUC, center = TRUE, scale = TRUE)
|
||||
)
|
||||
|
||||
write.csv(zscores, file.path(out_dir, "zscores_interaction.csv"), row.names = FALSE)
|
||||
}
|
||||
|
||||
# QC plot generation and publication
|
||||
generate_and_publish_qc <- function(df, delta_bg_tolerance, out_dir_qc) {
|
||||
publish_multiple_plots(df, c("L", "K", "r", "AUC", "delta_bg"), "scatter", out_dir_qc)
|
||||
|
||||
delta_bg_above_tolerance <- df[df$delta_bg >= delta_bg_tolerance, ]
|
||||
publish_multiple_plots(delta_bg_above_tolerance, c("L", "K", "r", "AUC", "delta_bg"), "scatter", out_dir_qc, "_above_tolerance")
|
||||
}
|
||||
|
||||
# Run QC plot generation and publication
|
||||
generate_and_publish_qc(df, delta_bg_tolerance, args$out_dir_qc)
|
||||
|
||||
# Run summary statistics, interaction score, and z-score calculations and publications
|
||||
variables <- c("L", "K", "r", "AUC", "delta_bg")
|
||||
publish_summary_stats(df, variables, args$out_dir)
|
||||
publish_interaction_scores(df, args$out_dir)
|
||||
publish_zscores(df, args$out_dir)
|
||||
@@ -7,7 +7,7 @@ parse_arguments <- function() {
|
||||
"/home/bryan/documents/develop/scripts/hartmanlab/workflow/out/20240116_jhartman2_DoxoHLD",
|
||||
2,
|
||||
"/home/bryan/documents/develop/scripts/hartmanlab/workflow/out/20240116_jhartman2_DoxoHLD/StudyInfo.csv",
|
||||
"/home/bryan/documents/develop/scripts/hartmanlab/workflow/out/20240116_jhartman2_DoxoHLD/Exp1",
|
||||
"/home/bryan/documents/develop/scripts/hartmanlab/workflow/out/20240116_jhartman2_DoxoHLD/Exp1",
|
||||
"/home/bryan/documents/develop/scripts/hartmanlab/workflow/out/20240116_jhartman2_DoxoHLD/Exp2"
|
||||
)
|
||||
} else {
|
||||
|
||||
Reference in New Issue
Block a user