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Hardcode variables for rank plots

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Bryan Roessler
2024-09-16 19:33:00 -04:00
parent 9fe45ba73f
commit 36b5a3aa4e
1 changed files with 22 additions and 19 deletions
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41
qhtcp-workflow/apps/r/calculate_interaction_zscores.R
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@@ -672,20 +672,18 @@ generate_interaction_plot_configs <- function(df, variables) {
return(configs)
}
generate_rank_plot_configs <- function(df, variables, is_lm = FALSE, adjust = FALSE) {
df_filtered <- filter_data(df, variables, missing = TRUE)
generate_rank_plot_configs <- function(df_filtered, is_lm = FALSE, adjust = FALSE) {
# Define SD bands
sd_bands <- c(1, 2, 3)
# Define variables for Avg ZScore and Rank Avg ZScore plots
avg_zscore_vars <- c("r", "L", "K", "AUC")
variables <- c("r", "L", "K", "AUC")
# Initialize list to store plot configurations
configs <- list()
#### 1. SD-Based Plots for L and K ####
# SD-based plots for L and K
for (var in c("L", "K")) {
for (sd_band in sd_bands) {
@@ -741,8 +739,8 @@ generate_rank_plot_configs <- function(df, variables, is_lm = FALSE, adjust = FA
}
}
#### 2. Avg ZScore and Rank Avg ZScore Plots for r, L, K, and AUC ####
for (var in avg_zscore_vars) {
# Average ZScore and Rank Avg ZScore Plots for r, L, K, and AUC
for (var in variables) {
for (plot_type in c("Avg_Zscore_vs_lm", "Rank_Avg_Zscore_vs_lm")) {
# Define x and y variables based on plot type
@@ -821,9 +819,22 @@ generate_correlation_plot_configs <- function(df, variables) {
return(configs)
}
filter_data <- function(df, variables, nf = FALSE, missing = FALSE, limits_map = NULL, verbose = TRUE) {
filter_data <- function(df, variables, nf = FALSE, missing = FALSE, adjust = FALSE,
limits_map = NULL, verbose = TRUE) {
for (variable in variables) {
avg_zscore_col <- paste0("Avg_Zscore_", var)
z_lm_col <- paste0("Z_lm_", var)
rank_col <- paste0("Rank_", var)
rank_lm_col <- paste0("Rank_lm_", var)
if (adjust) {
message("Replacing NA with 0.001 for interaction variables")
df[[avg_zscore_col]] <- if_else(is.na(df[[avg_zscore_col]]), 0.001, df[[avg_zscore_col]])
df[[z_lm_col]] <- if_else(is.na(df[[z_lm_col]]), 0.001, df[[z_lm_col]])
}
if (nf) {
non_finite <- df %>% filter(!is.finite(.data[[variable]]))
if (verbose && nrow(non_finite) > 0) {
@@ -1242,33 +1253,27 @@ main <- function() {
file = file.path(out_dir, "ZScores_Interaction_Deletion_Suppressors_K_lm.csv"), row.names = FALSE)
message("Generating rank plots")
# Generate rank plots for L and K using standard ranks
zscores_interactions_joined_filtered <- filter_data(zscores_interactions_joined, variables, missing = TRUE, adjust = TRUE)
rank_plot_configs <- generate_rank_plot_configs(
df = zscores_interactions_joined,
df = zscores_interactions_joined_filtered,
variables = interaction_vars,
is_lm = FALSE,
adjust = TRUE
)
# Save the generated rank plots for L and K
generate_and_save_plots(output_dir = out_dir, file_name = "RankPlots",
plot_configs = rank_plot_configs, grid_layout = list(ncol = 3, nrow = 2))
message("Generating ranked linear model plots")
# Generate rank plots for L and K using linear model (`lm`) ranks
rank_lm_plot_configs <- generate_rank_plot_configs(
df = zscores_interactions_joined,
df = zscores_interactions_joined_filtered,
variables = interaction_vars,
is_lm = TRUE,
adjust = TRUE
)
# Save the linear model based rank plots for L and K
generate_and_save_plots(output_dir = out_dir, file_name = "RankPlots_lm",
plot_configs = rank_lm_plot_configs, grid_layout = list(ncol = 3, nrow = 2))
message("Filtering and reranking plots")
# Filter rows where either Z_lm_L or Avg_Zscore_L is not NA
# Formerly X_NArm
zscores_interactions_filtered <- zscores_interactions %>%
group_by(across(all_of(orf_group_vars))) %>%
@@ -1296,7 +1301,6 @@ main <- function() {
message("Generating filtered ranked plots")
rank_plot_filtered_configs <- generate_rank_plot_configs(
df = zscores_interactions_filtered,
variables = interaction_vars,
is_lm = FALSE,
adjust = FALSE
)
@@ -1307,7 +1311,6 @@ main <- function() {
message("Generating filtered ranked linear model plots")
rank_plot_lm_filtered_configs <- generate_rank_plot_configs(
df = zscores_interactions_filtered,
variables = interaction_vars,
is_lm = TRUE,
adjust = FALSE
)