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Improve rank plot filtering and plot config generation

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This commit is contained in:
Bryan Roessler
2024-09-15 21:58:19 -04:00
parent 534c570335
commit 1dfb5d5084
1 changed files with 46 additions and 48 deletions
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90
qhtcp-workflow/apps/r/calculate_interaction_zscores.R
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@@ -707,16 +707,16 @@ generate_interaction_plot_configs <- function(df, variables) {
))
}
generate_rank_plot_configs <- function(df, interaction_vars, rank_vars = c("L", "K"), is_lm = FALSE) {
generate_rank_plot_configs <- function(df, interaction_vars, rank_vars = c("L", "K"), is_lm = FALSE, adjust = FALSE) {
# Adjust missing values and compute ranks for each interaction variable
for (var in interaction_vars) {
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) {
for (var in interaction_vars) {
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 (all(c(avg_zscore_col, z_lm_col) %in% names(df))) {
# Replace 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]])
@@ -724,8 +724,7 @@ generate_rank_plot_configs <- function(df, interaction_vars, rank_vars = c("L",
# Compute ranks for interaction variables
df[[rank_col]] <- rank(df[[avg_zscore_col]], na.last = "keep")
df[[rank_lm_col]] <- rank(df[[z_lm_col]], na.last = "keep")
} else {
warning(paste("Columns", avg_zscore_col, "or", z_lm_col, "not found in the data frame"))
}
}
@@ -779,8 +778,7 @@ generate_rank_plot_configs <- function(df, interaction_vars, rank_vars = c("L",
enhancer_label = NULL,
suppressor_label = NULL,
shape = 3,
size = 0.1,
position = "jitter"
size = 0.1
)
}
}
@@ -1226,18 +1224,12 @@ main <- function() {
file = file.path(out_dir, "ZScores_Interaction_Deletion_Suppressors_K_lm.csv"), row.names = FALSE)
message("Generating rank plots")
# Generate rank plot configurations and adjust the dataframe
zscores_interactions_adjusted <- generate_rank_plot_configs(
df = zscores_interactions,
interaction_vars = interaction_vars,
is_lm = FALSE
)$adjusted_df
# Generate rank plots for L and K using standard ranks
rank_plot_configs <- generate_rank_plot_configs(
df = zscores_interactions_adjusted,
df = zscores_interactions,
interaction_vars = interaction_vars,
is_lm = FALSE
is_lm = FALSE,
adjust = TRUE
)$plot_configs
# Save the generated rank plots for L and K
@@ -1246,9 +1238,10 @@ main <- function() {
# Generate rank plots for L and K using linear model (`lm`) ranks
rank_lm_plot_configs <- generate_rank_plot_configs(
df = zscores_interactions_adjusted,
df = zscores_interactions,
interaction_vars = interaction_vars,
is_lm = TRUE
is_lm = TRUE,
adjust = TRUE
)$plot_configs
# Save the linear model based rank plots for L and K
@@ -1256,23 +1249,20 @@ main <- function() {
plot_configs = rank_lm_plot_configs, grid_layout = list(ncol = 3, nrow = 2))
message("Filtering and regenerating rank plots")
# Formerly X_NArm
# Filter rows where either Z_lm_L or Avg_Zscore_L is not NA
zscores_interactions_filtered <- zscores_interactions %>%
group_by(across(all_of(orf_group_vars))) %>%
filter(!is.na(Z_lm_L) | !is.na(Avg_Zscore_L))
filter(!is.na(Z_lm_L) | !is.na(Avg_Zscore_L)) %>%
ungroup()
# Final filtered correlation calculations and plots
lm_results <- zscores_interactions_filtered %>%
summarise(
# Final filtered correlation calculations and Overlap column
zscores_interactions_filtered <- zscores_interactions_filtered %>%
rowwise() %>%
mutate(
lm_R_squared_L = if (n() > 1) summary(lm(Z_lm_L ~ Avg_Zscore_L))$r.squared else NA,
lm_R_squared_K = if (n() > 1) summary(lm(Z_lm_K ~ Avg_Zscore_K))$r.squared else NA,
lm_R_squared_r = if (n() > 1) summary(lm(Z_lm_r ~ Avg_Zscore_r))$r.squared else NA,
lm_R_squared_AUC = if (n() > 1) summary(lm(Z_lm_AUC ~ Avg_Zscore_AUC))$r.squared else NA
)
zscores_interactions_filtered <- zscores_interactions_filtered %>%
left_join(lm_results, by = orf_group_vars) %>%
mutate(
lm_R_squared_AUC = if (n() > 1) summary(lm(Z_lm_AUC ~ Avg_Zscore_AUC))$r.squared else NA,
Overlap = case_when(
Z_lm_L >= 2 & Avg_Zscore_L >= 2 ~ "Deletion Enhancer Both",
Z_lm_L <= -2 & Avg_Zscore_L <= -2 ~ "Deletion Suppressor Both",
@@ -1285,24 +1275,32 @@ main <- function() {
) %>%
ungroup()
rank_plot_configs <- c(
generate_rank_plot_configs(zscores_interactions_filtered, "Rank_L", "Avg_Zscore_L", "L"),
generate_rank_plot_configs(zscores_interactions_filtered, "Rank_K", "Avg_Zscore_K", "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 filtered rank plots")
rank_plot_filtered_configs <- generate_rank_plot_configs(
df = zscores_interactions_filtered,
interaction_vars = interaction_vars,
is_lm = FALSE,
adjust = FALSE
)$plot_configs
generate_and_save_plots(output_dir = out_dir, file_name = "RankPlots_na_rm",
plot_configs = rank_plot_filtered_configs,
grid_layout = list(ncol = 3, nrow = 2))
rank_lm_plot_configs <- c(
generate_rank_plot_configs(zscores_interactions_filtered, "Rank_lm_L", "Z_lm_L", "L", is_lm = TRUE),
generate_rank_plot_configs(zscores_interactions_filtered, "Rank_lm_K", "Z_lm_K", "K", is_lm = TRUE)
)
generate_and_save_plots(output_dir = out_dir, file_name = "RankPlots_lm",
plot_configs = rank_lm_plot_configs, grid_layout = list(ncol = 3, nrow = 2))
rank_plot_lm_filtered_configs <- generate_rank_plot_configs(
df = zscores_interactions_filtered,
interaction_vars = interaction_vars,
is_lm = TRUE,
adjust = FALSE
)$plot_configs
generate_and_save_plots(output_dir = out_dir, file_name = "RankPlots_lm_na_rm",
plot_configs = rank_plot_lm_filtered_configs,
grid_layout = list(ncol = 3, nrow = 2))
message("Generating correlation plots")
correlation_plot_configs <- generate_correlation_plot_configs(zscores_interactions_filtered, interaction_vars)
generate_and_save_plots(output_dir = out_dir, file_name = "Avg_Zscore_vs_lm_NA_rm",
plot_configs = correlation_plot_configs, grid_layout = list(ncol = 2, nrow = 2))
plot_configs = correlation_plot_configs,
grid_layout = list(ncol = 2, nrow = 2))
})
})
}