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Fix interaction groupings

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This commit is contained in:
Bryan Roessler
2024-10-03 20:58:28 -04:00
parent 4b273db78d
commit b23c6dafef
1 changed files with 219 additions and 171 deletions
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358
qhtcp-workflow/apps/r/calculate_interaction_zscores.R
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@@ -163,22 +163,18 @@ load_and_filter_data <- function(easy_results_file, sd = 3) {
return(df)
}
# Update Gene names using the SGD gene list
update_gene_names <- function(df, sgd_gene_list) {
# Load SGD gene list
genes <- read.delim(file = sgd_gene_list,
quote = "", header = FALSE,
genes <- read.delim(file = sgd_gene_list, quote = "", header = FALSE,
colClasses = c(rep("NULL", 3), rep("character", 2), rep("NULL", 11)))
# Create a named vector for mapping ORF to GeneName
gene_map <- setNames(genes$V5, genes$V4)
# Vectorized match to find the GeneName from gene_map
mapped_genes <- gene_map[df$ORF]
# Replace NAs in mapped_genes with original Gene names (preserves existing Gene names if ORF is not found)
updated_genes <- ifelse(is.na(mapped_genes) | df$OrfRep == "YDL227C", df$Gene, mapped_genes)
# Ensure Gene is not left blank or incorrectly updated to "OCT1"
gene_map <- setNames(genes$V5, genes$V4) # ORF to GeneName mapping
df <- df %>%
mutate(Gene = ifelse(updated_genes == "" | updated_genes == "OCT1", OrfRep, updated_genes))
mutate(
mapped_genes = gene_map[ORF],
Gene = if_else(is.na(mapped_genes) | OrfRep == "YDL227C", Gene, mapped_genes),
Gene = if_else(Gene == "" | Gene == "OCT1", OrfRep, Gene) # Handle invalid names
)
return(df)
}
@@ -203,61 +199,82 @@ calculate_summary_stats <- function(df, variables, group_vars) {
)
# Create a cleaned version of df that doesn't overlap with summary_stats
cleaned_df <- df %>%
df_cleaned <- df %>%
select(-any_of(setdiff(intersect(names(df), names(summary_stats)), group_vars)))
df_joined <- left_join(cleaned_df, summary_stats, by = group_vars)
df_joined <- left_join(df_cleaned, summary_stats, by = group_vars)
return(list(summary_stats = summary_stats, df_with_stats = df_joined))
}
calculate_interaction_scores <- function(df, df_bg, group_vars) {
calculate_interaction_scores <- function(df, df_bg, group_vars, overlap_threshold = 2) {
max_conc <- max(as.numeric(df$conc_num_factor), na.rm = TRUE)
total_conc_num <- length(unique(df$conc_num))
# Include background statistics per concentration
bg_stats <- df_bg %>%
group_by(conc_num, conc_num_factor) %>%
# Calculate WT statistics from df_bg
wt_stats <- df_bg %>%
filter(conc_num == 0) %>% # use the zero drug concentration background
summarise(
WT_L = first(mean_L),
WT_K = first(mean_K),
WT_r = first(mean_r),
WT_AUC = first(mean_AUC),
WT_sd_L = first(sd_L),
WT_sd_K = first(sd_K),
WT_sd_r = first(sd_r),
WT_sd_AUC = first(sd_AUC),
WT_L = mean(mean_L, na.rm = TRUE),
WT_sd_L = mean(sd_L, na.rm = TRUE),
WT_K = mean(mean_K, na.rm = TRUE),
WT_sd_K = mean(sd_K, na.rm = TRUE),
WT_r = mean(mean_r, na.rm = TRUE),
WT_sd_r = mean(sd_r, na.rm = TRUE),
WT_AUC = mean(mean_AUC, na.rm = TRUE),
WT_sd_AUC = mean(sd_AUC, na.rm = TRUE)
)
# Add WT statistics to df
df <- df %>%
mutate(
WT_L = wt_stats$WT_L,
WT_sd_L = wt_stats$WT_sd_L,
WT_K = wt_stats$WT_K,
WT_sd_K = wt_stats$WT_sd_K,
WT_r = wt_stats$WT_r,
WT_sd_r = wt_stats$WT_sd_r,
WT_AUC = wt_stats$WT_AUC,
WT_sd_AUC = wt_stats$WT_sd_AUC
)
# Compute mean values at zero concentration
mean_L_zero_df <- df %>%
filter(conc_num == 0) %>%
group_by(across(all_of(group_vars))) %>%
summarise(
mean_L_zero = mean(mean_L, na.rm = TRUE),
mean_K_zero = mean(mean_K, na.rm = TRUE),
mean_r_zero = mean(mean_r, na.rm = TRUE),
mean_AUC_zero = mean(mean_AUC, na.rm = TRUE),
.groups = "drop"
)
# Calculate total number of concentrations
total_conc_num <- length(unique(df$conc_num))
# Join mean_L_zero_df to df
df <- df %>%
left_join(mean_L_zero_df, by = group_vars)
# Join background statistics to df
calculations <- df %>%
left_join(bg_stats, by = c("conc_num", "conc_num_factor"))
# Perform calculations
calculations <- calculations %>%
group_by(across(all_of(group_vars))) %>%
# Calculate Raw Shifts and Z Shifts
df <- df %>%
mutate(
N = n(),
NG = sum(NG, na.rm = TRUE),
DB = sum(DB, na.rm = TRUE),
SM = sum(SM, na.rm = TRUE),
num_non_removed_concs = n_distinct(conc_num[DB != 1]),
# Raw shifts
Raw_Shift_L = mean_L - WT_L,
Raw_Shift_K = mean_K - WT_K,
Raw_Shift_r = mean_r - WT_r,
Raw_Shift_AUC = mean_AUC - WT_AUC,
# Z shifts
Raw_Shift_L = mean_L_zero - WT_L,
Raw_Shift_K = mean_K_zero - WT_K,
Raw_Shift_r = mean_r_zero - WT_r,
Raw_Shift_AUC = mean_AUC_zero - WT_AUC,
Z_Shift_L = Raw_Shift_L / WT_sd_L,
Z_Shift_K = Raw_Shift_K / WT_sd_K,
Z_Shift_r = Raw_Shift_r / WT_sd_r,
Z_Shift_AUC = Raw_Shift_AUC / WT_sd_AUC,
Z_Shift_AUC = Raw_Shift_AUC / WT_sd_AUC
)
calculations <- df %>%
group_by(across(all_of(group_vars))) %>%
mutate(
NG_sum = sum(NG, na.rm = TRUE),
DB_sum = sum(DB, na.rm = TRUE),
SM_sum = sum(SM, na.rm = TRUE),
num_non_removed_concs = total_conc_num - sum(DB, na.rm = TRUE) - 1,
# Expected values
Exp_L = WT_L + Raw_Shift_L,
@@ -271,38 +288,32 @@ calculate_interaction_scores <- function(df, df_bg, group_vars) {
Delta_r = mean_r - Exp_r,
Delta_AUC = mean_AUC - Exp_AUC,
# Adjust Deltas for NG and SM
# Adjust deltas for NG and SM
Delta_L = if_else(NG == 1, mean_L - WT_L, Delta_L),
Delta_K = if_else(NG == 1, mean_K - WT_K, Delta_K),
Delta_r = if_else(NG == 1, mean_r - WT_r, Delta_r),
Delta_AUC = if_else(NG == 1, mean_AUC - WT_AUC, Delta_AUC),
Delta_L = if_else(SM == 1, mean_L - WT_L, Delta_L),
# Z-scores
# Calculate Z-scores
Zscore_L = Delta_L / WT_sd_L,
Zscore_K = Delta_K / WT_sd_K,
Zscore_r = Delta_r / WT_sd_r,
Zscore_AUC = Delta_AUC / WT_sd_AUC
) %>%
ungroup()
# Fit linear models within each group
calculations <- calculations %>%
group_by(across(all_of(group_vars))) %>%
group_modify(~ {
data <- .x
# Fit linear models
lm_L <- lm(Delta_L ~ conc_num_factor, data = data)
lm_K <- lm(Delta_K ~ conc_num_factor, data = data)
lm_r <- lm(Delta_r ~ conc_num_factor, data = data)
lm_AUC <- lm(Delta_AUC ~ conc_num_factor, data = data)
data <- data %>%
# Perform linear models
lm_L <- lm(Delta_L ~ conc_num_factor, data = .x)
lm_K <- lm(Delta_K ~ conc_num_factor, data = .x)
lm_r <- lm(Delta_r ~ conc_num_factor, data = .x)
lm_AUC <- lm(Delta_AUC ~ conc_num_factor, data = .x)
.x %>%
mutate(
lm_intercept_L = coef(lm_L)[1],
lm_slope_L = coef(lm_L)[2],
R_Squared_L = summary(lm_L)$r.squared,
lm_Score_L = max_conc * lm_slope_L + lm_intercept_L,
# Repeat for K, r, and AUC
lm_intercept_K = coef(lm_K)[1],
lm_slope_K = coef(lm_K)[2],
R_Squared_K = summary(lm_K)$r.squared,
@@ -316,11 +327,10 @@ calculate_interaction_scores <- function(df, df_bg, group_vars) {
R_Squared_AUC = summary(lm_AUC)$r.squared,
lm_Score_AUC = max_conc * lm_slope_AUC + lm_intercept_AUC
)
return(data)
}) %>%
ungroup()
# Compute lm means and sds across all data without grouping
# Summary statistics for lm scores
lm_means_sds <- calculations %>%
summarise(
lm_mean_L = mean(lm_Score_L, na.rm = TRUE),
@@ -330,26 +340,39 @@ calculate_interaction_scores <- function(df, df_bg, group_vars) {
lm_mean_r = mean(lm_Score_r, na.rm = TRUE),
lm_sd_r = sd(lm_Score_r, na.rm = TRUE),
lm_mean_AUC = mean(lm_Score_AUC, na.rm = TRUE),
lm_sd_AUC = sd(lm_Score_AUC, na.rm = TRUE)
lm_sd_AUC = sd(lm_Score_AUC, na.rm = TRUE),
.groups = "drop"
)
# Apply global lm means and sds to calculate Z_lm_*
calculations <- calculations %>%
mutate(
Z_lm_L = (lm_Score_L - lm_means_sds$lm_mean_L) / lm_means_sds$lm_sd_L,
Z_lm_K = (lm_Score_K - lm_means_sds$lm_mean_K) / lm_means_sds$lm_sd_K,
Z_lm_r = (lm_Score_r - lm_means_sds$lm_mean_r) / lm_means_sds$lm_sd_r,
Z_lm_AUC = (lm_Score_AUC - lm_means_sds$lm_mean_AUC) / lm_means_sds$lm_sd_AUC
lm_mean_L = lm_means_sds$lm_mean_L,
lm_sd_L = lm_means_sds$lm_sd_L,
lm_mean_K = lm_means_sds$lm_mean_K,
lm_sd_K = lm_means_sds$lm_sd_K,
lm_mean_r = lm_means_sds$lm_mean_r,
lm_sd_r = lm_means_sds$lm_sd_r,
lm_mean_AUC = lm_means_sds$lm_mean_AUC,
lm_sd_AUC = lm_means_sds$lm_sd_AUC
)
# Build interactions data frame
# Continue with gene Z-scores and interactions
calculations <- calculations %>%
mutate(
Z_lm_L = (lm_Score_L - lm_mean_L) / lm_sd_L,
Z_lm_K = (lm_Score_K - lm_mean_K) / lm_sd_K,
Z_lm_r = (lm_Score_r - lm_mean_r) / lm_sd_r,
Z_lm_AUC = (lm_Score_AUC - lm_mean_AUC) / lm_sd_AUC
)
# Build summary stats (interactions)
interactions <- calculations %>%
group_by(across(all_of(group_vars))) %>%
summarise(
Avg_Zscore_L = mean(Zscore_L, na.rm = TRUE),
Avg_Zscore_K = mean(Zscore_K, na.rm = TRUE),
Avg_Zscore_r = mean(Zscore_r, na.rm = TRUE),
Avg_Zscore_AUC = mean(Zscore_AUC, na.rm = TRUE),
Avg_Zscore_L = sum(Zscore_L, na.rm = TRUE) / first(num_non_removed_concs),
Avg_Zscore_K = sum(Zscore_K, na.rm = TRUE) / first(num_non_removed_concs),
Avg_Zscore_r = sum(Zscore_r, na.rm = TRUE) / (total_conc_num - 1),
Avg_Zscore_AUC = sum(Zscore_AUC, na.rm = TRUE) / (total_conc_num - 1),
# Interaction Z-scores
Z_lm_L = first(Z_lm_L),
@@ -369,27 +392,67 @@ calculate_interaction_scores <- function(df, df_bg, group_vars) {
Z_Shift_r = first(Z_Shift_r),
Z_Shift_AUC = first(Z_Shift_AUC),
# NG, DB, SM values
NG = first(NG),
DB = first(DB),
SM = first(SM),
# R Squared values
R_Squared_L = first(R_Squared_L),
R_Squared_K = first(R_Squared_K),
R_Squared_r = first(R_Squared_r),
R_Squared_AUC = first(R_Squared_AUC),
# Include Drug
Drug = first(Drug),
# NG, DB, SM values
NG = first(NG),
DB = first(DB),
SM = first(SM),
.groups = "drop"
)
# Return the dataframes without creating full_data
# Creating the final calculations and interactions dataframes with only required columns for csv output
calculations_df <- calculations %>%
select(
all_of(group_vars),
conc_num, conc_num_factor, conc_num_factor_factor,
N, NG, DB, SM,
mean_L, median_L, sd_L, se_L,
mean_K, median_K, sd_K, se_K,
mean_r, median_r, sd_r, se_r,
mean_AUC, median_AUC, sd_AUC, se_AUC,
Raw_Shift_L, Raw_Shift_K, Raw_Shift_r, Raw_Shift_AUC,
Z_Shift_L, Z_Shift_K, Z_Shift_r, Z_Shift_AUC,
WT_L, WT_K, WT_r, WT_AUC,
WT_sd_L, WT_sd_K, WT_sd_r, WT_sd_AUC,
Exp_L, Exp_K, Exp_r, Exp_AUC,
Delta_L, Delta_K, Delta_r, Delta_AUC,
Zscore_L, Zscore_K, Zscore_r, Zscore_AUC
)
interactions_df <- interactions %>%
select(
all_of(group_vars),
NG, DB, SM,
Avg_Zscore_L, Avg_Zscore_K, Avg_Zscore_r, Avg_Zscore_AUC,
Z_lm_L, Z_lm_K, Z_lm_r, Z_lm_AUC,
Raw_Shift_L, Raw_Shift_K, Raw_Shift_r, Raw_Shift_AUC,
Z_Shift_L, Z_Shift_K, Z_Shift_r, Z_Shift_AUC
)
calculations_no_overlap <- calculations %>%
# DB, NG, SM are same as in interactions, the rest may be different and need to be checked
select(-any_of(c(
"DB", "NG", "SM",
"Raw_Shift_L", "Raw_Shift_K", "Raw_Shift_r", "Raw_Shift_AUC",
"Z_Shift_L", "Z_Shift_K", "Z_Shift_r", "Z_Shift_AUC",
"Z_lm_L", "Z_lm_K", "Z_lm_r", "Z_lm_AUC"
)))
# Use left_join to avoid dimension mismatch issues
full_data <- calculations_no_overlap %>%
left_join(interactions, by = group_vars)
# Return full_data and the two required dataframes (calculations and interactions)
return(list(
calculations = calculations,
interactions = interactions
calculations = calculations_df,
interactions = interactions_df,
full_data = full_data
))
}
@@ -475,9 +538,11 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs) {
), color = error_bar_color)
}
} else {
# Original code for calculating from mean and sd
# Ensure the mean and sd columns exist
y_mean_col <- paste0("mean_", config$y_var)
y_sd_col <- paste0("sd_", config$y_var)
if (y_mean_col %in% colnames(df) && y_sd_col %in% colnames(df)) {
plot <- plot + geom_errorbar(aes(
x = .data[[config$x_var]],
ymin = .data[[y_mean_col]] - .data[[y_sd_col]],
@@ -485,6 +550,7 @@ generate_and_save_plots <- function(out_dir, filename, plot_configs) {
), color = error_bar_color)
}
}
}
# Convert ggplot to plotly for interactive version
plotly_plot <- suppressWarnings(plotly::ggplotly(plot))
@@ -714,7 +780,7 @@ generate_plate_analysis_plot_configs <- function(variables, df_before = NULL, df
return(list(plots = plot_configs))
}
generate_interaction_plot_configs <- function(df, df_calculations, df_interactions, type) {
generate_interaction_plot_configs <- function(df, type) {
# Define the y-limits for the plots
limits_map <- list(
@@ -762,13 +828,14 @@ generate_interaction_plot_configs <- function(df, df_calculations, df_interactio
)
plot_config$position <- "jitter"
# Annotation labels
annotations <- list(
list(x = 0.25, y = y_limits[1] + 0.1 * y_span, label = "NG ="), # Slightly above y-min
list(x = 0.25, y = y_limits[1] + 0.05 * y_span, label = "DB ="),
list(x = 0.25, y = y_limits[1], label = "SM =")
list(x = 0, y = y_limits[1] + 0.1 * y_span, label = "NG ="),
list(x = 0, y = y_limits[1] + 0.05 * y_span, label = "DB ="),
list(x = 0, y = y_limits[1], label = "SM =")
)
# Loop over unique x values and add NG, DB, SM values at calculated y positions
# Annotation values
for (x_val in unique(df$conc_num_factor_factor)) {
current_df <- df %>% filter(.data[[plot_config$x_var]] == x_val)
annotations <- append(annotations, list(
@@ -794,7 +861,6 @@ generate_interaction_plot_configs <- function(df, df_calculations, df_interactio
}
}
# Delta interaction plots (use df_calculations and df_interactions)
if (type == "reference") {
group_vars <- c("OrfRep", "Gene", "num")
} else if (type == "deletion") {
@@ -1195,14 +1261,14 @@ main <- function() {
ss <- calculate_summary_stats(df_na_within_2sd_k, "L",
group_vars = c("conc_num"))$summary_stats
write.csv(ss,
file = file.path(out_dir_qc, "max_observed_L_vals_for_spots_within_2sd_K.csv"),
file = file.path(out_dir_qc, "max_observed_L_vals_for_spots_within_2SD_K.csv"),
row.names = FALSE)
message("Calculating summary statistics for L outside 2SD of K")
ss <- calculate_summary_stats(df_na_outside_2sd_k, "L", group_vars = c("conc_num"))
df_na_l_outside_2sd_k_stats <- ss$df_with_stats
write.csv(ss$summary_stats,
file = file.path(out_dir, "max_observed_L_vals_for_spots_outside_2sd_K.csv"),
file = file.path(out_dir, "max_observed_L_vals_for_spots_outside_2SD_K.csv"),
row.names = FALSE)
plate_analysis_plot_configs <- generate_plate_analysis_plot_configs(
@@ -1300,11 +1366,10 @@ main <- function() {
plot_configs = plate_analysis_no_zeros_boxplot_configs),
list(out_dir = out_dir_qc, filename = "L_vs_K_for_strains_2SD_outside_mean_K",
plot_configs = l_outside_2sd_k_plot_configs),
list(out_dir = out_dir_qc, filename = "delta_background_vs_K_for_strains_2sd_outside_mean_K",
list(out_dir = out_dir_qc, filename = "delta_background_vs_K_for_strains_2SD_outside_mean_K",
plot_configs = delta_bg_outside_2sd_k_plot_configs)
)
# Generating quality control plots in parallel
# furrr::future_map(plot_configs, function(config) {
# generate_and_save_plots(config$out_dir, config$filename, config$plot_configs)
# }, .options = furrr_options(seed = TRUE))
@@ -1325,9 +1390,9 @@ main <- function() {
) %>%
filter(!is.na(L))
message("Calculating summary statistics for background strain")
message("Calculating background strain summary statistics")
ss_bg <- calculate_summary_stats(df_bg, c("L", "K", "r", "AUC", "delta_bg"),
group_vars = c("OrfRep", "conc_num"))
group_vars = c("OrfRep", "Drug", "conc_num", "conc_num_factor_factor"))
summary_stats_bg <- ss_bg$summary_stats
df_bg_stats <- ss_bg$df_with_stats
write.csv(
@@ -1339,7 +1404,7 @@ main <- function() {
df_reference <- df_na_stats %>% # formerly X2_RF
filter(OrfRep == strain) %>%
filter(!is.na(L)) %>%
group_by(conc_num) %>%
group_by(OrfRep, Drug, conc_num) %>%
mutate(
max_l_theoretical = max(max_L, na.rm = TRUE),
L = ifelse(L == 0 & !is.na(L) & conc_num > 0, max_l_theoretical, L),
@@ -1347,21 +1412,25 @@ main <- function() {
L = ifelse(L >= max_l_theoretical & !is.na(L) & conc_num > 0, max_l_theoretical, L)) %>%
ungroup()
message("Calculating reference strain interaction scores")
message("Calculating reference strain summary statistics")
df_reference_stats <- calculate_summary_stats(
df = df_reference,
variables = c("L", "K", "r", "AUC"),
group_vars = c("OrfRep", "Gene", "num", "conc_num")
group_vars = c("OrfRep", "Gene", "Drug", "num", "conc_num", "conc_num_factor_factor")
)$df_with_stats
reference_results <- calculate_interaction_scores(df_reference_stats, df_bg_stats, group_vars = c("OrfRep", "Gene", "num"))
df_calculations_reference <- reference_results$calculations
df_interactions_reference <- reference_results$interactions
message("Calculating reference strain interaction scores")
results <- calculate_interaction_scores(df_reference_stats, df_bg_stats, group_vars = c("OrfRep", "Gene", "Drug", "num"))
df_calculations_reference <- results$calculations
df_interactions_reference <- results$interactions
df_interactions_reference_joined <- results$full_data
write.csv(df_calculations_reference, file = file.path(out_dir, "zscore_calculations_reference.csv"), row.names = FALSE)
write.csv(df_interactions_reference, file = file.path(out_dir, "zscore_interactions_reference.csv"), row.names = FALSE)
message("Setting missing deletion values to the highest theoretical value at each drug conc for L")
df_deletion <- df_na_stats %>% # formerly X2
filter(OrfRep != strain) %>%
filter(!is.na(L)) %>%
group_by(conc_num) %>%
group_by(OrfRep, Gene, conc_num) %>%
mutate(
max_l_theoretical = max(max_L, na.rm = TRUE),
L = ifelse(L == 0 & !is.na(L) & conc_num > 0, max_l_theoretical, L),
@@ -1369,81 +1438,60 @@ main <- function() {
L = ifelse(L >= max_l_theoretical & !is.na(L) & conc_num > 0, max_l_theoretical, L)) %>%
ungroup()
message("Calculating deletion strain(s) interactions scores")
message("Calculating deletion strain(s) summary statistics")
df_deletion_stats <- calculate_summary_stats(
df = df_deletion,
variables = c("L", "K", "r", "AUC"),
group_vars = c("OrfRep", "Gene", "conc_num")
group_vars = c("OrfRep", "Gene", "Drug", "conc_num", "conc_num_factor_factor")
)$df_with_stats
deletion_results <- calculate_interaction_scores(df_deletion_stats, df_bg_stats, group_vars = c("OrfRep", "Gene"))
df_calculations <- deletion_results$calculations
df_interactions <- deletion_results$interactions
# Writing Z-Scores to file
write.csv(df_calculations_reference, file = file.path(out_dir, "zscore_calculations_reference.csv"), row.names = FALSE)
write.csv(df_interactions_reference, file = file.path(out_dir, "zscore_interactions_reference.csv"), row.names = FALSE)
message("Calculating deletion strain(s) interactions scores")
results <- calculate_interaction_scores(df_deletion_stats, df_bg_stats, group_vars = c("OrfRep", "Gene", "Drug"))
df_calculations <- results$calculations
df_interactions <- results$interactions
df_interactions_joined <- results$full_data
write.csv(df_calculations, file = file.path(out_dir, "zscore_calculations.csv"), row.names = FALSE)
write.csv(df_interactions, file = file.path(out_dir, "zscore_interactions.csv"), row.names = FALSE)
# Create interaction plots
message("Generating reference interaction plots")
reference_plot_configs <- generate_interaction_plot_configs(
df_reference_stats, df_calculations_reference, df_interactions_reference, "reference")
reference_plot_configs <- generate_interaction_plot_configs(df_interactions_reference_joined, df_bg_stats, "reference")
generate_and_save_plots(out_dir, "interaction_plots_reference", reference_plot_configs)
message("Generating deletion interaction plots")
deletion_plot_configs <- generate_interaction_plot_configs(
df_deletion_stats, df_calculations, df_interactions, "deletion")
deletion_plot_configs <- generate_interaction_plot_configs(df_interactions_joined, df_bg_stats, "deletion")
generate_and_save_plots(out_dir, "interaction_plots", deletion_plot_configs)
# Define conditions for enhancers and suppressors
# TODO Add to study config?
threshold <- 2
enhancer_condition_L <- df_interactions$Avg_Zscore_L >= threshold
suppressor_condition_L <- df_interactions$Avg_Zscore_L <= -threshold
enhancer_condition_K <- df_interactions$Avg_Zscore_K >= threshold
suppressor_condition_K <- df_interactions$Avg_Zscore_K <= -threshold
# Subset data
message("Writing enhancer/suppressor csv files")
interaction_threshold <- 2 # TODO add to study config?
enhancer_condition_L <- df_interactions$Avg_Zscore_L >= interaction_threshold
suppressor_condition_L <- df_interactions$Avg_Zscore_L <= -interaction_threshold
enhancer_condition_K <- df_interactions$Avg_Zscore_K >= interaction_threshold
suppressor_condition_K <- df_interactions$Avg_Zscore_K <= -interaction_threshold
enhancers_L <- df_interactions[enhancer_condition_L, ]
suppressors_L <- df_interactions[suppressor_condition_L, ]
enhancers_K <- df_interactions[enhancer_condition_K, ]
suppressors_K <- df_interactions[suppressor_condition_K, ]
# Save enhancers and suppressors
message("Writing enhancer/suppressor csv files")
enhancers_and_suppressors_L <- df_interactions[enhancer_condition_L | suppressor_condition_L, ]
enhancers_and_suppressors_K <- df_interactions[enhancer_condition_K | suppressor_condition_K, ]
write.csv(enhancers_L, file = file.path(out_dir, "zscore_interactions_deletion_enhancers_L.csv"), row.names = FALSE)
write.csv(suppressors_L, file = file.path(out_dir, "zscore_interactions_deletion_suppressors_L.csv"), row.names = FALSE)
write.csv(enhancers_K, file = file.path(out_dir, "zscore_interactions_deletion_enhancers_K.csv"), row.names = FALSE)
write.csv(suppressors_K, file = file.path(out_dir, "zscore_interactions_deletion_suppressors_K.csv"), row.names = FALSE)
# Combine conditions for enhancers and suppressors
enhancers_and_suppressors_L <- df_interactions[enhancer_condition_L | suppressor_condition_L, ]
enhancers_and_suppressors_K <- df_interactions[enhancer_condition_K | suppressor_condition_K, ]
# Save combined enhancers and suppressors
write.csv(enhancers_and_suppressors_L,
file = file.path(out_dir, "zscore_interactions_deletion_enhancers_and_suppressors_L.csv"), row.names = FALSE)
write.csv(enhancers_and_suppressors_K,
file = file.path(out_dir, "zscore_interaction_deletion_enhancers_and_suppressors_K.csv"), row.names = FALSE)
# Handle linear model based enhancers and suppressors
lm_threshold <- 2 # TODO add to study config?
enhancers_lm_L <- df_interactions[df_interactions$Z_lm_L >= lm_threshold, ]
suppressors_lm_L <- df_interactions[df_interactions$Z_lm_L <= -lm_threshold, ]
enhancers_lm_K <- df_interactions[df_interactions$Z_lm_K >= lm_threshold, ]
suppressors_lm_K <- df_interactions[df_interactions$Z_lm_K <= -lm_threshold, ]
# Save linear model based enhancers and suppressors
message("Writing linear model enhancer/suppressor csv files")
write.csv(enhancers_lm_L,
file = file.path(out_dir, "zscore_interactions_deletion_enhancers_lm_L.csv"), row.names = FALSE)
write.csv(suppressors_lm_L,
file = file.path(out_dir, "zscore_interactions_deletion_suppressors_lm_L.csv"), row.names = FALSE)
write.csv(enhancers_lm_K,
file = file.path(out_dir, "zscore_interactions_deletion_enhancers_lm_K.csv"), row.names = FALSE)
write.csv(suppressors_lm_K,
file = file.path(out_dir, "zscore_interactions_deletion_suppressors_lm_K.csv"), row.names = FALSE)
lm_interaction_threshold <- 2 # TODO add to study config?
enhancers_lm_L <- df_interactions[df_interactions$Z_lm_L >= lm_interaction_threshold, ]
suppressors_lm_L <- df_interactions[df_interactions$Z_lm_L <= -lm_interaction_threshold, ]
enhancers_lm_K <- df_interactions[df_interactions$Z_lm_K >= lm_interaction_threshold, ]
suppressors_lm_K <- df_interactions[df_interactions$Z_lm_K <= -lm_interaction_threshold, ]
write.csv(enhancers_lm_L, file = file.path(out_dir, "zscore_interactions_deletion_enhancers_lm_L.csv"), row.names = FALSE)
write.csv(suppressors_lm_L, file = file.path(out_dir, "zscore_interactions_deletion_suppressors_lm_L.csv"), row.names = FALSE)
write.csv(enhancers_lm_K, file = file.path(out_dir, "zscore_interactions_deletion_enhancers_lm_K.csv"), row.names = FALSE)
write.csv(suppressors_lm_K, file = file.path(out_dir, "zscore_interactions_deletion_suppressors_lm_K.csv"), row.names = FALSE)
message("Generating rank plots")
rank_plot_configs <- generate_rank_plot_configs(
@@ -1463,7 +1511,7 @@ main <- function() {
generate_and_save_plots(out_dir = out_dir, filename = "rank_plots_lm",
plot_configs = rank_lm_plot_configs)
overlap_threshold <- 2
overlap_threshold <- 2 # TODO add to study config?
df_interactions_filtered <- df_interactions %>%
filter(!is.na(Z_lm_L) & !is.na(Avg_Zscore_L)) %>%
mutate(