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Absorb finite filter

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This commit is contained in:
Bryan Roessler
2024-09-16 15:33:25 -04:00
parent 5c87ff8615
commit 3f2d629371
1 changed files with 17 additions and 19 deletions
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36
qhtcp-workflow/apps/r/calculate_interaction_zscores.R
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@@ -579,7 +579,7 @@ generate_interaction_plot_configs <- function(df, variables) {
AUC = c(-6500, 6500)
)
df_filtered <- filter_data_for_plots(df, variables, missing = TRUE, limits_map)
df_filtered <- filter_data(df, variables, missing = TRUE, limits_map)
# Define annotation label functions
generate_annotation_labels <- function(df, var, annotation_name) {
@@ -672,7 +672,7 @@ generate_interaction_plot_configs <- function(df, variables) {
generate_rank_plot_configs <- function(df, variables, is_lm = FALSE, adjust = FALSE) {
df_filtered <- filter_data_for_plots(df, variables, missing = TRUE)
df_filtered <- filter_data(df, variables, missing = TRUE)
for (var in variables) {
avg_zscore_col <- paste0("Avg_Zscore_", var)
@@ -776,22 +776,20 @@ generate_correlation_plot_configs <- function(df, variables) {
return(configs)
}
filter_and_print_non_finite <- function(df, vars_to_check, print_vars) {
non_finite_rows <- df %>% filter(if_any(all_of(vars_to_check), ~ !is.finite(.)))
if (nrow(non_finite_rows) > 0) {
message("Filtering non-finite rows:")
print(non_finite_rows %>% select(all_of(print_vars)), n = 200)
}
df %>% filter(if_all(all_of(vars_to_check), is.finite))
}
filter_data_for_plots <- function(df, variables, missing = FALSE, limits_map = NULL) {
filter_data <- function(df, variables, nf = FALSE, missing = FALSE, limits_map = NULL) {
# Loop through each variable to filter and print missing/out-of-range data
for (variable in variables) {
y_var_sym <- sym(variable)
if (nf) {
non_finite <- df %>% filter(!is.finite(!!y_var_sym))
if (nrow(non_finite) > 0) {
message("Non-finite rows for variable ", variable, ":")
print(non_finite)
}
df <- df %>% filter(is.finite(!!y_var_sym))
}
# Filter missing data
if (missing) {
@@ -844,7 +842,7 @@ main <- function() {
update_gene_names(args$sgd_gene_list) %>%
as_tibble()
# Quality Control: Filter rows above tolerance
# Filter rows above delta background tolerance
df_above_tolerance <- df %>% filter(DB == 1)
df_na <- df %>% mutate(across(all_of(summary_vars), ~ ifelse(DB == 1, NA, .)))
df_no_zeros <- df_na %>% filter(L > 0)
@@ -857,20 +855,20 @@ main <- function() {
message("Calculating summary statistics before quality control")
ss <- calculate_summary_stats(df, summary_vars, group_vars = group_vars)
df_stats <- ss$df_with_stats
df_filtered_stats <- filter_and_print_non_finite(df_stats, "L", print_vars)
message("Filtering non-finite data")
df_filtered_stats <- filter_data(df_stats, c("L"), nf = TRUE)
message("Calculating summary statistics after quality control")
ss <- calculate_summary_stats(df_na, summary_vars, group_vars = group_vars)
df_na_ss <- ss$summary_stats
df_na_stats <- ss$df_with_stats
write.csv(df_na_ss, file = file.path(out_dir, "summary_stats_all_strains.csv"), row.names = FALSE)
# Filter out non-finite rows for plotting
df_na_filtered_stats <- filter_and_print_non_finite(df_na_stats, "L", print_vars)
df_na_filtered_stats <- filter_data(df_na_stats, c("L"), nf = TRUE)
message("Calculating summary statistics after quality control excluding zero values")
ss <- calculate_summary_stats(df_no_zeros, summary_vars, group_vars = group_vars)
df_no_zeros_stats <- ss$df_with_stats
df_no_zeros_filtered_stats <- filter_and_print_non_finite(df_no_zeros_stats, "L", print_vars)
df_no_zeros_filtered_stats <- filter_data(df_no_zeros_stats, c("L"), nf = TRUE)
message("Filtering by 2SD of K")
df_na_within_2sd_k <- df_na_stats %>%