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Cleanup output

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Bryan Roessler
2024-09-17 21:03:19 -04:00
parent 24abf3c359
commit 5d0d017a67
1 changed files with 13 additions and 19 deletions
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32
qhtcp-workflow/apps/r/calculate_interaction_zscores.R
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@@ -168,10 +168,10 @@ calculate_summary_stats <- function(df, variables, group_vars) {
across(all_of(variables), list( across(all_of(variables), list(
mean = ~mean(., na.rm = TRUE), mean = ~mean(., na.rm = TRUE),
median = ~median(., na.rm = TRUE), median = ~median(., na.rm = TRUE),
max = ~max(., na.rm = TRUE), max = ~ ifelse(all(is.na(.)), NA, max(., na.rm = TRUE)),
min = ~min(., na.rm = TRUE), min = ~ ifelse(all(is.na(.)), NA, min(., na.rm = TRUE)),
sd = ~sd(., na.rm = TRUE), sd = ~sd(., na.rm = TRUE),
se = ~sd(., na.rm = TRUE) / sqrt(N) # Corrected SE calculation se = ~sd(., na.rm = TRUE) / sqrt(N) - 1 # TODO needs comment for explanation
), .names = "{.fn}_{.col}"), ), .names = "{.fn}_{.col}"),
.groups = "drop" .groups = "drop"
) )
@@ -331,7 +331,7 @@ calculate_interaction_scores <- function(df, max_conc, variables, group_vars) {
# Declare column order for output # Declare column order for output
calculations <- stats %>% calculations <- stats %>%
select( select(
"OrfRep", "Gene", "conc_num", "conc_num_factor", "N", "OrfRep", "Gene", "num", "conc_num", "conc_num_factor", "N",
"mean_L", "mean_K", "mean_r", "mean_AUC", "mean_L", "mean_K", "mean_r", "mean_AUC",
"median_L", "median_K", "median_r", "median_AUC", "median_L", "median_K", "median_r", "median_AUC",
"sd_L", "sd_K", "sd_r", "sd_AUC", "sd_L", "sd_K", "sd_r", "sd_AUC",
@@ -932,7 +932,7 @@ filter_data <- function(df, variables, nf = FALSE, missing = FALSE, adjust = FAL
# Adjust NAs to .001 for linear model # Adjust NAs to .001 for linear model
if (adjust) { if (adjust) {
if (verbose) message("Replacing NA with 0.001 for Avg_Zscore_ and Z_lm_ columns.") if (verbose) message("Replacing NA with 0.001 for Avg_Zscore_ and Z_lm_ columns")
df <- df %>% df <- df %>%
mutate( mutate(
across(all_of(avg_zscore_cols), ~ ifelse(is.na(.), 0.001, .)), across(all_of(avg_zscore_cols), ~ ifelse(is.na(.), 0.001, .)),
@@ -942,14 +942,12 @@ filter_data <- function(df, variables, nf = FALSE, missing = FALSE, adjust = FAL
# Filter non-finite values # Filter non-finite values
if (nf) { if (nf) {
if (verbose) message("Filtering non-finite values for variables: ", paste(variables, collapse = ", "))
non_finite_df <- df %>% non_finite_df <- df %>%
filter(if_any(all_of(variables), ~ !is.finite(.))) filter(if_any(all_of(variables), ~ !is.finite(.)))
if (verbose && nrow(non_finite_df) > 0) { if (verbose && nrow(non_finite_df) > 0) {
message("Filtering non-finite rows for variables ", paste(variables, collapse = ", "), ":") message("Filtering non-finite rows for variable(s) ", paste(variables, collapse = ", "), ":")
print(non_finite_df) print(non_finite_df %>% select(all_of(c("scan", "Plate", "Row", "Col", "num", "conc_num", variables))), n = 30)
} }
df <- df %>% df <- df %>%
@@ -958,18 +956,15 @@ filter_data <- function(df, variables, nf = FALSE, missing = FALSE, adjust = FAL
# Filter missing values # Filter missing values
if (missing) { if (missing) {
if (verbose) message("Filtering missing values for variables: ", paste(variables, collapse = ", "))
# Identify missing rows for logging
missing_df <- df %>% missing_df <- df %>%
filter(if_any(all_of(variables), ~ is.na(.))) filter(if_any(all_of(variables), ~ is.na(.)))
if (verbose && nrow(missing_df) > 0) { if (verbose && nrow(missing_df) > 0) {
message("Missing data for variables ", paste(variables, collapse = ", "), ":") message("Filtering missing data for variable(s) ", paste(variables, collapse = ", "), ":")
print(missing_df) print(missing_df %>% select(all_of(c("scan", "Plate", "Row", "Col", "num", "conc_num", variables))), n = 30)
} }
# Keep only rows where all specified variables are not missing
df <- df %>% df <- df %>%
filter(if_all(all_of(variables), ~ !is.na(.))) filter(if_all(all_of(variables), ~ !is.na(.)))
} }
@@ -980,14 +975,13 @@ filter_data <- function(df, variables, nf = FALSE, missing = FALSE, adjust = FAL
if (variable %in% variables) { if (variable %in% variables) {
ylim_vals <- limits_map[[variable]] ylim_vals <- limits_map[[variable]]
if (verbose) message("Applying limits for variable ", variable, ": [", ylim_vals[1], ", ", ylim_vals[2], "].")
out_of_range_df <- df %>% out_of_range_df <- df %>%
filter(.data[[variable]] < ylim_vals[1] | .data[[variable]] > ylim_vals[2]) filter(.data[[variable]] < ylim_vals[1] | .data[[variable]] > ylim_vals[2])
if (verbose && nrow(out_of_range_df) > 0) { if (verbose && nrow(out_of_range_df) > 0) {
message("Out-of-range data for variable ", variable, ":") message("Applying limits for variable ", variable, ": [", ylim_vals[1], ", ", ylim_vals[2], "].")
print(out_of_range_df) message("Filtering out-of-range data for variable ", variable, ":")
print(out_of_range_df %>% select(all_of(c("scan", "Plate", "Row", "Col", "num", "conc_num", variables))), n = 30)
} }
df <- df %>% df <- df %>%
@@ -998,7 +992,7 @@ filter_data <- function(df, variables, nf = FALSE, missing = FALSE, adjust = FAL
# Calculate Rank Columns if 'rank' is TRUE # Calculate Rank Columns if 'rank' is TRUE
if (rank) { if (rank) {
if (verbose) message("Calculating rank columns for variables: ", paste(variables, collapse = ", ")) if (verbose) message("Calculating ranks for variable(s): ", paste(variables, collapse = ", "))
for (col in avg_zscore_cols) { for (col in avg_zscore_cols) {
rank_col <- paste0("Rank_", col) rank_col <- paste0("Rank_", col)