bryan/hartman-server
1
0
Rozštěpit 0
Zdrojový kód Úkoly Pull requesty Vydání Wiki Aktivita

Extend filter_data into process_data

Procházet zdrojové kódy
Tento commit je obsažen v:
Bryan Roessler
2024-09-18 13:09:58 -04:00
rodič 977561b200
revize b35551c3e2
1 změnil soubory, kde provedl 45 přidání a 51 odebrání
Stáhněte soubor záplaty Stáhněte rozdílový soubor Rozbalit všechny soubory Sbalit všechny soubory

96
qhtcp-workflow/apps/r/calculate_interaction_zscores.R
Zobrazit soubor

@@ -669,7 +669,7 @@ generate_interaction_plot_configs <- function(df, variables) {
AUC = c(-6500, 6500)
)
df_filtered <- filter_data(df, variables, missing = TRUE, limits_map = limits_map)
df_filtered <- process_data(df, variables, filter_na = TRUE, limits_map = limits_map)
# Define annotation label functions
generate_annotation_labels <- function(df, var, annotation_name) {
@@ -836,7 +836,7 @@ generate_rank_plot_configs <- function(df_filtered, variables, is_lm = FALSE) {
if (plot_type == "Avg_Zscore_vs_lm") {
x_var <- paste0("Avg_Zscore_", variable)
y_var <- paste0("Z_lm_", variable)
title_suffix <- paste("Avg Zscore vs lm", variable)
title <- paste("Avg Zscore vs lm", variable)
rectangles <- list(
list(xmin = -2, xmax = 2, ymin = -2, ymax = 2,
fill = NA, color = "grey20", alpha = 0.1
@@ -845,9 +845,12 @@ generate_rank_plot_configs <- function(df_filtered, variables, is_lm = FALSE) {
} else {
x_var <- paste0("Rank_", variable)
y_var <- paste0("Rank_lm_", variable)
title_suffix <- paste("Rank Avg Zscore vs lm", variable)
title <- paste("Rank Avg Zscore vs lm", variable)
rectangles <- NULL
}
print(summary(df_filtered[[x_var]]))
print(summary(df_filtered[[y_var]]))
# Fit linear model
lm_model <- lm(as.formula(paste(y_var, "~", x_var)), data = df_filtered)
@@ -862,7 +865,7 @@ generate_rank_plot_configs <- function(df_filtered, variables, is_lm = FALSE) {
x_var = x_var,
y_var = y_var,
plot_type = "scatter",
title = title_suffix,
title = title,
annotations = list(
list(
x = 0,
@@ -936,36 +939,26 @@ generate_correlation_plot_configs <- function(df) {
return(configs)
}
filter_data <- function(df, variables, nf = FALSE, missing = FALSE, adjust = FALSE,
rank = FALSE, limits_map = NULL, verbose = TRUE) {
process_data <- function(df, variables, filter_nf = FALSE, filter_na = FALSE, adjust = FALSE,
rank = FALSE, limits_map = NULL) {
avg_zscore_cols <- paste0("Avg_Zscore_", variables)
z_lm_cols <- paste0("Z_lm_", variables)
# Step 1: Adjust NAs to 0.001 for linear model (if adjust = TRUE)
if (adjust) {
if (verbose) message("Replacing NA with 0.001 for Avg_Zscore_ and Z_lm_ columns")
df <- df %>%
mutate(
across(all_of(avg_zscore_cols), ~ ifelse(is.na(.), 0.001, .)),
across(all_of(z_lm_cols), ~ ifelse(is.na(.), 0.001, .))
)
}
# Filter non-finite values
if (nf) {
if (filter_nf) {
message("Filtering non-finite values")
df <- df %>%
filter(if_all(all_of(variables), ~ is.finite(.)))
}
# Filter missing values
if (missing) {
if (filter_na) {
message("Filtering NA values")
df <- df %>%
filter(if_all(all_of(variables), ~ !is.na(.)))
}
# Apply limits from 'limits_map' if provided
if (!is.null(limits_map)) {
message("Filtering data outside y-limits (for plotting)")
for (variable in names(limits_map)) {
if (variable %in% variables) {
ylim_vals <- limits_map[[variable]]
@@ -975,19 +968,23 @@ filter_data <- function(df, variables, nf = FALSE, missing = FALSE, adjust = FAL
}
}
if (adjust) {
message("Replacing NA with 0.001 for Avg_Zscore_ and Z_lm_ columns for ranks")
df <- df %>%
mutate(
across(all_of(avg_zscore_cols), ~ ifelse(is.na(.), 0.001, .)),
across(all_of(z_lm_cols), ~ ifelse(is.na(.), 0.001, .))
)
}
# Calculate and add rank columns
# TODO probably should be moved to separate function
if (rank) {
if (verbose) message("Calculating ranks for variable(s): ", paste(variables, collapse = ", "))
for (variable in variables) {
avg_zscore_col <- paste0("Avg_Zscore_", variable)
rank_col <- paste0("Rank_", variable)
df[[rank_col]] <- rank(df[[avg_zscore_col]], na.last = "keep")
z_lm_col <- paste0("Z_lm_", variable)
rank_lm_col <- paste0("Rank_lm_", variable)
df[[rank_lm_col]] <- rank(df[[z_lm_col]], na.last = "keep")
}
message("Calculating ranks for Avg_Zscore_ and Z_lm_ columns")
df <- df %>%
mutate(across(all_of(avg_zscore_cols), rank, .names = "Rank_{.col}", na.last = "keep"))
df <- df %>%
mutate(across(all_of(z_lm_cols), rank, .names = "Rank_lm_{.col}", na.last = "keep"))
}
return(df)
@@ -1030,7 +1027,7 @@ main <- function() {
group_vars = c("conc_num", "conc_num_factor"))
df_stats <- ss$df_with_stats
message("Filtering non-finite data")
df_filtered_stats <- filter_data(df_stats, c("L"), nf = TRUE)
df_filtered_stats <- process_data(df_stats, c("L"), filter_nf = TRUE)
message("Calculating summary statistics after quality control")
ss <- calculate_summary_stats(
@@ -1040,7 +1037,7 @@ main <- function() {
df_na_ss <- ss$summary_stats
df_na_stats <- ss$df_with_stats
write.csv(df_na_ss, file = file.path(out_dir, "summary_stats_all_strains.csv"), row.names = FALSE)
df_na_filtered_stats <- filter_data(df_na_stats, c("L"), nf = TRUE)
df_na_filtered_stats <- process_data(df_na_stats, c("L"), filter_nf = TRUE)
message("Calculating summary statistics after quality control excluding zero values")
ss <- calculate_summary_stats(
@@ -1048,7 +1045,7 @@ main <- function() {
variables = summary_vars,
group_vars = c("conc_num", "conc_num_factor"))
df_no_zeros_stats <- ss$df_with_stats
df_no_zeros_filtered_stats <- filter_data(df_no_zeros_stats, c("L"), nf = TRUE)
df_no_zeros_filtered_stats <- process_data(df_no_zeros_stats, c("L"), filter_nf = TRUE)
message("Filtering by 2SD of K")
df_na_within_2sd_k <- df_na_stats %>%
@@ -1337,14 +1334,14 @@ main <- function() {
file = file.path(out_dir, "ZScores_Interaction_Deletion_Suppressors_K_lm.csv"), row.names = FALSE)
message("Generating rank plots")
zscores_interactions_joined_filtered <- filter_data(
# Formerly InteractionScores_AdjustMissing
zscores_interactions_joined_ranked <- process_data(
df = zscores_interactions_joined,
variables = interaction_vars,
missing = TRUE,
adjust = TRUE,
rank = TRUE)
rank_plot_configs <- generate_rank_plot_configs(
df = zscores_interactions_joined_filtered,
df = zscores_interactions_joined_ranked,
variables = interaction_vars,
is_lm = FALSE
)
@@ -1353,7 +1350,7 @@ main <- function() {
message("Generating ranked linear model plots")
rank_lm_plot_configs <- generate_rank_plot_configs(
df = zscores_interactions_joined_filtered,
df = zscores_interactions_joined_ranked,
variables = interaction_vars,
is_lm = TRUE
)
@@ -1361,17 +1358,10 @@ main <- function() {
plot_configs = rank_lm_plot_configs, grid_layout = list(ncol = 3, nrow = 2))
message("Filtering and reranking plots")
# Filter out rows where both Z_lm_L and Avg_Zscore_L are NA
zscores_interactions_filtered <- zscores_interactions_joined %>%
filter(!is.na(Z_lm_L) | !is.na(Avg_Zscore_L))
# Formerly X_NArm
zscores_interactions_filtered <- zscores_interactions_filtered %>%
zscores_interactions_filtered <- zscores_interactions_joined %>%
filter(!is.na(Z_lm_L) | !is.na(Avg_Zscore_L)) %>%
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,
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",
@@ -1382,14 +1372,18 @@ main <- function() {
Z_lm_L >= 2 & Avg_Zscore_L <= -2 ~ "Deletion Enhancer lm, Deletion Suppressor Avg Z score",
Z_lm_L <= -2 & Avg_Zscore_L >= 2 ~ "Deletion Suppressor lm, Deletion Enhancer Avg Z score",
TRUE ~ "No Effect"
)
),
lm_R_squared_L = summary(lm(Z_lm_L ~ Avg_Zscore_L))$r.squared,
lm_R_squared_K = summary(lm(Z_lm_K ~ Avg_Zscore_K))$r.squared,
lm_R_squared_r = summary(lm(Z_lm_r ~ Avg_Zscore_r))$r.squared,
lm_R_squared_AUC = summary(lm(Z_lm_AUC ~ Avg_Zscore_AUC))$r.squared
)
# Re-rank
zscores_interactions_filtered <- filter_data(
zscores_interactions_filtered <- process_data(
df = zscores_interactions_filtered,
variables = interaction_vars,
missing = TRUE, # TODO what I'm currently having issues with
filter_na = TRUE, # TODO what I'm currently having issues with
rank = TRUE
)