Gdr 2199

DESCRIPTION

@@ -2,8 +2,8 @@ Type: Package
 Package: gDRcore
 Title: Processing functions and interface to process and analyze drug
     dose-response data
-Version: 1.1.2
-Date: 2023-12-15
+Version: 1.1.3
+Date: 2024-01-03
 Authors@R: c(
     person("Bartosz", "Czech", , "[email protected]", role = "aut"),
     person("Arkadiusz", "Gladki", role=c("cre", "aut"), email="[email protected]"),

NEWS.md

@@ -1,3 +1,7 @@
+## 1.1.3 (2024-01-03)
+- simplify logic of assays for combination data
+- rename `matrix` into `combination`
+
 ## 1.1.2 (2023-12-15)
 - fix issue with wrong assignment of `untreated` records
 

R/combinations-calculate_matrix_metric.R

@@ -39,7 +39,13 @@ NULL
 #' calculate_HSA(sa1, "conc", sa2, "conc2", "x")
 #' @export
 calculate_HSA <- function(sa1, series_id1, sa2, series_id2, metric) {
-  .calculate_matrix_metric(sa1, series_id1, sa2, series_id2, metric, FXN = pmin)
+  .calculate_matrix_metric(sa1,
+                           series_id1,
+                           sa2,
+                           series_id2,
+                           metric,
+                           FXN = pmin,
+                           measured_col = "mx")
 }
 
 
@@ -50,7 +56,12 @@ calculate_HSA <- function(sa1, series_id1, sa2, series_id2, metric) {
 #' sa2 <- data.table::data.table(conc = rep(0, n), conc2 = seq(n), x = seq(n))
 #' calculate_Bliss(sa1, "conc", sa2, "conc2", "x")
 #' @export
-calculate_Bliss <- function(sa1, series_id1, sa2, series_id2, metric) {
+calculate_Bliss <- function(sa1,
+                            series_id1,
+                            sa2,
+                            series_id2,
+                            metric,
+                            measured_col = "mx") {
   if (metric %in% c("GRvalue", "GR")) {
     lambda <- function(x, y) {
       ifelse(x < 0 | y < 0,
@@ -80,7 +91,8 @@ calculate_Bliss <- function(sa1, series_id1, sa2, series_id2, metric) {
     sa2, 
     series_id2, 
     metric, 
-    FXN = lambda
+    FXN = lambda,
+    measured_col = measured_col
   )
 }
 
@@ -89,15 +101,17 @@ calculate_Bliss <- function(sa1, series_id1, sa2, series_id2, metric) {
                                      series_id1, 
                                      sa2, 
                                      series_id2, 
-                                     metric, FXN) {
+                                     metric,
+                                     FXN,
+                                     measured_col = "x") {
   checkmate::assert_data_table(sa1)
   checkmate::assert_data_table(sa2)
 
   checkmate::assert_true(all(sa1[[series_id2]] == 0L))
   checkmate::assert_true(all(sa2[[series_id1]] == 0L))
 
-  data.table::setnames(sa1, "x", "metric1", skip_absent = TRUE)
-  data.table::setnames(sa2, "x", "metric2", skip_absent = TRUE)
+  data.table::setnames(sa1, measured_col, "metric1", skip_absent = TRUE)
+  data.table::setnames(sa2, measured_col, "metric2", skip_absent = TRUE)
 
   u <- data.table::CJ(sa1[[series_id1]], sa2[[series_id2]])
   colnames(u) <- c(series_id1, series_id2)

R/combinations-isobolograms.R

@@ -338,7 +338,7 @@ calculate_Loewe <- function(
 
 
 get_isocutoffs <- function(df_mean, normalization_type) {
-  if (min(df_mean[normalization_type == normalization_type, x], na.rm = TRUE) > 0.7) {
+  if (min(df_mean[normalization_type == normalization_type, mx], na.rm = TRUE) > 0.7) {
     iso_cutoffs <- NULL
   } else {
     if (normalization_type == "GR") {
@@ -350,7 +350,7 @@ get_isocutoffs <- function(df_mean, normalization_type) {
       max(
         max_val, 
         ceiling(
-          20 * min(df_mean[normalization_type == normalization_type, x] + 0.08, na.rm = TRUE)
+          20 * min(df_mean[normalization_type == normalization_type, mx] + 0.08, na.rm = TRUE)
         ) / 20
       ), 
       0.8,  

R/data_type.R

@@ -65,11 +65,13 @@ identify_data_type <- function(df,
 
   df[, record_id := .I]
   df[, type := NA_character_]
+
+  sa_name <- gDRutils::get_experiment_groups("single-agent")[["single-agent"]]
 
   controls <- rowSums(df[, conc_ids, with = FALSE] == 0) == length(conc_ids)
   single_agent <- rowSums(df[, conc_ids, with = FALSE] != 0) == 1
   df$type <- ifelse(controls, "control",
-                    ifelse(single_agent, "single-agent", NA))
+                    ifelse(single_agent, sa_name, NA))
 
 
   if (length(conc_ids) > 1) {
@@ -81,7 +83,9 @@ identify_data_type <- function(df,
     )
     conc_ratio <- conc_ratio[!names(conc_ratio) %in% c("Inf", "-Inf")]
 
-    type <- ifelse(length(conc_ratio) <= codilution_conc, "co-dilution", "matrix")
+    type <- ifelse(length(conc_ratio) <= codilution_conc,
+                   gDRutils::get_experiment_groups("single-agent")[["co-dilution"]],
+                   gDRutils::get_experiment_groups("combination"))
     df$type[missing_type_rows] <- type
   }
   df
@@ -156,7 +160,10 @@ split_raw_data <- function(df,
       "drug_moa", "drug_moa2", "drug_moa3", "concentration", "concentration2",
       "concentration3"), 
     simplify = FALSE)
-  ) 
+  )
+
+  sa_name <- gDRutils::get_experiment_groups("single-agent")[["single-agent"]]
+
   drug_ids <- drug_ids[which(drug_ids %in% names(df))]
   codrug_ids <- drug_ids[grep("[0-9]", names(drug_ids))]
   conc_idx <- drug_ids[grep("concentration", names(drug_ids))]
@@ -167,7 +174,7 @@ split_raw_data <- function(df,
   types <- setdiff(names(df_list), "control")
   control <- df_list[["control"]]
   df_list[["control"]] <- NULL
-  cotrt_types <- setdiff(names(df_list), "single-agent")
+  cotrt_types <- setdiff(names(df_list), sa_name)
 
   control_sa_idx <- which(
     rowSums(df[, conc_idx, with = FALSE] == 0) == length(conc_idx)
@@ -193,8 +200,8 @@ split_raw_data <- function(df,
       df_merged <- rbind(
         df_list[[x]], 
         cotrt_matching[control, on = intersect(names(cotrt_matching), names(control))])
-      if (x == "matrix") {
-        matrix_data <- rbind(df_merged, df_list[["single-agent"]])
+      if (x == gDRutils::get_experiment_groups("combination")) {
+        matrix_data <- rbind(df_merged, df_list[[sa_name]])
         for (j in conc_idx)
           data.table::set(matrix_data, which(is.na(matrix_data[[j]])), j, 0)
         for (j in codrug_drug_id) {
@@ -207,10 +214,10 @@ split_raw_data <- function(df,
     })
   }
 
-  if (any("single-agent" == names(df_list))) {
+  if (any(sa_name == names(df_list))) {
     sa_idx <- gDRutils::loop(
       grep(drug_ids[["concentration"]], drug_ids, value = TRUE), 
-      function(x) which(!df_list[["single-agent"]][, x, with = FALSE] == 0)
+      function(x) which(!df_list[[sa_name]][, x, with = FALSE] == 0)
     )
     sa_idx[["concentration"]] <- NULL
 
@@ -221,15 +228,15 @@ split_raw_data <- function(df,
         value = TRUE
       )
       selected_columns <- unname(drug_ids[c("drug_name", "drug", "drug_moa", "concentration")])
-      df_list[["single-agent"]][sa_idx[[codrug]], selected_columns] <- 
-        df_list[["single-agent"]][sa_idx[[codrug]], codrug_cols, with = FALSE]
+      df_list[[sa_name]][sa_idx[[codrug]], selected_columns] <- 
+        df_list[[sa_name]][sa_idx[[codrug]], codrug_cols, with = FALSE]
     }
-    df_list[["single-agent"]][, codrug_ids] <- NULL
+    df_list[[sa_name]][, codrug_ids] <- NULL
 
-    selected_columns <- names(df_list[["single-agent"]])
+    selected_columns <- names(df_list[[sa_name]])
 
-    df_list[["single-agent"]] <- rbind(
-      df_list[["single-agent"]],
+    df_list[[sa_name]] <- rbind(
+      df_list[[sa_name]],
       control_sa[, selected_columns, with = FALSE]
     )
   }