Gdr 2188

DESCRIPTION

@@ -2,8 +2,8 @@ Type: Package
 Package: gDRcore
 Title: Processing functions and interface to process and analyze drug
     dose-response data
-Version: 1.7.6
-Date: 2025-07-15
+Version: 1.7.7
+Date: 2025-07-28
 Authors@R: c(
     person("Bartosz", "Czech", , "bartosz.czech@contractors.roche.com", role = "aut",
            comment = c(ORCID = "0000-0002-9908-3007")),

NEWS.md

@@ -1,3 +1,6 @@
+## gDRcore 1.7.7 - 2025-07-28
+* replace `row/col_fittings` in source for matrix Metrics assay
+
 ## gDRcore 1.7.6 - 2025-07-15
 * swap drugs to unify combination order
 

R/combinations-isobolograms.R

@@ -1,9 +1,9 @@
 #' @keywords combinations
 calculate_Loewe <- function(
     df_mean, 
-    row_fittings, 
-    col_fittings, 
-    codilution_fittings, 
+    drug_2, 
+    drug_1, 
+    codilution, 
     series_identifiers,
     normalization_type,
     conc_margin = 10 ^ 0.5,
@@ -34,22 +34,22 @@ calculate_Loewe <- function(
   max1_cap <- gDRutils::round_concentration(max(axis_1$conc_1) * conc_margin)
   max2_cap <- gDRutils::round_concentration(max(axis_2$conc_2) * conc_margin)
 
-  data.table::setorder(row_fittings, -cotrt_value)
-  data.table::setorder(col_fittings, cotrt_value)
-  if (!is.null(codilution_fittings)) {
-    data.table::setorder(codilution_fittings, -ratio)
-    codilution_fittings <- 
-      codilution_fittings[codilution_fittings$fit_type %in% 
+  data.table::setorder(drug_2, -cotrt_value)
+  data.table::setorder(drug_1, cotrt_value)
+  if (!is.null(codilution)) {
+    data.table::setorder(codilution, -ratio)
+    codilution <- 
+      codilution[codilution$fit_type %in% 
                           "DRC3pHillFitModelFixS0", ]
     # more restrictive to be sure that it adds fit quality
-    codilution_fittings <-
-      codilution_fittings[codilution_fittings$N_conc >= min_n_conc &
-                            codilution_fittings$r2 > min_r2, ]
+    codilution <-
+      codilution[codilution$N_conc >= min_n_conc &
+                            codilution$r2 > min_r2, ]
   }
   futile.logger::flog.debug(
     "codilution fittings after transformation/filtering:"
   )
-  futile.logger::flog.debug(knitr::kable(codilution_fittings))
+  futile.logger::flog.debug(knitr::kable(codilution))
 
   for (isobol_value in iso_cutoffs) { # run through the different isobolograms
 
@@ -58,11 +58,11 @@ calculate_Loewe <- function(
     # filtered_cf can be empty (i.e. no rows) - this edge case is handled in 
     # calculate_isobolograms
     filtered_cf <- 
-      codilution_fittings[codilution_fittings$x_inf < (isobol_value - .1), ]
+      codilution[codilution$x_inf < (isobol_value - .1), ]
 
     df_iso <- calculate_isobolograms(
-      row_fittings, 
-      col_fittings, 
+      drug_2, 
+      drug_1, 
       filtered_cf, 
       isobol_value, 
       max1_cap, 
@@ -328,35 +328,35 @@ get_isocutoffs <- function(df_mean, normalization_type) {
 
 
 #' @keywords combinations
-calculate_isobolograms <- function(row_fittings, 
-                                   col_fittings, 
-                                   codilution_fittings, 
+calculate_isobolograms <- function(drug_2, 
+                                   drug_1, 
+                                   codilution, 
                                    isobol_value,
                                    max1_cap, 
                                    max2_cap) {
 
     # cutoff point by row
     conc2 <- gDRutils::predict_conc_from_efficacy(
       efficacy = isobol_value,
-      x_inf = row_fittings$x_inf,
-      x_0 = row_fittings$x_0,
-      ec50 = row_fittings$ec50,
-      h = row_fittings$h
+      x_inf = drug_2$x_inf,
+      x_0 = drug_2$x_0,
+      ec50 = drug_2$ec50,
+      h = drug_2$h
     )
-    row_iso <- data.table::data.table(conc_1 = row_fittings[, cotrt_value],
+    row_iso <- data.table::data.table(conc_1 = drug_2[, cotrt_value],
       conc_2 = conc2,
       fit_type = "by_row")
 
     # cutoff point by columns
     conc1 <- gDRutils::predict_conc_from_efficacy(
       efficacy = isobol_value,
-      x_inf = col_fittings$x_inf,
-      x_0 = col_fittings$x_0,
-      ec50 = col_fittings$ec50,
-      h = col_fittings$h
+      x_inf = drug_1$x_inf,
+      x_0 = drug_1$x_0,
+      ec50 = drug_1$ec50,
+      h = drug_1$h
     )
     col_iso <- data.table::data.table(conc_1 = conc1,
-      conc_2 = col_fittings[, cotrt_value],
+      conc_2 = drug_1[, cotrt_value],
       fit_type = "by_col")
 
     xy_iso <- data.table::rbindlist(list(row_iso, col_iso))
@@ -365,29 +365,29 @@ calculate_isobolograms <- function(row_fittings,
 
     # cutoff point by diagonal (co-dilution)
     # co-dil is given as concentration of drug 1
-    if (!is.null(codilution_fittings) && NROW(codilution_fittings) > 1) {
+    if (!is.null(codilution) && NROW(codilution) > 1) {
       conc_mix <- gDRutils::predict_conc_from_efficacy(
         efficacy = isobol_value,
-        x_inf = codilution_fittings$x_inf,
-        x_0 = codilution_fittings$x_0,
-        ec50 = codilution_fittings$ec50,
-        h = codilution_fittings$h
+        x_inf = codilution$x_inf,
+        x_0 = codilution$x_0,
+        ec50 = codilution$ec50,
+        h = codilution$h
       ) 
       codil_iso <- data.table::data.table(
-        conc_1 = conc_mix / (1 + codilution_fittings$ratio),
-        conc_2 = conc_mix / (1 + (1 / codilution_fittings$ratio)),
+        conc_1 = conc_mix / (1 + codilution$ratio),
+        conc_2 = conc_mix / (1 + (1 / codilution$ratio)),
         fit_type = "by_codil"
       )
 
       # Keep isobologram values within matrix values.
       capped_idx <- codil_iso$conc_1 > max1_cap | codil_iso$conc_2 > max2_cap
       codil_iso$conc_1[capped_idx] <- pmin(
         max1_cap, 
-        codilution_fittings$conc_ratio * max2_cap
+        codilution$conc_ratio * max2_cap
       )[capped_idx]
       codil_iso$conc_2[capped_idx] <- pmin(
         max2_cap, 
-        max1_cap / codilution_fittings$conc_ratio
+        max1_cap / codilution$conc_ratio
       )[capped_idx]
 
       isobologram_values <- data.table::rbindlist(list(xy_iso, codil_iso), fill = TRUE)

R/fit_SE.combinations.R

@@ -151,71 +151,71 @@ fit_SE.combinations <- function(se,
       # fit by column: the series in the primary identifier, the cotrt is the 
       # secondary one
 
-      col_fittings <- fit_combo_cotreatments(
+      drug_1 <- fit_combo_cotreatments(
         avg_subset,
         series_id = id, 
         cotrt_id = id2, 
         norm_type
       )
-      col_fittings <- if (all(is.na(col_fittings$fit_type))) {
-        x <- col_fittings[1, ]
+      drug_1 <- if (all(is.na(drug_1$fit_type))) {
+        x <- drug_1[1, ]
         x[, "cotrt_value"] <- NA
         x
       } else {
-        na.omit(col_fittings, col = "fit_type")
+        na.omit(drug_1, col = "fit_type")
       }
 
       # fit by row (flipped): the series in the secondary identifier, the 
       # cotrt is the primary one
-      row_fittings <- fit_combo_cotreatments(
+      drug_2 <- fit_combo_cotreatments(
         avg_subset,
         series_id = id2, 
         cotrt_id = id, 
         norm_type
       )
 
-      row_fittings <- na.omit(row_fittings, col = "fit_type")
+      drug_2 <- na.omit(drug_2, col = "fit_type")
 
       # fit by codilution (diagonal)
-      codilution_fittings <- fit_combo_codilutions(
+      codilution <- fit_combo_codilutions(
         avg_subset,
         series_identifiers, 
         norm_type
       )
-      codilution_fittings <- na.omit(codilution_fittings, col = "fit_type")
+      codilution <- na.omit(codilution, col = "fit_type")
 
       # apply the fit to get smoothed data: results per column
       # (along primary identifier for each value of the secondary identifier)
       complete_subset$col_values <- map_ids_to_fits(
         pred = complete_subset[[id]],
         match_col = complete_subset[[id2]], 
-        col_fittings, 
+        drug_1, 
         "cotrt_value"
       )
       # apply the fit the get smoothed data: results per row
       # (along secondary identifier for each value of the primary identifier)
       complete_subset$row_values <- map_ids_to_fits(
         pred = complete_subset[[id2]],
         match_col = complete_subset[[id]], 
-        row_fittings, "cotrt_value"
+        drug_2, "cotrt_value"
       )
-      metrics_names <- c("col_fittings", "row_fittings")
-      if (!is.null(codilution_fittings)) {
+      metrics_names <- c("drug_1", "drug_2")
+      if (!is.null(codilution)) {
         # apply the fit the get smoothed data: codilution results (along sum 
         # of identifiers for each ratio)
         complete_subset$codil_values <- map_ids_to_fits(
           pred = complete_subset[[id2]] + complete_subset[[id]],
           match_col = gDRutils::round_concentration(complete_subset[[id2]] / complete_subset[[id]], 1),
-          codilution_fittings, 
+          codilution, 
           "ratio"
         )
-        metrics_names <- c(metrics_names, "codilution_fittings")
+        metrics_names <- c(metrics_names, "codilution")
       } 
 
       metrics_merged <- data.table::rbindlist(mget(metrics_names), fill = TRUE)
-      # we need it to distinguish which rows are col_fittings/row_fittings 
+      # we need it to distinguish which rows are drug_1/drug_2 
       # and codilution_fitting
-      metrics_merged$source <- rep(
+      metrics_merged$dilution_drug <- rep(
         metrics_names, 
         vapply(mget(metrics_names), nrow, numeric(1))
       )
@@ -288,13 +288,13 @@ fit_SE.combinations <- function(se,
       ]
       isobologram_out <- if (
           sum((av_matrix_dense[[id]] * av_matrix_dense[[id2]]) > 0) > 9 &&
-          min(row_fittings$cotrt_value) == 0
+          min(drug_2$cotrt_value) == 0
         ) {
           calculate_Loewe(
             av_matrix, 
-            row_fittings, 
-            col_fittings, 
-            codilution_fittings,
+            drug_2, 
+            drug_1, 
+            codilution,
             series_identifiers = c(id, id2), 
             normalization_type = norm_type
           )
@@ -337,13 +337,13 @@ fit_SE.combinations <- function(se,
       excess$row_id <-
         isobologram_out$df_all_iso_points$row_id <-
         isobologram_out$df_all_iso_curves$row_id <-
-        col_fittings$row_id <-
+        drug_1$row_id <-
         scores$row_id <-
         metrics_merged$row_id <- i
       excess$col_id <-
         isobologram_out$df_all_iso_points$col_id <-
         isobologram_out$df_all_iso_curves$col_id <-
-        col_fittings$col_id <-
+        drug_1$col_id <-
         scores$col_id <-
         metrics_merged$col_id <- j