Fix fraction efficient

R/utils_fractions.R

@@ -264,41 +264,53 @@
 #' @return data.table
 #' @keywords internal
 .removeOverlappingFeatures = function(input) {
-    fraction_keep = Fraction = NULL
+    Fraction = NULL
 
     if (data.table::uniqueN(input$Fraction) > 1) {
-        input[, fraction_keep := .getCorrectFraction(.SD), 
-              by = "feature", 
-              .SDcols = c("feature", "Fraction", "Run", "Intensity")]
-        input = input[Fraction == fraction_keep]
-        input = input[, !(colnames(input) == "fraction_keep"), with = FALSE]
+        measurement_count = input[
+            !is.na(Intensity) & Intensity > 0,
+            .(n_obs = uniqueN(Run)),
+            by = .(feature, Fraction)
+        ]
+        measurement_count[, is_max := n_obs == max(n_obs), by = "feature"]
+        max_fractions = measurement_count[(is_max)]
+
+        fraction_map = .resolveFractionTies(input, max_fractions)
+        input = input[fraction_map, on = .(feature, Fraction), nomatch = 0]
     }
     input
 }
 
 
-#' Get a name of fraction with the largest number of measurements or the largest
-#' average intensity
+#' Resolve ties when multiple fractions share the maximum number of measurements
+#' for a given feature. In the case of a tie, the fraction with the highest
+#' mean intensity is selected.
 #' @param input output of `MSstatsPreprocess`
-#' @return character - label of the fraction that has most measurements or
-#' highest mean intensity for a given feature
+#' @param max_fractions data.table of fractions that share the maximum number
+#' of unique runs per feature, as produced by `.removeOverlappingFeatures`
+#' @return data.table with columns `feature` and `Fraction`, containing one
+#' selected fraction per feature
 #' @keywords internal
-.getCorrectFraction = function(input) {
-    Intensity = Run = Fraction = NULL
+.resolveFractionTies = function(input, max_fractions) {
+    tie_features = max_fractions[, .(n_ties = .N), by = "feature"][n_ties > 1, feature]
 
-    measurement_count = input[!is.na(Intensity) & Intensity > 0, 
-                              list(n_obs = data.table::uniqueN(Run)),
-                              by = "Fraction"]
-    which_max_measurements = which(measurement_count$n_obs == max(measurement_count$n_obs))
-    if (length(which_max_measurements) == 1L) {
-        return(unique(measurement_count$Fraction[which_max_measurements]))
+    if (length(tie_features) > 0) {
+        tied_fractions = max_fractions[feature %in% tie_features, .(feature, Fraction)]
+        avg_abundance = input[
+            tied_fractions, on = .(feature, Fraction), nomatch = 0
+        ][!is.na(Intensity) & Intensity > 0,
+          .(mean_abundance = mean(Intensity)),
+          by = .(feature, Fraction)]
+        best_tied = avg_abundance[, .SD[which.max(mean_abundance)], by = "feature"]
+        best_simple = max_fractions[
+            !feature %in% tie_features,
+            .(Fraction = Fraction[1]),
+            by = "feature"
+        ]
+        rbind(best_simple[, .(feature, Fraction)],
+              best_tied[, .(feature, Fraction)])
     } else {
-        input = input[Fraction %in% measurement_count$Fraction[which_max_measurements]]
-        average_abundance = input[!is.na(Intensity) & Intensity > 0, 
-                                  list(mean_abundance = mean(Intensity)),
-                                  by = "Fraction"]
-        which_max_abundance = which.max(average_abundance$mean_abundance)
-        unique(average_abundance$Fraction[which_max_abundance])
+        max_fractions[, .(Fraction = Fraction[1]), by = "feature"]
     }
 }
 

inst/tinytest/test_fractions.R

@@ -67,17 +67,34 @@ fractionated = data.table::data.table(
     Run = 1:12,
     Intensity = c(NA, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2)
 )
-picked_A = MSstatsConvert:::.getCorrectFraction(fractionated[feature == "A"])
-picked_B = MSstatsConvert:::.getCorrectFraction(fractionated[feature == "B"])
 ### More observations win
-expect_equal(picked_A, 2)
-### Higher average intensity wins
-expect_equal(picked_B, 2)
+expect_equal(
+    unique(MSstatsConvert:::.removeOverlappingFeatures(fractionated[feature == "A"])$Fraction),
+    2
+)
+### Higher average intensity wins on tie
+expect_equal(
+    unique(MSstatsConvert:::.removeOverlappingFeatures(fractionated[feature == "B"])$Fraction),
+    2
+)
 ### For full data
 expect_identical(
     MSstatsConvert:::.removeOverlappingFeatures(data.table::copy(fractionated)),
     fractionated[Fraction == 2]
 )
+### Non-tied fraction with high mean intensity should not be selected over tied fractions
+fractionated_third = data.table::data.table(
+    feature = rep("A", 9),
+    Fraction = c(rep(1, 3), rep(2, 3), rep(3, 3)),
+    Run = 1:9,
+    Intensity = c(1, 1, 1,    # Fraction 1: 3 obs, mean = 1 (ties for max n_obs)
+                  2, 2, 2,    # Fraction 2: 3 obs, mean = 2 (ties for max n_obs)
+                  10, NA, NA) # Fraction 3: 1 obs, mean = 10 (loses on n_obs but mean would win w/o fix)
+)
+expect_equal(
+    unique(MSstatsConvert:::.removeOverlappingFeatures(fractionated_third)$Fraction),
+    2
+)
 fractionated_tmt = fractionated = data.table::data.table(
     feature = rep(c("A", "B"), each = 6),
     Fraction = rep(rep(c(1, 2), each = 3), times = 2),