fixing Recal/processKnown function

R/RecalFunctions.R

@@ -1,3 +1,6 @@
+O_mass <- 15.9949146223
+H2_mass <- 2.01565
+
 #' Plot Spectrum Function
 #'
 #' This function generates a mass spectrum plot using ggplot2.
@@ -72,21 +75,27 @@ processMassList <- function(data) {
 #' @param kmd_col The column name for KMD in both datasets.
 #' @param z_col The column name for Z in both datasets.
 #' @param num_col The column name to store the calculated number of compounds.
-#' @param type The column name to store the type of compound.
+#' @param type_col The column name to store the type of compound.
+#' @param type A molecule type of the annotation.
+#' @param multiplier The multiplier for the num column. 
+#' @param element_mass The mass of the group used for correction.
 #' @param step_limit The limit for considering compounds.
 #' @param remove_indices Indices of rows to be removed from the result.
 #'
 #' @return A processed dataset with updated abundance and type columns.
-processKnown <- function(rest, known, kmd_col, z_col, num_col, type, step_limit, remove_indices) {
+processKnown <- function(rest, known, kmd_col, z_col, num_col, type_col, type, multiplier, element_mass, step_limit, remove_indices) {
   names(known)[2] <- "base_mass"
 
   step_result <- merge(rest, known, by.x = c(kmd_col, z_col), by.y = c(kmd_col, z_col))
-  step_result[[num_col]] <- round((step_result$Exp_mass - step_result$base_mass) / step_limit)
-  step_result[[type]] <- step_result[[type]] + step_result[[num_col]]
-  step_result$Type <- type
-  step_result$form <- paste(step_result[c("C", "H", "O", "N", "S", "P", "E", "S34", "N15", "D", "Cl", "Fl", "Cl37", "M", "NH4", "POE", "NOE")], sep = "_")
+  step_result[[num_col]] <- round((step_result$Exp_mass - step_result$base_mass) / element_mass)
+  step_result[[type_col]] <- step_result[[type_col]] + step_result[[num_col]]*multiplier
+  step_result$Type <- type 
+  step_result <- dplyr::mutate(
+    step_result,
+    form=paste(C, H, O, N, S, P, E, S34, N15, D, Cl, Fl, Cl37, M, NH4, POE, NOE, sep="_")
+  )
   step_result <- step_result[abs(step_result[[num_col]]) <= step_limit, ]
-  step_result <- step_result[-remove_indices, ]
+  step_result <- step_result[-remove_indices]
   return(step_result)
 }
 
@@ -217,10 +226,10 @@ Recal <- function(df,
     ############
     # Picking recalibrants with series
     # Process known_O
-    Step2 <- processKnown(Rest, RecalList[c(1:21, 24, 25)], "KMD_O", "z_O", "O_num", "O", step_O, c(10, 31))
+    Step2 <- processKnown(Rest, RecalList[c(1:21, 24, 25)], "KMD_O", "z_O", "O_num", "O", "O", 1, O_mass, step_O, c(10, 31))
 
     # Process known_H2
-    Step3 <- processKnown(Rest, RecalList[c(1:21, 27, 28)], "KMD_H2", "z_H2", "H2_num", "H2", step_H2, c(10, 31))
+    Step3 <- processKnown(Rest, RecalList[c(1:21, 27, 28)], "KMD_H2", "z_H2", "H2_num", "H", "H2", 2, H2_mass, step_H2, c(10, 31))
 
     Out <- rbind(Step2, Step3)
     Out2 <- dplyr::distinct(Out, Exp_mass)
@@ -287,33 +296,11 @@ Recal <- function(df,
     ############
     # Picking recalibrants with series
 
-    knownO <- RecalList[c(1:22, 25, 26)]
-    names(knownO)[2] <- "base_mass"
-    Step2 <- merge(Rest, knownO, by.x = c("KMD_O", "z_O"), by.y = c("KMD_O", "z_O"))
-    Step2$O_num <- round(((Step2$Exp_mass - Step2$base_mass)) / 15.9949146223)
-    Step2$O <- Step2$O + Step2$O_num
-    Step2$Type <- "O"
-    Step2$form <- paste(Step2$C, Step2$H, Step2$O, Step2$N, Step2$S, Step2$P, Step2$E, Step2$S34,
-      Step2$N15, Step2$D, Step2$Cl, Step2$Fl, Step2$Cl37, Step2$M, Step2$NH4,
-      Step2$POE, Step2$NOE,
-      sep = "_"
-    )
-    Step2 <- Step2[abs(Step2$O_num) <= step_O, ]
-    Step2 <- Step2[-c(11, 13, 33)] #
-
-    knownH2 <- RecalList[c(1:22, 28, 29)]
-    names(knownH2)[2] <- "base_mass"
-    Step3 <- merge(Rest, knownH2, by.x = c("KMD_H2", "z_H2"), by.y = c("KMD_H2", "z_H2"))
-    Step3$H2_num <- round(((Step3$Exp_mass - Step3$base_mass)) / 2.01565)
-    Step3$H <- Step3$H + 2 * Step3$H2_num
-    Step3$Type <- "H2"
-    Step3$form <- paste(Step3$C, Step3$H, Step3$O, Step3$N, Step3$S, Step3$P, Step3$E, Step3$S34,
-      Step3$N15, Step3$D, Step3$Cl, Step3$Fl, Step3$Cl37, Step3$M, Step3$NH4,
-      Step3$POE, Step3$NOE,
-      sep = "_"
-    )
-    Step3 <- Step3[abs(Step3$H2_num) <= step_H2, ]
-    Step3 <- Step3[-c(11, 13, 33)]
+    # Process known O16
+    Step2 <- processKnown(Rest, RecalList[c(1:22, 25, 26)], "KMD_O", "z_O", "O_num", "O", "O", 1, O_mass, step_O, c(11, 13, 33))
+
+    # Process known_H2
+    Step3 <- processKnown(Rest, RecalList[c(1:22, 28, 29)], "KMD_H2", "z_H2", "H2_num", "H", "H2", 2, H2_mass, step_H2, c(11, 13, 33))
 
     Out <- rbind(Step2, Step3)
     Out2 <- dplyr::distinct(Out, Exp_mass, .keep_all = TRUE)

tests/testthat/test-RecalFunctions.R

@@ -56,3 +56,29 @@ patrick::with_parameters_test_that("RecalList works",
   },
   mode = c("neg", "pos"),
 )
+
+test_that("Replicate the Recal bug", {
+  unambig <- read.delim("test-data/bug_unambig.tabular")
+  mono <- read.delim("test-data/bug_mono.tabular")
+  iso <- read.delim("test-data/bug_iso.tabular")
+  recallist <- read.delim("test-data/bug_recalseries.tabular")
+
+  actual <- MFAssignR::Recal(
+      df = unambig,
+      peaks = mono,
+      isopeaks = iso,
+      mode = "neg",
+      SN = 6*346.0706,
+      series1 = recallist$Series[1],
+      series2 = recallist$Series[2],
+      series3 = recallist$Series[3],
+      series4 = recallist$Series[4],
+      series5 = recallist$Series[5],
+      step_O = 3,
+      step_H2 = 5,
+      mzRange = 50,
+      CalPeak = 150
+    )
+    expected <- readRDS("test-data/bug_recal_expected.rds")
+    expect_equal(actual, expected)
+})