Major reworking of refit_curves.

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: luminary
 Title: Tools for Working with xMAP INTELLIFLEX Data
-Version: 0.0.1
+Version: 0.0.9
 Authors@R: 
     person(
     given   = "Hefin", 
@@ -14,7 +14,7 @@ Description: An R package for importing and working with Luminex data acquired
 License: GPL (>= 3) + file LICENSE
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
 RdMacros: mathjaxr
 Suggests: 
     knitr,
@@ -35,6 +35,6 @@ Imports:
     mathjaxr
 Config/testthat/edition: 3
 Depends: 
-    R (>= 2.10),
+    R (>= 3.0),
     S7
 LazyData: true

NAMESPACE

@@ -1,7 +1,5 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method(print,intelliframe)
-S3method(str,intelliframe)
 export(get_analytes)
 export(get_curve_data)
 export(get_expected)
@@ -14,6 +12,10 @@ export(intelliframe)
 export(plot_curves)
 export(read_xmap)
 export(refit_curves)
+export(update_recovery)
+export(update_recovery_avg)
+export(update_summary_data)
+export(update_well_data)
 importFrom(S7,props)
 importFrom(rlang,.data)
 importFrom(stats,reformulate)

R/extract_sheet_data.R

@@ -9,8 +9,7 @@
 #'
 #' @return A tibble.
 #'
-#' @examples
-#' 1 + 1
+#' @noRd
 extract_sheet_data <- function(file, sheet, numeric_data = TRUE) {
   readxl::read_xlsx(path = file, sheet = sheet, skip = 24, na = c("-", " ")) |>
     dplyr::filter(.data$Location != "Location") |>

R/plot_curves.R

@@ -75,7 +75,8 @@ plot_curves <- function(.intelliframe, analytes = NULL, type = "individual", int
       ggplot2::aes(.data[["Result"]], .data[["Predicted"]]),
       inherit.aes = FALSE
     ) +
-    ggplot2::scale_colour_brewer(type = "qual", palette = "Set1") +
+    # ggplot2::scale_colour_brewer(type = "qual", palette = "Set1") +
+    ggplot2::scale_colour_manual(values = c("Control" = "#E41A1C", "Standard" = "#377EB8", "Unknown" = "#4DAF4A")) +
     suppressWarnings(
       ggplot2::geom_point(
         data = experimental_data,

R/print.R

@@ -1,13 +1,13 @@
 #' print method for intelliframe class
 #'
+#'
 #' @param x An intelliframe object.
 #' @param max.level How many levels to print.
 #' @param ... Unused.
 #'
-#' @exportS3Method
-print.intelliframe <- function(x, max.level = 1, ...) {
-  counts <- get_well_data(x) |>
-    dplyr::summarise(.by = "Type", dplyr::n())
+#' @noRd
+method(print, intelliframe) <- function(x, max.level = 1, ...) {
+  counts <- dplyr::summarise(get_well_data(x), .by = "Type", dplyr::n())
 
   counts <- lapply(c("Standard", "Control", "Unknown"), function(.type) {
     if(.type %in% counts$Type) {

R/read_xmap.R

@@ -27,7 +27,13 @@ read_xmap <- function(file) {
   }) |> stats::setNames(well_data_sheets)
 
   messages <- extract_sheet_data(file, sheet = "Messages", numeric_data = FALSE)
-  excluded_wells <-  readxl::read_xlsx(file, sheet = "Excluded Wells", skip = 20)
+
+  excluded_wells <-  readxl::read_xlsx(
+    file,
+    sheet     = "Excluded Wells",
+    skip      = 20,
+    col_types = "text"
+  )
 
   expected <- sheet_data$Expected
 

R/refit_curves.R

@@ -56,6 +56,9 @@
 #' @param add_to_zeroes A numeric value (\code{0.1} by default) added to zeros during
 #'   the curve fitting procedure as it requires the concentration to be
 #'   log10-transformed.
+#' @param use_excluded Logical flag indicating whether wells with a value of
+#'   \code{TRUE} in the \code{Excluded} column are used to refit the curves and
+#'   calculate summary statistics. Defaults to \code{FALSE}.
 #' @param silent Logical flag indicating whether warnings and/or messages during
 #'   curve fitting should be silenced. Defaults to \code{FALSE}.
 #'
@@ -74,26 +77,42 @@
 #'
 #' @examples
 #' 1+1
-refit_curves <- function(.intelliframe, npars = "all", weight_method = "res", LPweight = 0.25, add_to_zeroes = 0.02, silent = FALSE) {
+refit_curves <- function(
+  .intelliframe,
+  npars         = "all",
+  weight_method = "res",
+  LPweight      = 0.25,
+  add_to_zeroes = 0.02,
+  use_excluded  = FALSE,
+  silent        = FALSE
+) {
 
   well_data <- get_well_data(.intelliframe)
 
   standards <-  dplyr::filter(well_data, .data[["Type"]] == "Standard")
 
+  ident_cols <- c(
+    "Plate", "Group", "Location", "Well ID", "Sample ID", "Standard", "Type",
+    "Analyte", "Expected"
+    )
+
+  excluded_wells <- well_data$Excluded
+
   expected <- get_expected(.intelliframe) |>
-    dplyr::select("Plate", "Group", "Well ID", "Sample ID", "Standard", "Type", "Analyte", "Expected")
+    dplyr::select(dplyr::all_of(ident_cols))
 
-  standard_list <- dplyr::left_join(
-    standards,
-    expected,
-    by = c("Plate", "Group", "Well ID", "Sample ID", "Standard", "Type", "Analyte", "Expected")
-  )
+  standard_list <- dplyr::left_join(standards, expected, by = ident_cols)
 
   standard_list <- split(standard_list, standard_list$Analyte)
 
   fits <- lapply(standard_list, function(analyte) {
+
+    if(!use_excluded) {
+      analyte <- analyte[-excluded_wells, ]
+    }
+
     non_zero <- ifelse(
-      analyte$Expected < 1,
+      analyte$Expected == 0,
       analyte$Expected + add_to_zeroes,
       analyte$Expected
     )
@@ -108,41 +127,32 @@ refit_curves <- function(.intelliframe, npars = "all", weight_method = "res", LP
     )
   })
 
+  intelliframe_out <- .intelliframe
+
   names(fits) <- names(standard_list)
 
-  suppressWarnings({
-    refitted <- lapply(names(fits), function(analyte) {
-      maximum <- max(standard_list[[analyte]]$MFI)
-      dplyr::filter(well_data, .data[["Analyte"]] == analyte) |>
-        dplyr::mutate(Result = nplr::getEstimates(fits[[analyte]], targets = .data[["MFI"]] / maximum)$x)
-    }) |> dplyr::bind_rows()
-  })
+  prop(intelliframe_out, "well_data") <-
+    update_well_data(well_data, fits, standard_list, silent)
 
-  intelliframe_out <- .intelliframe
-  intelliframe_out@well_data <- refitted
-
-  intelliframe_out@recovery <- dplyr::filter(refitted, .data[["Type"]] %in% c("Standard", "Control")) |>
-    dplyr::mutate(Recovery = dplyr::case_when(
-      .data[["Expected"]] == 0 ~ NA_real_,
-      .default = .data[["Result"]] / .data[["Expected"]]
-    )) |>
-    dplyr::select(-c("MFI", "Result", "Messages", "Exclude Reason", "Excluded", "Expected"))
-
-  intelliframe_out@recovery_avg <- intelliframe_out@recovery |>
-    dplyr::select(-.data[["Location"]]) |>
-    dplyr::left_join(
-      dplyr::select(intelliframe_out@recovery_avg, -.data[["Recovery"]]),
-      by = c("Plate", "Group", "Well ID", "Sample ID", "Standard", "Type", "Analyte")
-      ) |>
-    dplyr::select(
-      "Plate", "Group", "Location", "Well ID", "Sample ID", "Standard", "Type",
-      "Analyte", "Recovery"
-    ) |>
-    dplyr::summarise(
-      .by = c("Plate", "Group", "Location", "Well ID", "Sample ID", "Standard",
-              "Type", "Analyte"),
-      Recovery = mean(.data[["Recovery"]], na.rm = TRUE)
+  prop(intelliframe_out, "recovery") <-
+    update_recovery(well_data)
+
+  prop(intelliframe_out, "recovery_avg") <-
+    update_recovery_avg(
+      get_recovery(intelliframe_out),
+      get_recovery_avg(intelliframe_out),
+      use_excluded,
+      excluded_wells
+    )
+
+  prop(intelliframe_out, "summary_data") <-
+    update_summary_data(
+      well_data,
+      get_summary_data(intelliframe_out),
+      use_excluded,
+      excluded_wells
     )
 
   intelliframe_out
 }
+

R/update_curve_data.R

@@ -0,0 +1,68 @@
+lapply(names(fits), function(x) {
+
+  lloq <- dplyr::filter(
+    u5plex@summary_data,
+    Analyte == x,
+    Type == "Standard",
+    Result_CV <= 0.2,
+    Result_Avg / Expected > 0.8,
+    Result_Avg / Expected < 1.2
+  ) |>
+    dplyr::pull(Expected) |>
+    min()
+
+  blank <- dplyr::filter(
+    u5plex@well_data,
+    Analyte == x,
+    Type == "Standard",
+    Expected == 0
+  ) |>
+    dplyr::pull(MFI)
+
+  blank_targets <-
+    (mean(blank) + 2.5*sd(blank)) /
+    max(dplyr::filter(u5plex@well_data, Analyte == x)$MFI)
+
+  suppressWarnings({
+    suppressMessages({
+      mdd <- nplr::getEstimates(
+        fits[[x]],
+        targets = blank_targets
+      )$x
+    })
+  })
+
+  df_low <- data.frame(
+    x = 10^(fits[[x]]@x),
+    y = fits[[x]]@y
+  )[10^(fits[[x]]@x) %in% unique(10^(fits[[x]]@x))[1:3],]
+
+  fit_low <- nplr::nplr(
+    x        = df_low$x,
+    y        = df_low$y#,
+    # npars    = npars,
+    # method   = weight_method,
+    # LPweight = LPweight,
+    # silent   = silent
+  )
+
+  suppressWarnings({
+    suppressMessages({
+      lod <- nplr::getEstimates(
+        fit_low,
+        targets = blank_targets
+      )$x
+    })
+  })
+
+  tibble::tibble(
+    Analyte     = x,
+    Fit         = paste0("nplr ", fits[[x]]@npars, "PL"),
+    LLoQ        = lloq,
+    MDD         = mdd,
+    LoD         = lod,
+    `R Squared` = fits[[x]]@goodness$gof,
+    Slope       = fits[[x]]@pars$s
+  )
+}) |> dplyr::bind_rows()
+

R/update_recovery.R

@@ -0,0 +1,23 @@
+#' update_recovery
+#'
+#' Update the recovery property from an intelliframe object using up-to-date
+#'   well_data. Not meant to be called by the user.
+#'
+#' @param .well_data A tibble containing the well_data property.
+#'
+#' @return An intelliframe
+#' @export
+#'
+#' @noRd
+update_recovery <- function(.well_data){
+  dplyr::filter(.well_data, .data[["Type"]] %in% c("Standard", "Control")) |>
+
+    dplyr::mutate(
+      Recovery = dplyr::case_when(
+        .data[["Expected"]] == 0 ~ NA_real_,
+        .default = .data[["Result"]] / .data[["Expected"]]
+      )
+    ) |>
+
+    dplyr::select(-c("MFI", "Result", "Messages", "Exclude Reason", "Excluded", "Expected"))
+}

R/update_recovery_avg.R

@@ -0,0 +1,39 @@
+#' update_recovery_avg
+#'
+#' Update the recovery_avg property from an intelliframe object using up-to-date
+#'   recovery. Not meant to be called by the user.
+#'
+#' @param .recovery A tibble containing the recovery property.
+#' @param .recovery A tibble containing the original recovery_avg property.
+#' @param .use_excluded Logical flag indicating whether wells with a value of
+#'   \code{TRUE} in the \code{Excluded} column are used to calculate summary
+#'   statistics.
+#' @param .excluded_wells Logical vector indicating whether each well is
+#'   excluded.
+#'
+#' @return An intelliframe
+#' @export
+#'
+#' @noRd
+update_recovery_avg <- function(.recovery, .recovery_avg, .use_excluded, .excluded_wells) {
+  correct_location <- dplyr::select(.recovery, -Location) |>
+    dplyr::left_join(
+      dplyr::select(.recovery_avg, -Recovery),
+      by = c("Plate", "Group", "Well ID", "Sample ID", "Standard", "Type", "Analyte")
+    ) |>
+    dplyr::select(
+      "Plate", "Group", "Location", "Well ID", "Sample ID", "Standard", "Type",
+      "Analyte", "Recovery"
+    )
+
+  if(!.use_excluded) {
+    correct_location <- correct_location[-.excluded_wells, ]
+  }
+
+  dplyr::summarise(
+    correct_location,
+    .by = c("Plate", "Group", "Location", "Well ID", "Sample ID", "Standard",
+            "Type", "Analyte"),
+    Recovery = mean(Recovery, na.rm = TRUE)
+  )
+}