Update documentation and NAMESPACE

NAMESPACE

@@ -70,8 +70,10 @@ importFrom(grDevices,colorRamp)
 importFrom(grDevices,rgb)
 importFrom(keras,load_model_hdf5)
 importFrom(keras,predict_classes)
+importFrom(stats,IQR)
 importFrom(stats,approx)
 importFrom(stats,median)
+importFrom(stats,quantile)
 importFrom(stats,sd)
 importFrom(utils,data)
 importFrom(utils,read.csv)

R/IL_methods.R

@@ -43,27 +43,35 @@ function(struct, id, rts, minint = Inf) {
 
 #'@title exportIL
 #'@md
-#'@description Organizes the IL entries into multiple injections
-#' taking into account the user-specified parameters. Outputs a
-#' single or multiple csv files that serve as input for the MS
-#' to performed MSMS analysis.
+#'@description Organizes the IL entries into multiple injections taking into
+#' account the user-specified parameters. Outputs a single or multiple csv
+#' files that serve as input for the MS to performed MSMS analysis.
 #'
 #'@param struct The RHermesExp object.
-#'@param id The IL ID in the RHermesExp object. The IDs are
-#' assigned by the order in which the IL are generated.
-#'@param folder A string containing the folder to save the IL
-#' csv/s into. By default will be your working directory
-#'@param maxOver Numeric, very important. It's the number of
-#' mz-rt segments that can be monitored at the same time by the
-#' MS instrument. Higher numbers lead to less injections but the
-#' number of scans for each IL entry will be reduced and gives
-#' problems when deconvoluting the MS2 spectras.
-#'@param sepFiles Logical, whether to generate a single csv file
-#' or multiple csvs, each corresponding to each
-#' injection/chromatographic run. From our experience with an
-#' Orbitrap Fusion, separate csvs will simplify the task.
-#'@return Nothing. As a side effect, it generates one/multiple
-#' .csv files with the inclusion list data
+#'@param id The IL ID in the RHermesExp object. The IDs are assigned by the
+#' order in which the IL are generated.
+#'@param file A string containing the folder to save the IL csv/s into and the
+#' basename for the file. By default will be your working directory and the
+#' default name is 'InclusionList' as in './InclusionList'.
+#'@param mode Whether to plan set of continuous MS2 entries (PRM), adaptative
+#' injection time scans at the apexes of the entries' peaks or both (which
+#' optimizes instrument free time). Options are: "continuous", "deep" or
+#' "both".
+#'@param maxOver Numeric, very important. It's the number of mz-rt segments that
+#' can be monitored at the same time by the MS instrument. Higher numbers lead
+#' to less injections but the number of scans for each IL entry will be reduced
+#' and gives problems when deconvoluting the MS2 spectras. It is ignored if
+#' mode = "deep". Defaults to 5.
+#'@param defaultIT Numeric, the default IT in ms for continuous MS2 scans (only
+#' aplicable in Orbitrap instruments and for "continuous" and "both" modes).
+#' Defaults to 100ms.
+#'@param maxInjections Numeric, the maximum number of planned injections to
+#' export. Defaults to 9999 to export all of them.
+#'@param sepFiles Logical, whether to generate a single csv file or multiple
+#' csvs, each corresponding to each injection/chromatographic run. From our
+#' experience with an Orbitrap Fusion, separate csvs will simplify the task.
+#'@return Nothing. As a side effect, it generates one/multiple .csv files with
+#' the inclusion list data
 #'@examples
 #'if(FALSE){
 #' exportIL(myHermes, 1, 'C:/SomeFolder', maxOver = 5, sepFiles = FALSE)
@@ -129,17 +137,17 @@ injectionPlanner <- function(IL, injections, maxover, byMaxInt = TRUE,
  idx <- which(is.na(IL$start) | is.na(IL$end)) #NA depuration
  if (length(idx) != 0) {IL <- IL[-idx, ]}
  plan <- list()
- 
+
  if(mode != "continuous"){
  message("Calculating high IT scans")
  deep_IL <- calculate_deep_IL(IL)
  } else {
  deep_IL <- data.frame()
  }
- 
+
  mint <- min(IL$start)
  maxt <- max(IL$end)
- 
+
  if(mode %in% c("continuous", "both")){
  while (nrow(IL) != 0 & injections > 0) {
  timeInt <- seq(mint,
@@ -155,7 +163,7 @@ injectionPlanner <- function(IL, injections, maxover, byMaxInt = TRUE,
  }
  curinj <- IL[ok_entries, ]
  IL <- IL[-ok_entries, ]
- 
+
  if(mode == "both" & any(OL == 0)){
  deep_ok_entries <- c()
  for (i in seq_len(nrow(deep_IL))) {
@@ -171,7 +179,7 @@ injectionPlanner <- function(IL, injections, maxover, byMaxInt = TRUE,
  curinj <- rbind(curinj, deep_curinj, fill = TRUE)
  }
  }
- 
+
  plan <- c(plan, list(curinj))
  injections <- injections - 1
  }
@@ -219,7 +227,7 @@ injectionPlanner <- function(IL, injections, maxover, byMaxInt = TRUE,
  }
  }
 
- 
+
  if (nrow(IL) != 0) {
  message(paste0(nrow(IL), "ILs haven't been added to the injection plan",
  " due to lack of space. Try again with more injections,",
@@ -249,10 +257,10 @@ setMethod("show", "RHermesIL", function(object){
 })
 
 calculate_XIC_estimation <- function(raw, mzs, rts){
- points <- filter(raw, between(mz, mzs[1], mzs[2]))
- points <- filter(points, between(rt, rts[1], rts[2]))
+ points <- filter(raw, between(.data$mz, mzs[1], mzs[2]))
+ points <- filter(points, between(.data$rt, rts[1], rts[2]))
  xic <- sapply(unique(points$rt), function(rt){
- sum(points$rtiv[points$rt == rt]) 
+ sum(points$rtiv[points$rt == rt])
  })
  xic <- data.frame(rt = unique(points$rt), int = xic)
  return(xic)
@@ -293,10 +301,11 @@ calculate_best_interval <- function(scans, objective = 1e6, maxIT = 2000){
  intervals <- lapply(apexes, function(apex){
  maxt <- min(max(apex - min(scans$rt), max(scans$rt) - apex), maxIT/1000)
  scans <- approx(scans$rt, scans$int, n = 1000) %>% do.call(rbind, .) %>%
- t %>% as.data.frame %>% rename(rt = x, rtiv = y)
+ t %>% as.data.frame
  for(t in seq(0.1, maxt, 0.01)){
  integral <- calculate_integral(filter(scans,
- between(rt, apex-t, apex+t)))
+ between(.data$x,
+ apex-t, apex+t)))
  if(integral > objective){return(c(apex-t, apex+t))}
  }
  return(c(apex-maxt, apex+maxt))
@@ -312,7 +321,7 @@ calculate_integral <- function(scans){
 # Authorship Evan Friedland, Stackoverflow #6836409
 inflect <- function(x, threshold = 1){
  up <- sapply(1:threshold, function(n) c(x[-(seq(n))], rep(NA, n)))
- down <- sapply(-1:-threshold, function(n){ 
+ down <- sapply(-1:-threshold, function(n){
  c(rep(NA, abs(n)),
  x[-seq(length(x), length(x) - abs(n) + 1)])
  })

R/Metadata_methods.R

@@ -164,7 +164,7 @@ function(struct, db = "hmdb", adcharge = 1, admult = 1, DBfile = "",
  row.names(struct@metadata@ExpParam@adlist) <- NULL
  if(all(!is.na(adlist))){
  struct@metadata@ExpParam@adlist <- filter(adlist(struct),
- adduct %in% adlist)
+ .data$adduct %in% adlist)
  if(nrow(adlist(struct)) == 0){
  warning("No adducts remaining, please check the adduct names.")
  }

R/SOI_functions.R

@@ -157,7 +157,7 @@ PLprocesser <- function(PL, ExpParam, SOIParam, blankPL = NA, filename) {
  }
  Groups <- distinct(Groups)
 
- 
+
  ## Initial Peak Retrieval
  message("Initial peak retrieval:")
  peakscol <- lapply(seq_len(nrow(Groups)), retrievePeaks,
@@ -242,7 +242,7 @@ densityProc <- function(x, DataPL, h){
  message("Running Density Filter")
  BinRes <- densityFilter(DataPL, h, rtbin, "M0", shift)
  cutoff <- BinRes[[1]] * scanspercent
- 
+
  #Correcting CUT < 1 cases to avoid errors (eg. if it was 0, any time region
  #would be included). Added a 1 for robustness.
  cutoff[cutoff < 1] <- median(c(cutoff[cutoff > 1], 1))
@@ -477,6 +477,7 @@ blankSubstraction <- function(Groups, blankPL){
  Groups <- rbind(sure, Groups)
 }
 
+#'@importFrom stats IQR quantile
 firstCleaning <- function(i, Groups, blankPL){
  cur <- Groups[i, ]
  st <- cur$start
@@ -497,11 +498,11 @@ firstCleaning <- function(i, Groups, blankPL){
  (quantile(blankpks$rtiv, 0.25) + quantile(blankpks$rtiv, 0.75))
 
  if(is.na(sampleCV) | is.na(blankCV)){return(FALSE)}
- 
+
  sampleMax <- max(peaks$rtiv)
  # blankMax <- max(blankpks$rtiv)
  q90_ratio <- quantile(peaks$rtiv,0.9) / quantile(blankpks$rtiv,0.9)
- 
+
  #We have to be restrictive with the conditions, otherwise we collect junk
  if (sampleCV/blankCV > 5) {return(TRUE)}
  if (q90_ratio > 3 & sampleMax > 15000) {return(TRUE)}
@@ -525,7 +526,7 @@ prepareNetInput <- function(i, Groups, blankPL){
  smooth_pks <- data.frame(approx(x = peaks,
  xout = seq(from = st, to = end,
  length.out = Npoints),
- rule = 1, ties = min)) 
+ rule = 1, ties = min))
  smooth_pks[is.na(smooth_pks[, "y"]), "y"] <- 0
  blankpks <- blankPL[.(f)] %>% filter(., .data$rt >= st - deltat &
  .data$rt <= end + deltat &
@@ -573,7 +574,7 @@ parallelFilter <- function(anot, ScanResults, bins, timebin){
  mint <- min(data)
  maxt <- max(data)
  res <- rep(0, length(bins))
- 
+
  #Shortcut to get in which bin each point is
  l <- table(ceiling((data - bins[1]) / timebin))
  if (length(l) == 0) return(res)
@@ -586,14 +587,14 @@ densityFilter <- function(ScanResults, h, timebin, iso = "M0", tshift = 0) {
  rtmin <- floor(min(h$retentionTime))
  rtmax <- ceiling(max(h$retentionTime))
  bins <- seq(from = rtmin - tshift, to = rtmax + tshift, by = timebin)
- 
+
  #Getting how many scans were taken on each bin
  scans <- lapply(bins, function(curbin) {
  return(h %>% filter(., .data$retentionTime > curbin &
  .data$retentionTime <= curbin + timebin) %>%
  dim(.) %>% .[1])
  })
- 
+
  #Counting how many scan entries are on each bin
  setkeyv(ScanResults, c("formv", "rt"))
  RES <- lapply(unique(ScanResults$formv), parallelFilter, ScanResults,
@@ -656,21 +657,23 @@ rho_chaos <- function(data, nlevels = 20, fillGaps = TRUE){
 
 #### filterSOI-related ####
 
-#' @title filterSOI
-#' @author Roger Gine
-#' @description Performs a series of filters and quality checks to a given SOI
-#' list, removing unwanted SOIs in the process.
-#' @inheritParams findSOI
-#' @param id ID of the SOI list to be filtered/checked.
-#' @param minint Minimun SOI intensity. All SOIs below this value will be
-#' removed from the SOI list
-#' @param isofidelity Boolean. Whether to perform an isotopic fidelity check.
-#' @return A filtered SOI list.
+#'@title filterSOI
+#'@author Roger Gine
+#'@description Performs a series of filters and quality checks to a given SOI
+#' list, removing unwanted SOIs in the process.
+#'@inheritParams findSOI
+#'@param id ID of the SOI list to be filtered/checked.
+#'@param minint Minimun SOI intensity. All SOIs below this value will be removed
+#' from the SOI list
+#'@param isofidelity Boolean. Whether to perform an isotopic fidelity check.
+#'@param minscore Numeric. Minimum value (between 0 and 1) of isofidelity to
+#' retain an entry. Defaults to 0.8.
+#'@return A filtered SOI list.
 #' @examples
 #'\dontshow{struct <- readRDS(system.file("extdata", "exampleObject.rds",
 #' package = "RHermes"))}
 #' struct <- filterSOI(struct, id = 1, minint = 10000, isofidelity = TRUE)
-#' @export
+#'@export
 setGeneric("filterSOI", function(struct, id, minint = 1e4, isofidelity, minscore = 0.8) {
  standardGeneric("filterSOI")
 })