Started adding documentation

NAMESPACE

@@ -1,11 +1,14 @@
 # Generated by roxygen2: do not edit by hand
 
 export(adaptive.bin)
+export(add_feature_ids)
 export(adjust.time)
 export(aggregate_by_rt)
+export(as_feature_sample_table)
 export(bigauss.esti)
 export(bigauss.esti.EM)
 export(bigauss.mix)
+export(check_files)
 export(clean_data_matrix)
 export(comb)
 export(compute_boundaries)
@@ -41,6 +44,7 @@ export(compute_template)
 export(compute_template_adjusted_rt)
 export(compute_uniq_grp)
 export(correct_time)
+export(correct_time_v2)
 export(count_peaks)
 export(create_aligned_feature_table)
 export(create_features_from_cluster)
@@ -63,6 +67,7 @@ export(get_features_in_rt_range)
 export(get_mzrange_bound_indices)
 export(get_num_workers)
 export(get_rt_region_indices)
+export(get_sample_name)
 export(get_single_occurrence_mask)
 export(get_times_to_use)
 export(hybrid)

R/adjust.time.R

@@ -2,6 +2,10 @@
 NULL
 #> NULL
 
+#' Combine template and sample features
+#' @param template_features Tibble Template feature table (mz, rt, cluster, sample_id).
+#' @param features Tibble Sample feature table (mz, rt, cluster, sample_id).
+#' @return Tibble Combined feature table (rbind).
 #' @export
 compute_comb <- function(template_features, features) {
   combined <- dplyr::bind_rows(
@@ -11,6 +15,12 @@ compute_comb <- function(template_features, features) {
   return(combined)
 }
 
+#' Select features to use for retention time alignment
+#' @description This function selects features present in both the sample
+#' feature table and template feature table given they have the same cluster,
+#' are adjacent in the combined table.
+#' @param combined Tibble Table with (mz, rt, cluster, sample_id).
+#' @return List of bool Returns list of bools with TRUE at each index where this condition is met.
 #' @export
 compute_sel <- function(combined) {
   l <- nrow(combined)
@@ -19,6 +29,11 @@ compute_sel <- function(combined) {
   return(sel)
 }
 
+#' Create two column table with paired sample and template retention times.
+#' @param combined Tibble Table with features from sample and template.
+#' @param sel list of bools List of bools indiciating which features to pair.
+#' See 'compute_sel'.
+#' @param j string Template sample_id.
 #' @export
 compute_template_adjusted_rt <- function(combined, sel, j) {
   all_features <- cbind(combined$rt[sel], combined$rt[sel + 1])
@@ -59,29 +74,34 @@ compute_corrected_features_v2 <- function(features, template_rt, delta_rt) {
   return(features |> dplyr::arrange_at(c("mz", "rt")))
 }
 
+#' Correct the rt in feature table based on paired feature rts and differences.
+#' @param features Tibble The feature table for which to correct rts.
+#' @param template_rt List of floats Template retention times for the paired features.
+#' @param delta_rt List of floats Differences between the paired rts.
+#' @return Tibble A table with corrected retention times.
 #' @export
-compute_corrected_features <- function(features, delta_rt, avg_time) {
+compute_corrected_features <- function(features, template_rt, delta_rt) {
   features <- features |> dplyr::arrange_at(c("rt", "mz"))
 
   corrected <- features$rt
   original <- features$rt
 
-  idx <- dplyr::between(original, min(delta_rt), max(delta_rt))
+  idx <- dplyr::between(original, min(template_rt), max(template_rt))
   to_correct <- original[idx]
   this.smooth <- ksmooth(
+    template_rt,
     delta_rt,
-    avg_time,
     kernel = "normal",
-    bandwidth = (max(delta_rt) - min(delta_rt)) / 5,
+    bandwidth = (max(template_rt) - min(template_rt)) / 5,
     x.points = to_correct
   )
 
   corrected[idx] <- this.smooth$y + to_correct
   lower_bound_adjustment <- mean(this.smooth$y[this.smooth$x == min(this.smooth$x)])
   upper_bound_adjustment <- mean(this.smooth$y[this.smooth$x == max(this.smooth$x)])
 
-  idx_lower <- original < min(delta_rt)
-  idx_upper <- original > max(delta_rt)
+  idx_lower <- original < min(template_rt)
+  idx_upper <- original > max(template_rt)
 
   corrected[idx_lower] <- corrected[idx_lower] + lower_bound_adjustment
   corrected[idx_upper] <- corrected[idx_upper] + upper_bound_adjustment
@@ -149,6 +169,7 @@ compute_template <- function(extracted_features) {
   return(tibble::as_tibble(template_features))
 }
 
+#' @export
 correct_time_v2 <- function(features, template) {
   if (unique(features$sample_id) == unique(template$sample_id))
     return(tibble::as_tibble(features))

R/unsupervised.R

@@ -2,6 +2,13 @@
 NULL
 #> NULL
 
+#' Read the metadata table, retention time data matrix and intensity data matrix
+#' and combine them into a single table
+#' @param metadata Tibble Feature metadata table with information concerning the peaks.
+#' @param rt_crosstab Tibble Data matrix with features on rows and samples on columns holding rt data.
+#' @param int_crosstab Tibble Data matrix with features on rows and samples on columns holding intensity data.
+#' @return Tibble A merged table containing all information.
+#' @export 
 as_feature_sample_table <- function(metadata, rt_crosstab, int_crosstab) {
   feature_names <- as.character(rt_crosstab$id)
   sample_names <- colnames(metadata)[-c(1:8)]
@@ -27,6 +34,9 @@ as_feature_sample_table <- function(metadata, rt_crosstab, int_crosstab) {
   return(data)
 }
 
+#' Check files whether they exist.
+#' @param filenames list of filenames Filenames to check whether they exist.
+#' @export
 check_files <- function(filenames) {
   missing <- !file.exists(filenames)
   missing_filenames <- paste0('\t', filenames[missing], collapse = '\n')
@@ -36,6 +46,10 @@ check_files <- function(filenames) {
   }
 }
 
+#' Get the sample name as basename of the file.
+#' @param filename string Name of the file.
+#' @return string Sample name.
+#' @export
 get_sample_name <- function(filename) {
   tools::file_path_sans_ext(basename(filename))
 }

R/utils.R

@@ -85,7 +85,10 @@ register_functions_to_cluster <- function(cluster) {
         'get_mzrange_bound_indices',
         'compute_mass_density',
         'l2normalize',
-        'compute_peaks_and_valleys'
+        'compute_peaks_and_valleys',
+        'as_feature_sample_table',
+        'check_files',
+        'get_sample_name'
     ))
     snow::clusterEvalQ(cluster, library("dplyr"))
     snow::clusterEvalQ(cluster, library("stringr"))