Remove redundant directories

src/alignment.r

@@ -1,10 +1,6 @@
 library(Biostrings)
 library(parallel)
 
-#' Import and process the TCR and VJ sequences
-#'
-#' @param file A file path with the sequences after applying a read simulator
-#' @return A \code{list} with the TCR sequences and VJ sequences
 parse_data <- function(file) {
   reversed_sequences <- Biostrings::readQualityScaledDNAStringSet(file)
   sequences <- Biostrings::reverseComplement(reversed_sequences)
@@ -15,10 +11,6 @@ parse_data <- function(file) {
   return(list(sequences, vj_segments))
 }
 
-#' Extracts the VJ metadata from the sequences read identifier
-#'
-#' @param metadata The read identifier of a sequence
-#' @return A \code{list} with the V and J gene identifier
 parse_metadata <- function(metadata) {
   id_elements <- unlist(strsplit(metadata, split = " "))
   v_identifier <- id_elements[2]
@@ -26,24 +18,12 @@ parse_metadata <- function(metadata) {
   return(list(v_id = v_identifier, j_id = j_identifier))
 }
 
-#' Fetches the sequence that matches the VJ gene identifier
-#'
-#' @param names The names of the VJ sequences
-#' @param vdj_segments A \code{DNAStringSet} containing the VJ sequences
-#' @param id The read identifier of a sequence
-#' @return A \code{character} containing the gene sequence
 match_id_sequence <- function(names, vdj_segments, id) {
   matches <- grep(names, pattern = id)
   row <- matches[1]
   return(as.character(vdj_segments[row]))
 }
 
-#' Gets the V and J sequences for a particular read identifier
-#'
-#' @param metadata The read identifier of a sequence
-#' @param names The names of the VJ sequences
-#' @param vdj_segments A \code{DNAStringSet} containing the VJ sequences
-#' @return A \code{list} with the V and J sequences
 get_vj_sequence <- function(metadata, names, vdj_segments) {
   identifiers <- parse_metadata(metadata)
   v_sequence <- match_id_sequence(names, vdj_segments, id = identifiers["v_id"])
@@ -51,11 +31,6 @@ get_vj_sequence <- function(metadata, names, vdj_segments) {
   return(list(v_seq = v_sequence, j_seq = j_sequence))
 }
 
-#' Obtains the VJ sequences for all the TCR sequences
-#'
-#' @param sequences A \code{QualityScaledDNAStringSet} with the TCR sequences
-#' @param vdj_segments A \code{DNAStringSet} containing the VJ sequences
-#' @return A \code{data.frame} with the V and J sequences
 fetch_vj_sequences <- function(sequences, vdj_segments) {
   vj_sequences <- sapply(names(sequences),
     names(vdj_segments),
@@ -66,11 +41,6 @@ fetch_vj_sequences <- function(sequences, vdj_segments) {
   return(results)
 }
 
-#' Perform a pairwise alignment of a sequence with the canonical V or J sequence
-#'
-#' @param sequence A \code{DNAString} containing the TCR sequences
-#' @param vdj_segment A \code{DNAString} containing the V or J sequence
-#' @return A \code{PairwiseAlignments}
 align_sequence <- function(sequence, vdj_segment) {
   return(Biostrings::pairwiseAlignment(
     subject = sequence,
@@ -80,13 +50,6 @@ align_sequence <- function(sequence, vdj_segment) {
   ))
 }
 
-#' Computes the coordinate shift of the Cysteine due to indels
-#'
-#' @param insertion An \code{IRanges} containing the insertions
-#' @param deletion An \code{IRanges} containing the deletions
-#' @param cys A \code{list} with the Cysteine coordinates
-#' @param alignment A \code{PairwiseAlignments}
-#' @return A \code{list} with the delta of the Cysteine coordinates
 handle_indels <- function(insertion, deletion, cys, alignment) {
   ins_start <- sum(Biostrings::width(deletion[start(deletion) <= cys$start]))
   ins_end <- sum(Biostrings::width(deletion[end(deletion) <= cys$end]))
@@ -97,10 +60,6 @@ handle_indels <- function(insertion, deletion, cys, alignment) {
   return(list("start" = ins_start - gaps, "end" = ins_end - gaps))
 }
 
-#' Find the coordinates of the first Cysteine of the HVR
-#'
-#' @param alignment A \code{PairwiseAlignments}
-#' @return A \code{list} with the Cysteine coordinates
 get_cys_coordinates <- function(alignment) {
   cys <- list("start" = 310, "end" = 312)
   insertion <- unlist(Biostrings::insertion(alignment))
@@ -111,12 +70,6 @@ get_cys_coordinates <- function(alignment) {
   return(list("start" = cys_start, "end" = cys_end))
 }
 
-#' Delimit the hypervariable region (HVR) for each TCR sequence
-#'
-#' @param sequences A \code{QualityScaledDNAStringSet} with the TCR sequences
-#' @param vdj_segments A \code{DNAStringSet} containing the VJ sequences
-#' @param cores Number of cores to apply multiprocessing
-#' @return A \code{QualityScaledDNAStringSet} containing the HVR
 get_hvr_sequences <- function(sequences, vdj_segments, cores = detectCores()) {
   df <- fetch_vj_sequences(sequences, vdj_segments)
   v_alignment <- parallel::mcmapply(sequences,

src/repertoire.r

@@ -1,10 +1,6 @@
 library(immuneSIM)
 library(Biostrings)
 
-#' Generate the beta chain of a human T-cell receptor (TCR)
-#'
-#' @param number_of_sequences Number of different sequences to generate
-#' @return A \code{data.frame} with the sequences, V and J genes and CDR3
 generate_repertoire <- function(number_of_sequences) {
   return(immuneSIM(
     number_of_seqs = number_of_sequences,
@@ -14,9 +10,6 @@ generate_repertoire <- function(number_of_sequences) {
   ))
 }
 
-#' Saves the sequences and CDR3 to FASTQ files
-#'
-#' @param data A \code{data.frame} with the preprocessed TCR sequences and CDR3
 save_data <- function(data) {
   Biostrings::writeXStringSet(data$sequence,
     "data/sequence.fastq",
@@ -25,11 +18,6 @@ save_data <- function(data) {
   Biostrings::writeXStringSet(data$junction, "data/HVR.fastq", format = "fastq")
 }
 
-#' Applies the reverse complement and amplifies the number of sequences
-#'
-#' @param data A \code{data.frame} containing the TCR sequences and CDR3
-#' @param reads Number of times to amplify each sequence
-#' @return A \code{data.frame} with reverse complement sequences and VJ metadata
 process_data <- function(data, reads) {
   dna_sequence <- Biostrings::DNAStringSet(data$sequence)
   data$sequence <- Biostrings::reverseComplement(dna_sequence)
@@ -40,9 +28,6 @@ process_data <- function(data, reads) {
   return(amplified_data)
 }
 
-#' Checks the number of command line arguments and captures them
-#'
-#' @return A \code{vector} containing the command line arguments
 parse_cli_arguments <- function() {
   args <- commandArgs(trailingOnly = TRUE)
   if (length(args) != 2) {