spell check

bin/reduce_sequence.R

@@ -3,15 +3,15 @@ library("optparse")
 
 option_list <- list(
   make_option(opt_str = c("-i", "--input"), default = NULL, help = "Input bed-file. Last column must be sequences.", metavar = "character"),
-  make_option(opt_str = c("-k", "--kmer"), default = 10, help = "Kmer length. Default = %default", metavar = "integer"),
+  make_option(opt_str = c("-k", "--kmer"), default = 10, help = "k-mer length. Default = %default", metavar = "integer"),
   make_option(opt_str = c("-m", "--motif"), default = 10, help = "Estimated motif length. Default = %default", metavar = "integer"),
   make_option(opt_str = c("-o", "--output"), default = "reduced.bed", help = "Output file. Default = %default", metavar = "character"),
   make_option(opt_str = c("-t", "--threads"), default = 1, help = "Number of threads to use. Use 0 for all available cores. Default = %default", metavar = "integer"),
   make_option(opt_str = c("-c", "--clean"), default = TRUE, help = "Delete all temporary files. Default = %default", metavar = "logical"),
-  make_option(opt_str = c("-s", "--min_seq_length"), default = NULL, help = "Remove sequences below this length. Defaults to the maximum value of motif and kmer and can not be lower.", metavar = "integer", type = "integer"),
-  make_option(opt_str = c("-n", "--minoverlap_kmer"), default = NULL, help = "Minimum required overlap between kmer. Used to create reduced sequence ranges out of merged kmer. Can not be greater than kmer length. Default = kmer - 1", metavar = "integer", type = "integer"),
-  make_option(opt_str = c("-v", "--minoverlap_motif"), default = NULL, help = "Minimum required overlap between motif and kmer to consider kmer significant. Used for kmer cutoff calculation. Can not be greater than motif and kmer length. Default = ceiling(motif / 2)", metavar = "integer", type = "integer"),
-  make_option(opt_str = c("-f", "--motif_occurence"), default = 1, help = "Define how many motifs are expected per sequence. This value is used during kmer cutoff calculation. Default = %default meaning that there should be approximately one motif per sequence.", metavar = "double")
+  make_option(opt_str = c("-s", "--min_seq_length"), default = NULL, help = "Remove sequences below this length. Defaults to the maximum value of motif and k-mer and can not be lower.", metavar = "integer", type = "integer"),
+  make_option(opt_str = c("-n", "--minoverlap_kmer"), default = NULL, help = "Minimum required overlap between k-mer. Used to create reduced sequence ranges out of merged k-mer. Can not be greater than k-mer length. Default = kmer - 1", metavar = "integer", type = "integer"),
+  make_option(opt_str = c("-v", "--minoverlap_motif"), default = NULL, help = "Minimum required overlap between motif and k-mer to consider k-mer significant. Used for k-mer cutoff calculation. Can not be greater than motif and k-mer length. Default = ceiling(motif / 2)", metavar = "integer", type = "integer"),
+  make_option(opt_str = c("-f", "--motif_occurrence"), default = 1, help = "Define how many motifs are expected per sequence. This value is used during k-mer cutoff calculation. Default = %default meaning that there should be approximately one motif per sequence.", metavar = "double")
 )
 
 opt_parser <- OptionParser(option_list = option_list, 
@@ -23,19 +23,19 @@ opt <- parse_args(opt_parser)
 #' Reduces each sequence to its most frequent region.
 #' 
 #' @param input Input bed-file. Last column must be sequences.
-#' @param kmer Kmer length. Default = 10
+#' @param kmer k-mer length. Default = 10
 #' @param motif Estimated motif length. Default = 10
 #' @param output Output file. Default = reduced.bed
 #' @param threads Number of threads to use. Default = 1. Use 0 for all cores.
 #' @param clean Delete all temporary files.
-#' @param minoverlap_kmer Minimum required overlap between kmer. Used to create reduced sequence ranges out of merged kmer. Can not be greater than kmer length . Default = kmer - 1
-#' @param minoverlap_motif Minimum required overlap between motif and kmer to consider kmer significant. Used for kmer cutoff calculation. Can not be greater than motif and kmer length. Default = ceiling(motif / 2)
-#' @param min_seq_length Remove sequences below this length. Defaults to the maximum value of motif and kmer and can not be lower.
-#' @param motif_occurence Define how many motifs are expected per sequence. This value is used during kmer cutoff calculation. Default = 1 meaning that there should be approximately one motif per sequence.
+#' @param minoverlap_kmer Minimum required overlap between k-mer. Used to create reduced sequence ranges out of merged k-mer. Can not be greater than k-mer length . Default = kmer - 1
+#' @param minoverlap_motif Minimum required overlap between motif and k-mer to consider k-mer significant. Used for k-mer cutoff calculation. Can not be greater than motif and k-mer length. Default = ceiling(motif / 2)
+#' @param min_seq_length Remove sequences below this length. Defaults to the maximum value of motif and k-mer and can not be lower.
+#' @param motif_occurrence Define how many motifs are expected per sequence. This value is used during k-mer cutoff calculation. Default = 1 meaning that there should be approximately one motif per sequence.
 #' 
-#' @details If there is a header supplied other then the default data.table nameing scheme ('V1', 'V2', etc.) it will be kept.
+#' @details If there is a header supplied other then the default data.table naming scheme ('V1', 'V2', etc.) it will be kept.
 #' 
-reduce_sequence <- function(input, kmer = 10, motif = 10, output = "reduced.bed", threads = NULL, clean = TRUE, minoverlap_kmer = kmer - 1, minoverlap_motif = ceiling(motif / 2), min_seq_length = max(c(motif, kmer)), motif_occurence = 1) {
+reduce_sequence <- function(input, kmer = 10, motif = 10, output = "reduced.bed", threads = NULL, clean = TRUE, minoverlap_kmer = kmer - 1, minoverlap_motif = ceiling(motif / 2), min_seq_length = max(c(motif, kmer)), motif_occurrence = 1) {
   if (system("which jellyfish", ignore.stdout = TRUE) != 0) {
     stop("Required program jellyfish not found! Please check whether it is installed.")
   }
@@ -76,7 +76,7 @@ reduce_sequence <- function(input, kmer = 10, motif = 10, output = "reduced.bed"
 
   # remove sequences below minimum length
   if (min_seq_length < max(c(kmer, motif))) {
-    stop("Minimum sequence length must be greater or equal to ", max(c(motif, kmer)), " (maximum value of kmer and motif).")
+    stop("Minimum sequence length must be greater or equal to ", max(c(motif, kmer)), " (maximum value of k-mer and motif).")
   }
 
   total_rows <- nrow(bed_table)
@@ -91,7 +91,7 @@ reduce_sequence <- function(input, kmer = 10, motif = 10, output = "reduced.bed"
   fasta_file <- paste0(basename(input), ".fasta")
   seqinr::write.fasta(sequences = as.list(bed_table[[ncol(bed_table)]]), names = bed_table[[4]], as.string = TRUE, file.out = fasta_file)
 
-  message("Counting kmer...")
+  message("Counting k-mer...")
   # count k-mer
   hashsize <- 4 ^ kmer
   count_output_binary <- "mer_count_binary.jf"
@@ -105,20 +105,20 @@ reduce_sequence <- function(input, kmer = 10, motif = 10, output = "reduced.bed"
 
   system(command = jellyfish_dump_call, wait = TRUE)
 
-  message("Reduce kmer.")
+  message("Reduce k-mer.")
   # load mer table
   # columns: kmer, count
   kmer_counts <- data.table::fread(input = mer_count_table, header = FALSE)
-  # order kmer descending
+  # order k-mer descending
   data.table::setorder(kmer_counts, -V2)
 
   # compute number of hits to keep
-  keep_hits <- significant_kmer(bed_table, kmer = kmer, motif = motif, minoverlap = minoverlap_motif, motif_occurence = motif_occurence)
+  keep_hits <- significant_kmer(bed_table, kmer = kmer, motif = motif, minoverlap = minoverlap_motif, motif_occurrence = motif_occurrence)
 
-  # reduce kmer
+  # reduce k-mer
   reduced_kmer <- reduce_kmer(kmer = kmer_counts, significant = keep_hits)
 
-  message("Find kmer in sequences.")
+  message("Find k-mer in sequences.")
   # find k-mer in sequences
   # columns: name, start, end, width
   ranges_table <- find_kmer_regions(bed = bed_table, kmer_counts = reduced_kmer, minoverlap = minoverlap_kmer, threads = threads)
@@ -155,21 +155,21 @@ reduce_sequence <- function(input, kmer = 10, motif = 10, output = "reduced.bed"
   data.table::fwrite(merged, file = output, sep = "\t", col.names = keep_col_names)
 }
 
-#' Predict how many interesting kmer are possible for the given data.
+#' Predict how many interesting k-mer are possible for the given data.
 #' 
 #' @param bed Bed table with sequences in last column
-#' @param kmer Length of kmer
+#' @param kmer Length of k-mer
 #' @param motif Length of motif
-#' @param minoverlap Minimum number of bases overlapping between kmer and motif. Must be <= motif & <= kmer. Defaults to ceiling(motif / 2).
-#' @param motif_occurence Define how many motifs are expected per sequence. Default = 1
+#' @param minoverlap Minimum number of bases overlapping between k-mer and motif. Must be <= motif & <= kmer. Defaults to ceiling(motif / 2).
+#' @param motif_occurrence Define how many motifs are expected per sequence. Default = 1
 #' 
-#' @return Number of interesting kmer.
-significant_kmer <- function(bed, kmer, motif, minoverlap = ceiling(motif / 2), motif_occurence = 1) {
+#' @return Number of interesting k-mer.
+significant_kmer <- function(bed, kmer, motif, minoverlap = ceiling(motif / 2), motif_occurrence = 1) {
   if (minoverlap > kmer || minoverlap > motif) {
     stop("Kmer & motif must be greater or equal to minoverlap!")
   }
-  if (motif_occurence <= 0) {
-    stop("Motif_occurence must be a numeric value above 0!")
+  if (motif_occurrence <= 0) {
+    stop("Motif_occurrence must be a numeric value above 0!")
   }
 
   # minimum sequence length to get all interesting overlaps
@@ -180,15 +180,15 @@ significant_kmer <- function(bed, kmer, motif, minoverlap = ceiling(motif / 2),
   # reduce to max interesting length
   seq_lengths <- ifelse(seq_lengths > min_seq_length, min_seq_length, seq_lengths)
 
-  # calculate max possible kmer
+  # calculate max possible k-mer
   topx <- sum(seq_lengths - kmer + 1)
 
-  return(topx * motif_occurence)
+  return(topx * motif_occurrence)
 }
 
-#' Orders kmer table after count descending and keeps all kmer with a cumulative sum below the given significance threshold.
+#' Orders k-mer table after count descending and keeps all k-mer with a cumulative sum below the given significance threshold.
 #' 
-#' @param kmer Kmer data.table columns: kmer, count
+#' @param kmer K-mer data.table columns: kmer, count
 #' @param significant Value from significant_kmer function.
 #' 
 #' @return reduced data.table
@@ -204,16 +204,16 @@ reduce_kmer <- function(kmer, significant) {
 #' create list of significant ranges (one for each bed entry)
 #' 
 #' @param bed Data.table of bed with sequence in last column
-#' @param kmer_counts Data.table of counted kmer. Column1 = kmer, column2 = count.
-#' @param minoverlap Minimum overlapping nucleotides between kmers to be merged. Positive integer. Must be smaller than kmer length.
+#' @param kmer_counts Data.table of counted k-mer. Column1 = kmer, column2 = count.
+#' @param minoverlap Minimum overlapping nucleotides between k-mers to be merged. Positive integer. Must be smaller than k-mer length.
 #' @param threads Number of threads.
 #' 
 #' @return Data.table with relative positions and width (start, end, width).
 #' 
-#' TODO Include number of motifs per sequence (aka motif_occurence). Attempt to keep best 2 regions for occurence = 2? Probably high impact on performance.
+#' TODO Include number of motifs per sequence (aka motif_occurrence). Attempt to keep best 2 regions for occurrence = 2? Probably high impact on performance.
 find_kmer_regions <- function(bed, kmer_counts, minoverlap = 1 , threads = NULL) {
   if (nchar(kmer_counts[1, 1]) <= minoverlap) {
-    stop("Minoverlap must be smaller than kmer length!")
+    stop("Minoverlap must be smaller than k-mer length!")
   }
 
   names(kmer_counts)[1:2] <- c("kmer", "count")
@@ -232,9 +232,9 @@ find_kmer_regions <- function(bed, kmer_counts, minoverlap = 1 , threads = NULL)
       return(data.table::data.table(start = NA, end = NA, width = NA, name = name))
     }
 
-    # add kmer sequences
+    # add k-mer sequences
     ranges[, sub_seq := stringr::str_sub(seq, start, end)]
-    # add kmer count
+    # add k-mer count
     ranges[, count := kmer_counts[ranges[["sub_seq"]], "count", on = "kmer"]]
 
     #### reduce ranges
@@ -253,7 +253,7 @@ find_kmer_regions <- function(bed, kmer_counts, minoverlap = 1 , threads = NULL)
       which(member == x)
     })
 
-    # calculate component score (= sum of kmer count)
+    # calculate component score (= sum of k-mer count)
     score <- vapply(node_membership, FUN.VALUE = numeric(1), function(x) {
       sum(kmer_counts[x, "count"])
     })