more documentation, replce =, <-

bin/1.2_filter_motifs/compareBed_runinfo.R

@@ -18,15 +18,15 @@
 
 # parsing parameters
 library(data.table)
-args = commandArgs(TRUE)
-min = as.numeric(args[1])
-max = as.numeric(args[2])
-input_raw = args[3]
-input_filtered = args[4]
-output = args[5]
-output_stats = args[6]
+args <- commandArgs(TRUE)
+min <- as.numeric(args[1])
+max <- as.numeric(args[2])
+input_raw <- args[3]
+input_filtered <- args[4]
+output <- args[5]
+output_stats <- args[6]
 
-data_filtered = fread(input_filtered, sep='\t')
+data_filtered <- fread(input_filtered, sep='\t')
 
 # check if data has less than 9 columns
 if (ncol(data_filtered) < 9) {
@@ -35,39 +35,45 @@ if (ncol(data_filtered) < 9) {
 
 # remove sequences that are smaller than minimum (parameter)
 # remove sequences that are longer than maximum (parameter)
-data_filtered = data_filtered[which(data_filtered[[3]] - data_filtered[[2]] >= min),]
-data_filtered = data_filtered[which(data_filtered[[3]] - data_filtered[[2]] <= max),]
+data_filtered <- data_filtered[which(data_filtered[[3]] - data_filtered[[2]] >= min),]
+data_filtered <- data_filtered[which(data_filtered[[3]] - data_filtered[[2]] <= max),]
 
 # make unique names and adjust length for splitted footprints
 # duplicated names have .0 , .1 , .2 ... added
-names=data_filtered[[4]]
-names(data_filtered)[4] = "name"
+names <- data_filtered[[4]]
+names(data_filtered)[4] <- "name"
 # all duplicated names
-duplicants = unique(data_filtered[duplicated(name)][[4]])
-data_filtered[[4]] = make.unique(as.character(data_filtered[[4]]))
+duplicants <- unique(data_filtered[duplicated(name)][[4]])
+data_filtered[[4]] <- make.unique(as.character(data_filtered[[4]]))
 data_filtered[match(duplicants, names), name := paste0(name,".0")]
 
 # recalculate length of sequences
-data_filtered[[7]] = data_filtered[[3]] - data_filtered[[2]]
+data_filtered[[7]] <- data_filtered[[3]] - data_filtered[[2]]
 
 # adding column "contains_maxpos", containing flag (0 or 1)
 # max_pos is the position of maximum score of a footprint
-data_filtered = cbind(data_filtered, contains_maxpos = 0)
+data_filtered <- cbind(data_filtered, contains_maxpos = 0)
 data_filtered$contains_maxpos[intersect(which(data_filtered[[2]] <= data_filtered[[8]]), which(data_filtered[[3]] > data_filtered[[8]]))] = 1
-data_filtered[[8]] = data_filtered[[8]] - data_filtered[[2]]
+data_filtered[[8]] <- data_filtered[[8]] - data_filtered[[2]]
 
 fwrite(data_filtered, output, col.names=FALSE, quote = FALSE, sep = '\t')
 
 # data is the initial data before any comparisons have been done (-d parameter of compareBed.sh)
-data = fread(input_raw, sep='\t')
+data <- fread(input_raw, sep='\t')
 
 # some statistics about the bedtool comparisons are stored in FilterMotifs.stats
-sum_data = sum(data[[3]]-data[[2]])
-sum_filtered = sum(data_filtered[[7]])
-difference_nt = formatC(sum_data/sum_filtered, digits = 4)
-loss_nt = formatC(1 - sum_filtered/sum_data, digits = 2)
-length_data = formatC(mean(data[[3]]-data[[2]]), digits = 4)
-length_filtered = formatC(mean(data_filtered[[7]]), digits = 4)
-stats = data.frame(sum_nt_input = sum_data, sum_nt_filtered = sum_filtered, quotient_of_nt = difference_nt, loss_of_nt = loss_nt, mean_length_input = length_data, mean_length_filtered = length_filtered, flag_1_ratio = length(which(data_filtered$containsMaxpos == 1))/dim(data_filtered)[1])
-stats = t(stats)
+# number of nucleotides input
+sum_data <- sum(data[[3]]-data[[2]])
+# number of nucleotides after filter
+sum_filtered <- sum(data_filtered[[7]])
+# quotient: sum_data/sum_filtered
+difference_nt <- formatC(sum_data/sum_filtered, digits = 4)
+# loss: 1 - sum_filtered/sum_data
+loss_nt <- formatC(1 - sum_filtered/sum_data, digits = 2)
+# mean length of footprints input
+length_data <- formatC(mean(data[[3]]-data[[2]]), digits = 4)
+# mean -ength of footprints after filter
+length_filtered <- formatC(mean(data_filtered[[7]]), digits = 4)
+stats <- data.frame(sum_nt_input = sum_data, sum_nt_filtered = sum_filtered, quotient_of_nt = difference_nt, loss_of_nt = loss_nt, mean_length_input = length_data, mean_length_filtered = length_filtered, flag_1_ratio = length(which(data_filtered$containsMaxpos == 1))/dim(data_filtered)[1])
+stats <- t(stats)
 write.table(stats, output_stats, col.names = FALSE, quote = FALSE, sep = '\t')