recalculate_frequency_old.R

# Recalculates relative frequency given a pre-filtered file (i.e. allreads_filtered.csv).
# This step is done in the tprk_pipeline.py. recalculate_frequency.R will replace the file. 

suppressMessages(suppressWarnings(require("optparse")))
suppressMessages(suppressWarnings(require(vegan)))

option_list <- list(make_option(c("-d", "--directory"), type="character", default=NULL, help="Specify working directory", metavar="character"),
                    make_option(c("-f", "--filename"), type="character", default=NULL, help="Specify file to recalculate frequency.", 
                                metavar="character"),
                    make_option(c("-i", "--illumina_check"), type="character", default=FALSE, help="Specify if these files are only PacBio.", 
                                metavar="character", action="store_true"));
opt_parser <- OptionParser(option_list=option_list);
opt <- parse_args(opt_parser)

path <- opt$directory
filename <- (opt$filename)

#####

## To run this script manually in R, uncomment the following lines. You do not need to change the preceding line of path
## but remember to recomment the lines if you want to run the script automatically in the pipeline.
## path refers to the folder your metadata.csv and sequencing files (.fastq) are.
## filename refers to the pre-filtered file (i.e. allreads_filtered.csv) that you want to recalculate frequencies for.

#path <- "/Users/uwvirongs/Documents/Michelle/tprk_pipeline/paper2_redo/pacbio_v_illumina/"
#filename <- "/Users/uwvirongs/Documents/Michelle/tprk_pipeline/paper2_redo/pacbio_v_illumina/allreads_filtered.csv"

## This script can also be run from the command line.
## Usage: rscript \path\to\recalculate_frequency.R -d [path] -f [file_name]

#####
allreads_filtered <- read.table(filename, sep=',', header=TRUE)
# Grabs the actual number of samples.
numsamples <- (length(colnames(allreads_filtered)) - 2) / 4
metadata <- read.table(paste(path,"/metadata.csv", sep=''), sep=',', header=TRUE)
sample_names <- c(as.character(metadata$SampleName))

# TODO: Implement Illumina check.
# if(opt$illumina_check) {
#   for (i in c(1:length(sample_names))) {
#     IlluminaCounttitle <- paste("Ill_",sample_names[i],"_Count",sep='')
#     illumina_filtered <- allreads_filtered[allreads_filtered[[IlluminaCounttitle]] < 5 ,]
#     illumina_filtered <- which(allreads_filtered[IlluminaCounttitle] < 5, arr.ind)
#     allreads_filtered1[illumina_filter]
#     for (j in allreads_filtered[IlluminaCounttitle]) {
#       if(j>=5) {
#         print(j)
#       }
#   }
# }

allreads_filtered1 <- allreads_filtered

# Allreads_filtered.csv has both PacBio and Illumina data, so must run through twice.
if (grepl("allreads_filtered.csv",filename)) {
  numsamples = numsamples * 2
}

# Loops through samples and recalculates frequency for each sample (column) for each region.
for (sample in c(1:(numsamples))){
  freqcol <- (sample * 2) + 1
  countcol <- (sample * 2) + 2
  for (region in unique(allreads_filtered1$Region)){
    allreads_filtered1[which(allreads_filtered1[,1]==region),freqcol] <- 
      allreads_filtered1[which(allreads_filtered1[,1]==region),countcol] / sum(allreads_filtered1[which(allreads_filtered1[,1]==region),countcol],na.rm=TRUE) * 100
  }
  allreads_filtered1[,freqcol] <- trimws(format(allreads_filtered1[,freqcol], digits = 4, nsmall = 4))
}

#out_filename <- substr(filename,1,nchar(filename)-4)
#allreads_out <- paste(path,"/",out_filename,".csv",sep='')

# Rewrites the file with the newly calculated relative frequencies.
write.csv(allreads_filtered1,file=filename,row.names=FALSE,quote=FALSE)

# Also makes Amin's requested CSV
if (grepl("allreads_filtered.csv",filename)) {
  amin_csv <- t(allreads_filtered1)
  variable_region_lists <- split(allreads_filtered1, f = allreads_filtered1$Region)
  for (i in 1:length(variable_region_lists)) {
    amin_csv <- t(variable_region_lists[[i]])
    colnames(amin_csv) <- NULL
    to_remove <- "Region"
    for (row in 1:nrow(amin_csv)) {
      row_name <- rownames(amin_csv)[row]
      if (grepl("_RelativeFreq", row_name)) {
        to_remove <- c(to_remove, row_name)
      }
    }
    removedamin_csv = amin_csv[ !(row.names(amin_csv) %in% to_remove), ]
    path <- gsub("[\r\n]", "", path)
    dir.create(file.path(path, "Diversity"), showWarnings=FALSE)
    write.table(removedamin_csv,file=paste(path,"/Diversity/V",i,".csv",sep=""), sep=",",quote=FALSE, col.names=FALSE)
  }
}