R/analyse_splitreads.R

In jessievb/RAID: Split barcoded FASTQ files and analyse data from the iSeq method

#' @export
raid_analysis_splitreads <- function(experiment_dir, data) {
  ## First set working directory to experiment folder
  setwd(experiment_dir)

  ## Location of the log files
  futile.logger::flog.appender(
      futile.logger::appender.file("output/exp_log/experimental_info.log"),
      name = "1")
  futile.logger::flog.layout(futile.logger::layout.tracearg, name = "1")

  futile.logger::flog.appender(
      futile.logger::appender.file("output/exp_log/Run_info.log"), name = "2")
  futile.logger::flog.layout(futile.logger::layout.tracearg, name = "2")

  ## Log info
  futile.logger::flog.info(
      "Start experiment %s analysis", data, name = "1")
  futile.logger::flog.info(
      "Import reads from experiment: %s", data, name = "2")

  ## Import the total split
  raid_split_reads <- raid_import_split_data(data)

  ## Log info
  split_reads_total <- nrow(raid_split_reads)
  futile.logger::flog.info("Total split reads in %s: %d",
                           data, split_reads_total, name = "1")

  ## Count row occurence and save tbl
  futile.logger::flog.info("Counting row occurence ... ", name = "2")
  UMI_count <- raid_umi_count(split_data_file_path = data, raid_barcode_tbl = raid_split_reads)

  ## Log info
  unique_reads_total <- nrow(UMI_count)
  futile.logger::flog.info("Total unique reads in %s: %d",
                           data, unique_reads_total, name = "1")

  ## Create UMI frequency table
  futile.logger::flog.info("Create UMI frequency tbl", name = "2")
  UMI_count_frequency <- raid_umi_count_frequency(split_data_file_path = data, UMI_count_input = UMI_count)





  ## Count UMI per barcode1
  futile.logger::flog.info(
      strwrap(
          "Counting total unique UMI strings per barcode1"),
      name = "2")

  barcode_count <- raid_barcode_count(split_data_file_path = data, UMI_count_tbl = UMI_count)

  barcode_count_matched <- raid_barcode_match(data, barcode_count_tbl = barcode_count)



  #futile.logger::flog.info("Create plots", name = "2")
  ## Create plot of barcode counts (which shows the distribution of
  ## counts)
  #ggplot2::ggplot(data = barcode_count, aes(antibody_count)) +
  #    ggplot2::geom_histogram(binwidth = 10)  # +
  # xlim(0, 800) + ylim(0,10000)

  #ggplot2::ggsave(
  #    "output/figures/barcode_count_histogram.eps", width = 5, height = 5)
 
  ## create plot of the UMI distribution
 #ggplot2::ggplot(data = UMI_count_frequency,
  #                aes(x = row_occurence_count, y = count_frequency)) +
  #    ggplot2::geom_point() +
  #    ggplot2::xlab("UMI occurence") + ggplot2::ylab("frequency") +
  #    ggplot2::scale_y_log10() + ggplot2::theme_bw()
  #ggplot2::ggsave("output/figures/UMI_frequency.eps", width = 5, height = 5)
  
  futile.logger::flog.info("Finished analysis", name = "1")
  futile.logger::flog.info("Finished analysis", name = "2")

 barcode_count_matched
#barcode_count
}