R/sparkParse.R

In ajfedorec/prNorm: Perform normalisation of plate reader data

#' Title
#'
#' @param data_csv path to csv file from Tecan Spark plate reader
#' @param layout_csv path to csv file containing plate layout information
#'
#' @return a data.frame containing the parsed plate reader data
#' @export
#'
sparkParse <- function(data_csv, layout_csv) {
  data <- utils::read.table(data_csv, sep = ",", blank.lines.skip = T, header = F,
                     stringsAsFactors = F)

  plate_layout <- utils::read.csv(layout_csv)

  start_time_idx <- which(data[,1] == "Start Time")
  if (length(start_time_idx) > 1) {
    start_time_idx <- start_time_idx[length(start_time_idx)]
  }

  next_block_start_idx <- start_time_idx + 2

  end_of_file <- F
  all_data <- c()
  while (!end_of_file) {
    # find what is being measured
    block_name <- data[next_block_start_idx, 1]

    # check if we are at the end of the file
    if (block_name == "End Time") {
      end_of_file <- T
      break
    }

    # find where the end of the current measurement block is
    block_end_idx <- next_block_start_idx
    while (data[block_end_idx, 1] != "") {
      block_end_idx <- block_end_idx + 1
    }

    # grab the data only for that measurement
    new_block <- data[(next_block_start_idx + 1):(block_end_idx - 1), ]

    # trim unecessary readings i.e. temp and cycle number
    # and rename columns
    times <- new_block[2,]
    new_block <- new_block[c(-1,-2,-3),]
    names(new_block) <- times
    names(new_block)[1] <- "well"

    # add info for each well
    joined_block <- dplyr::full_join(plate_layout, new_block)
    joined_block$measure <- block_name

    #
    all_data <- rbind(all_data, joined_block)

    #
    next_block_start_idx <- block_end_idx + 1
  }

  # rearrange data
  well_idx <- which(names(all_data) == "well")
  gathered_data <- tidyr::gather(all_data, key = "time", value = p, -c(1:well_idx, ncol(all_data)))
  gathered_data$time <- as.numeric(gathered_data$time)
  gathered_data$p <- as.numeric(gathered_data$p)
  spread_data <- tidyr::spread(gathered_data, key = measure, value = p)

  spread_data$row <- substr(x = spread_data$well, start = 1, stop = 1)
  spread_data$column <- as.numeric(substr(x = spread_data$well, start = 2, stop = nchar(spread_data$well)))
  spread_data <- dplyr::arrange_at(spread_data, dplyr::vars(time, row, column))

  return(spread_data)
}