R/load_input_files.R

In caravagn/TIN: TINC - Tumour-in-Normal contamination

#' Load data for TIN.
#'
#' @description
#'
#' The function can support both a file argument and a dataframe.
#' In the case a file is given, no column or row names should be
#' used. If the input is a dataframe or tibble, column names are
#' required. See the package for a detailed description of the
#' input format for TIN analysis.
#'
#' This function, after loading data, annotates which mutation
#' has VAF  within a custom range (default [0; 0.7]); these are
#' the only mutations that are used for the TIN analysis.
#'
#' @param x Either a filename, or a dataframe.
#' @param verbose If `TRUE`, more output is print to screen.
#' @param VAF_range_tumour A 2D vector that determines a
#' VAF range used to decide which mutation we do analyse from the
#' input cancer sample.
#' @param N If there are more than `N` mutations in VAF range
#' `VAF_range_tumour`, a random subset of size `N` is retained.
#'
#' @return A list that contains the following informations:
#' tumour calls, normal calls and joint calls, plus the input
#' VAF range and file name.
#'
#' @export
#'
#' @examples
#' # Generating a random TIN input
#' load_input(random_TIN())
load_input = function(x,
                      verbose = FALSE,
                      VAF_range_tumour = c(0, 0.7),
                      N = 20000
                      )
{
  file = x
  pio::pioHdr("TINC ~ Load tumour/normal data")

  ######## Detect if it is filename or data.frame, and open it
  if(is.character(file) && file.exists(file))
  {
    pio::pioStr("\n    Input file ", file, '~',
                file.size(file) %>% utils:::format.object_size(units = "auto"),
                suffix = '\n')

    # VCF file dumped in our TIN format
    dataset =  read.csv(file,
                        sep = '\t',
                        header = FALSE,
                        stringsAsFactors = FALSE) %>% as_tibble

    if(ncol(dataset) > 5) {
      message("Input VCF file has more than 5 columns, only the first 5 will be used.")
      dataset = dataset[, 1:5]
    }

    colnames(dataset) = c('id',
                          'n_ref_count',
                          'n_alt_count',
                          't_ref_count',
                          't_alt_count')

  }

  if(is.data.frame(file))
  {
    pio::pioStr("\n    Input dataframe ", dim(file)[1],  'x', dim(file)[2],
                suffix = '\n')

    # required TIN format
    required_cols = c('id', 'n_ref_count',
                      'n_alt_count',
                      't_ref_count',
                      't_alt_count')

    if(!all(required_cols %in% colnames(file)))
      stop("The input dataframe must have the following named columns: ", paste(required_cols, collapse = ', '))

    dataset = file %>% as_tibble()
  }
  ######## Detected: DONE


  # Germline data
  germline = dataset %>%
    dplyr::select(id, starts_with('n')) %>%
    dplyr::mutate(DP = n_ref_count + n_alt_count,
           NV = n_alt_count,
           VAF = NV / DP) %>%
    tidyr::separate(
      id,
      into = c('chr', 'from', 'to', 'ref', 'alt'),
      sep = ':',
      remove = FALSE
    ) %>%
    dplyr::select(chr,
           from,
           to,
           ref,
           alt,
           id,
           ends_with('count'),
           DP,
           NV,
           VAF)

  pio::pioTit("Mutations annotated in the normal sample")
  pio::pioDisp(germline)

  #  Tumour data
  tumour = dataset %>%
    dplyr::select(id, starts_with('t')) %>%
    dplyr::mutate(DP = t_ref_count + t_alt_count,
           NV = t_alt_count,
           VAF = NV / DP) %>%
    tidyr::separate(
      id,
      into = c('chr', 'from', 'to', 'ref', 'alt'),
      sep = ':',
      remove = FALSE
    ) %>%
    dplyr::select(chr,
           from,
           to,
           ref,
           alt,
           id,
           ends_with('count'),
           DP,
           NV,
           VAF)

  pio::pioTit("Mutations annotated in the tumour sample")
  pio::pioDisp(tumour)

  pio::pioStr("\n VAF summaries ", suffix = '\n\n')
  bind_rows(
    c(`sample` = "Normal", summary(germline$VAF) %>% round(2)),
    c(`sample` = "Tumour", summary(tumour$VAF) %>% round(2))
  ) %>%
    print

  # Joint data
  joint_data = germline %>%
    dplyr::full_join(
      tumour,
      by = c('chr', 'from', 'to', 'ref', 'alt', 'id'),
      suffix = c('.normal', '.tumour')
    )

  # Tumour filter
  tumour = tumour %>%
    dplyr::mutate(
      used = (VAF > VAF_range_tumour[1]) & (VAF < VAF_range_tumour[2])
      )

  # Downsample
  if(sum(tumour$used) > N)
  {
    message("\nMore than n = ", N, ' tumour mutations in the required VAF range, downsampling the data.\n')

    w_t =  tumour %>% dplyr::filter(used) %>% dplyr::sample_n(N) %>% dplyr::pull(id)
    tumour$used[!(tumour$id %in% w_t)] = FALSE
  }

  t_ids = tumour %>% dplyr::filter(used) %>% dplyr::pull(id)

  germline$used = FALSE
  germline$used[germline$id %in% t_ids] = TRUE

  joint_data$used = FALSE
  joint_data$used[joint_data$id %in% t_ids] = TRUE

  # Print
  TB = table(tumour$used)
  pio::pioStr("\n     VAF range",
              VAF_range_tumour[1], '~',
              VAF_range_tumour[2],
              ' [',
              paste(names(TB), TB),
              '] tumour mutations',
              suffix = '\n')


  return(list(
    tumour = tumour,
    normal = germline,
    joint = joint_data,
    VAF_range_tumour = VAF_range_tumour,
    file = file
  ))
}