comparison waveica_wrapper.R @ 2:6480c6d5fa36 draft

planemo upload for repository https://github.com/RECETOX/galaxytools/tree/master/tools/waveica commit 77bea6c84c5a932fa203723d4d5fea6147f06505

1:b77023c41c76

read_file <- function(file, metadata, ft_ext, mt_ext) {
  data <- read_data(file, ft_ext)

  if (!is.na(metadata)) {
    mt_data <- read_data(metadata, mt_ext)
    data <- merge(mt_data, data, by = "sampleName")
  }

}

waveica <- function(file,
                    metadata = NA,
                    ext,
                    wavelet_filter,
                    wavelet_length,
                    k,
                    t,
                    t2,
                    alpha,
                    exclude_blanks) {

  # get input from the Galaxy, preprocess data
  ext <- strsplit(x = ext, split = "\\,")[[1]]

  ft_ext <- ext[1]
  mt_ext <- ext[2]

  data <- read_file(file, metadata, ft_ext, mt_ext)

  required_columns <- c("sampleName", "class", "sampleType", "injectionOrder", "batch")
  verify_input_dataframe(data, required_columns)

  data <- sort_by_injection_order(data)

  # separate data into features, batch and group
  feature_columns <- colnames(data)[!colnames(data) %in% required_columns]

}

waveica_singlebatch <- function(file,
                                metadata = NA,
                                ext,
                                wavelet_filter,
                                wavelet_length,
                                k,
                                alpha,
                                cutoff,
                                exclude_blanks) {

  # get input from the Galaxy, preprocess data
  ext <- strsplit(x = ext, split = "\\,")[[1]]

  ft_ext <- ext[1]
  mt_ext <- ext[2]

  data <- read_file(file, metadata, ft_ext, mt_ext)

  required_columns <- c("sampleName", "class", "sampleType", "injectionOrder")
  optional_columns <- c("batch")
  verify_input_dataframe(data, required_columns)

  data <- sort_by_injection_order(data)

  feature_columns <- colnames(data)[!colnames(data) %in% c(required_columns, optional_columns)]
  features <- data[, feature_columns]
  injection_order <- data$injectionOrder

  # run WaveICA
  features <- recetox.waveica::waveica_nonbatchwise(

    alpha = alpha,
    cutoff = cutoff
  )

  data[, feature_columns] <- features

  # remove blanks from dataset
  if (exclude_blanks) {
    data <- exclude_group(data, group)
  }

verify_input_dataframe <- function(data, required_columns) {
  if (anyNA(data)) {
    stop("Error: dataframe cannot contain NULL values!
Make sure that your dataframe does not contain empty cells")
  } else if (!all(required_columns %in% colnames(data))) {
    stop("Error: missing metadata!
Make sure that the following columns are present in your dataframe: ", paste(required_columns, collapse = ", "))
  }
}


# Match group labels with [blank/sample/qc] and enumerate them
enumerate_groups <- function(group) {

store_data <- function(data, output, ext) {
  if (ext == "csv") {
    write.csv(data, file = output, row.names = FALSE, quote = FALSE)
  } else if (ext == "tsv") {
    write.table(data, file = output, sep = "\t", row.names = FALSE, quote = FALSE)
  } else {
    arrow::write_parquet(data, sink = output)
  }
  cat("Normalization has been completed.\n")
}

2:6480c6d5fa36

read_file <- function(file, metadata, ft_ext, mt_ext, transpose) {
  data <- read_data(file, ft_ext)

  if (transpose) {
    col_names <- c("sampleName", data[[1]])
    t_data <- data[-1]
    t_data <- t(t_data)
    data <- data.frame(rownames(t_data), t_data)
    colnames(data) <- col_names
  }

  if (!is.na(metadata)) {
    mt_data <- read_data(metadata, mt_ext)
    data <- merge(mt_data, data, by = "sampleName")
  }

}

waveica <- function(file,
                    metadata = NA,
                    ext,
                    transpose = FALSE,
                    wavelet_filter,
                    wavelet_length,
                    k,
                    t,
                    t2,
                    alpha,
                    exclude_blanks) {
  # get input from the Galaxy, preprocess data
  ext <- strsplit(x = ext, split = "\\,")[[1]]

  ft_ext <- ext[1]
  mt_ext <- ext[2]

  data <- read_file(file, metadata, ft_ext, mt_ext, transpose)

  required_columns <- c(
    "sampleName", "class", "sampleType",
    "injectionOrder", "batch"
  )
  data <- verify_input_dataframe(data, required_columns)

  data <- sort_by_injection_order(data)

  # separate data into features, batch and group
  feature_columns <- colnames(data)[!colnames(data) %in% required_columns]

}

waveica_singlebatch <- function(file,
                                metadata = NA,
                                ext,
                                transpose = FALSE,
                                wavelet_filter,
                                wavelet_length,
                                k,
                                alpha,
                                cutoff,
                                exclude_blanks) {
  # get input from the Galaxy, preprocess data
  ext <- strsplit(x = ext, split = "\\,")[[1]]

  ft_ext <- ext[1]
  mt_ext <- ext[2]

  data <- read_file(file, metadata, ft_ext, mt_ext, transpose)

  required_columns <- c("sampleName", "class", "sampleType", "injectionOrder")
  optional_columns <- c("batch")

  data <- verify_input_dataframe(data, required_columns)

  data <- sort_by_injection_order(data)

  feature_columns <- colnames(data)[!colnames(data) %in%
    c(required_columns, optional_columns)]
  features <- data[, feature_columns]
  injection_order <- data$injectionOrder

  # run WaveICA
  features <- recetox.waveica::waveica_nonbatchwise(

    alpha = alpha,
    cutoff = cutoff
  )

  data[, feature_columns] <- features
  group <- enumerate_groups(as.character(data$sampleType))
  # remove blanks from dataset
  if (exclude_blanks) {
    data <- exclude_group(data, group)
  }

verify_input_dataframe <- function(data, required_columns) {
  if (anyNA(data)) {
    stop("Error: dataframe cannot contain NULL values!
Make sure that your dataframe does not contain empty cells")
  } else if (!all(required_columns %in% colnames(data))) {
    stop(
      "Error: missing metadata!
Make sure that the following columns are present in your dataframe: ",
      paste(required_columns, collapse = ", ")
    )
  }

  data <- verify_column_types(data, required_columns)

  return(data)
}

verify_column_types <- function(data, required_columns) {
  # Specify the column names and their expected types
  column_types <- list(
    "sampleName" = c("character", "factor"),
    "class" = c("character", "factor"),
    "sampleType" = c("character", "factor"),
    "injectionOrder" = "integer",
    "batch" = "integer"
  )

  column_types <- column_types[required_columns]

  for (col_name in names(data)) {
    actual_type <- class(data[[col_name]])
    if (col_name %in% names(column_types)) {
      expected_types <- column_types[[col_name]]

      if (!actual_type %in% expected_types) {
        stop(
          "Column ", col_name, " is of type ", actual_type,
          " but expected type is ",
          paste(expected_types, collapse = " or "), "\n"
        )
      }
    } else {
      if (actual_type != "numeric") {
        data[[col_name]] <- as.numeric(as.character(data[[col_name]]))
      }
    }
  }
  return(data)
}


# Match group labels with [blank/sample/qc] and enumerate them
enumerate_groups <- function(group) {

store_data <- function(data, output, ext) {
  if (ext == "csv") {
    write.csv(data, file = output, row.names = FALSE, quote = FALSE)
  } else if (ext == "tsv") {
    write.table(data,
      file = output, sep = "\t",
      row.names = FALSE, quote = FALSE
    )
  } else {
    arrow::write_parquet(data, sink = output)
  }
  cat("Normalization has been completed.\n")
}