R/mutate_mean_intensity.R

In tidymass/massdataset: massdataset

#' Add Mean Intensity to mass_dataset Object
#'
#' This function calculates the mean intensity for each variable in the mass_dataset object
#' based on the specified samples and adds it as a new column to the variable information.
#' @param object A mass_dataset object.
#' @param according_to_samples A character vector specifying the samples to consider for the mean calculation. Default is "all".
#' @param na.rm Logical, whether to remove NA values before calculating the mean Default is TRUE.
#'
#' @return A modified mass_dataset object with added mean intensity information.
#'
#'
#' @author Xiaotao Shen <shenxt1990@outlook.com>
#' @export
#' @examples
#' data("expression_data")
#' data("sample_info")
#' data("variable_info")
#'
#' object =
#'   create_mass_dataset(
#'     expression_data = expression_data,
#'     sample_info = sample_info,
#'     variable_info = variable_info,
#'   )
#'
#' object
#'
#' ##calculate mean intensity according to all the samples
#' object2 =
#'   mutate_mean_intensity(object = object, na.rm = TRUE)
#'
#' object2
#'
#' head(extract_variable_info(object))
#' head(extract_variable_info(object2))
#'
#' ##calculate mean intensity according to only QC samples
#' object3 =
#'   mutate_mean_intensity(object = object2,
#'                 according_to_samples =
#'               get_sample_id(object)[extract_sample_info(object)$class == "QC"])
#'
#' object3
#'
#' head(extract_variable_info(object3))
#' ###remain variables with mean intensity (QC) /  mean intensity (Blank) > 3
#' qc_sample_name =
#'   get_sample_id(object)[extract_sample_info(object)$class == "QC"]
#' blank_sample_name =
#'   get_sample_id(object)[extract_sample_info(object)$class == "Blank"]
#'
#' object4 =
#' object %>%
#'   mutate_mean_intensity(according_to_samples = qc_sample_name,
#'                         na.rm = TRUE) %>%
#'   mutate_mean_intensity(according_to_samples = blank_sample_name,
#'                         na.rm = TRUE) %>%
#'   activate_mass_dataset(what = "variable_info") %>%
#'   mutate(mean_intensity.1 = case_when(
#'     is.na(mean_intensity.1) ~ 0,
#'     TRUE ~ mean_intensity.1
#'   )) %>%
#'   mutate(mean_intensity = case_when(
#'     is.na(mean_intensity) ~ 0,
#'     TRUE ~ mean_intensity
#'   )) %>%
#'   mutate(qc_blank_ratio = mean_intensity.1 / mean_intensity) %>%
#'   mutate(qc_blank_ratio = case_when(
#'     is.na(qc_blank_ratio) ~ 0,
#'     TRUE ~ qc_blank_ratio
#'   )) %>%
#'   filter(qc_blank_ratio > 3)
#'
#' object4
#' object4 %>%
#'   extract_variable_info()

mutate_mean_intensity <-
  function(object,
           according_to_samples = "all",
           na.rm = TRUE) {
    check_object_class(object = object, class = "mass_dataset")
    variable_id <- get_variable_id(object)
    sample_id <- get_sample_id(object)
    
    if (any(according_to_samples == "all")) {
      according_to_samples <- sample_id
    } else{
      according_to_samples <-
        sample_id[sample_id %in% according_to_samples]
    }
    
    if (length(according_to_samples) == 0) {
      stop(
        "All the samples you provide in according_to_samples are not in the object.
           Please check."
      )
    }
    
    expression_data <-
      object@expression_data %>%
      as.data.frame()
    
    mean_intensity <-
      expression_data[, according_to_samples, drop = FALSE] %>%
      apply(1, function(x) {
        mean(x, na.rm = na.rm)
      })
    
    
    new_column_name <-
      check_column_name(object@variable_info ,
                        column.name = "mean_intensity")
    
    object@variable_info <-
      cbind(object@variable_info,
            mean_intensity = mean_intensity) %>%
      as.data.frame()
    
    colnames(object@variable_info)[ncol(object@variable_info)] <-
      new_column_name
    
    ####variable_info_note
    new_variable_info_note <-
      data.frame(
        name = setdiff(
          colnames(object@variable_info),
          object@variable_info_note$name
        ),
        meaning = setdiff(
          colnames(object@variable_info),
          object@variable_info_note$name
        ),
        check.names = FALSE
      )
    object@variable_info_note <-
      rbind(object@variable_info_note,
            new_variable_info_note)
    object@variable_info <-
      object@variable_info[, object@variable_info_note$name, drop = FALSE]
    
    process_info <- object@process_info
    
    parameter <- new(
      Class = "tidymass_parameter",
      pacakge_name = "massdataset",
      function_name = "mutate_mean_intensity()",
      parameter = list("according_to_samples" = according_to_samples),
      time = Sys.time()
    )
    
    if (all(names(process_info) != "mutate_mean_intensity")) {
      process_info$mutate_mean_intensity <- parameter
    } else{
      process_info$mutate_mean_intensity <-
        c(process_info$mutate_mean_intensity,
          parameter)
    }
    
    object@process_info <- process_info
    return(object)
  }