R/filter_data.R

In jaspershen/metflow2: Comprehensitive tools for metabolomics data processing and data analysis

#' @title filter_rsd
#' @description Filter peaks according to RSD.
#' @author Xiaotao Shen
#' \email{shenxt1990@@163.com}
#' @param object A metflowClass object.
#' @param slot What group samples are used to calculate RSD.
#' @param rsd.cutoff RSD cutoff.
#' @return A new metflowClass object.
#' @export

filter_rsd = function(object,
                      slot = c("Subject", "QC"),
                      rsd.cutoff = 30){
  slot <- match.arg(slot)
  if (class(object) != "metflowClass") {
    stop("Only the metflowClass is supported!\n")
  }
  if (length(object@ms1.data) > 1) {
    stop("Please align batch first.\n")
  }
  
  rsd <- calculate_rsd(object = object, slot = slot)
  
  remain_idx <- which(rsd < rsd.cutoff)
  object@ms1.data <-
    list(object@ms1.data[[1]][remain_idx, , drop = FALSE])
  
  object@process.info$filterRSD <- list()
  object@process.info$filterRSD$slot <- slot
  object@process.info$filterRSD$rsd.cutoff <- rsd.cutoff
  invisible(object)
}




#' @title Filter samples
#' @description Filter samples.
#' @author Xiaotao Shen
#' \email{shenxt1990@@163.com}
#' @param object A metflowClass object.
#' @param min.fraction.peak Peaks minimun fraction in subject samples.
#' @return A new metflowClass object.
#' @export

filter_samples = function(object,
                          min.fraction.peak = 0.8){
  if (class(object) != "metflowClass") {
    stop("Only the metflowClass is supported!\n")
  }
  
  ms1_data <- object@ms1.data
  
  if (length(ms1_data) > 1) {
    stop("Please algin your peak tables first!\n")
  }
  
  ms1_data <- ms1_data[[1]]
  
  sample_info <- object@sample.info
  
  sample_name <- 
    sample_info %>% 
    dplyr::filter(class %in% c("Subject", "QC")) %>% 
    dplyr::pull(sample.name)
  
  sample_class <- 
    sample_info %>% 
    dplyr::filter(class %in% c("Subject", "QC")) %>% 
    dplyr::pull(class)
  
  subject_qc_data <-
    ms1_data %>% 
    dplyr::select(dplyr::one_of(sample_name)) %>% 
    tibble::as_tibble()
  
  rm(list = "ms1_data")
  
  na.fraction <- apply(subject_qc_data, 2, function(x) {
    sum(is.na(x) / nrow(subject_qc_data))
  })
  
  remove.idx.na.fraction <-
    which(na.fraction > 1 - min.fraction.peak)
  
  cat(
    "Samples with MV ratio larger than",
    1 - min.fraction.peak,
    ":\n",
    paste(names(remove.idx.na.fraction), collapse = "; ")
  )
  cat("\n")
  
  # na.fraction <- sort(na.fraction)
  na.fraction <-
    data.frame(
      peak.name = names(na.fraction),
      index = 1:length(na.fraction),
      class = sample_class,
      na.fraction,
      stringsAsFactors = FALSE
    )
  
  na.fraction <-
    left_join(na.fraction,
              object@sample.info[, c(1, 2)],
              by = c("peak.name" = "sample.name"))
  
  plot <- ggplot(data = na.fraction) +
    geom_point(aes(
      x = injection.order,
      y = na.fraction * 100,
      colour = class
    ),
    size = 2) +
    # scale_x_discrete(
    #   breaks = c("QC", "Subject"),
    #   labels = c("QC", "Subject"),
    #   # name = "Class"
    # ) +
    scale_colour_manual(values = c("#E64B35FF", "#4DBBD5FF")) +
    labs(x = "Injection order", y = "Missing value ratio (%)") +
    geom_hline(
      yintercept = 100 - min.fraction.peak * 100,
      color = "red",
      linetype = 2
    ) +
    ggrepel::geom_text_repel(
      data = dplyr::filter(na.fraction,
                           na.fraction > 1 - min.fraction.peak),
      mapping = aes(x = injection.order, y = na.fraction * 100,
                    label = peak.name)
    ) +
    theme_bw() +
    theme(
      axis.title = element_text(size = 15),
      axis.text = element_text(size = 12),
      legend.title = element_text(size = 15),
      legend.text = element_text(size = 12)
    )
  
  if (length(remove.idx.na.fraction) > 0) {
    remove.name <- colnames(subject_qc_data)[remove.idx.na.fraction]
    
    object@sample.info <-
      object@sample.info %>%
      dplyr::filter(., !(sample.name %in% remove.name))
    
    object@ms1.data[[1]] <-
      object@ms1.data[[1]] %>%
      dplyr::select(., -one_of(remove.name))
  }
  
  object@process.info$filterSample <- list()
  object@process.info$filterSample$min.fraction.peak <-
    min.fraction.peak
  object@process.info$filterSample$plot <- plot
  cat(crayon::bgRed("All done!\n"))
  invisible(object)
}


#' @title Filter peaks according to missing values.
#' @description Filter peaks.
#' @author Xiaotao Shen
#' \email{shenxt1990@@163.com}
#' @param object A metflowClass object.
#' @param min.fraction Peaks minimum fraction in samples (which.group).
#' @param type Any ("any") or all ("all") groups should be meet this min.fraction.
#' @param min.subject.blank.ratio Peak intensity ratio in subject and blank samples. If you set it < 1, no peaks will be removed.
#' @param dl.qc.r2.cutoff R2 cutoff for dilution QC. If you set it < 0, no peaks will be removed.
#' @param according.to Which information you want to use in sample information.
#' @param which.group What groups you want to use.
#' @return A new metflowClass object.
#' @export

filter_peaks = function(
  object,
  min.fraction = 0.5,
  type = c("any", "all"),
  min.subject.blank.ratio = 2,
  dl.qc.r2.cutoff = 0.7,
  according.to = c("class", "group"),
  which.group
){
  if (class(object) != "metflowClass") {
    stop("Only the metflowClass is supported!\n")
  }
  type <- match.arg(type)
  according.to <- match.arg(according.to)
  if(missing(which.group)){
    stop("Please provide the which.group\n")
  }
  
  sample_info <- 
    object@sample.info %>% 
    tibble::as_tibble()
  
  which.group <- 
    which.group[which.group %in% unique(dplyr::pull(sample_info, according.to))]
  
  if(length(which.group) == 0){
    stop("No group in the sample inforamtion.\n")
  }
  
  cat(crayon::yellow(paste(which.group, collapse = ";"), "are in the", according.to, ".\n"))
  
  ms1_data <- object@ms1.data
  
  if (length(ms1_data) > 1) {
    stop("Please algin your peak tables first!\n")
  }
  ms1_data <-
    ms1_data[[1]] %>%
    tibble::as_tibble()
  
  # sample <- ms1_data[,match(sample_info$sample.name, colnames(ms1_data))]
  # tags <- ms1_data[,-match(sample_info$sample.name, colnames(ms1_data))]
  
  object@process.info$filterPeaks <- list()
  
  cat(crayon::yellow(paste(rep("-", 20), collapse = ""), "\n"))
  cat(crayon::green("Removing peaks according to NA in samples...\n"))
  
  #remove peaks according to NA in samples
  
  non_na_fraction <- 
    lapply(which.group, function(x){
      temp_name <- 
        which(dplyr::pull(sample_info, according.to) == x) %>% 
        `[`(sample_info,.,) %>% 
        dplyr::pull(sample.name)
      
      temp_non_na_fraction <-
        ms1_data %>%
        dplyr::select(temp_name) %>%
        apply(1, function(x)
          sum(!is.na(x)) / length(temp_name))
      temp_non_na_fraction
    })
  
  non_na_fraction <- 
    non_na_fraction %>% 
    dplyr::bind_cols()
  
  colnames(non_na_fraction) <- which.group
  
  remain_idx <- 
    apply(non_na_fraction, 1, function(x){
      if(type == "all"){
        all(x >= min.fraction)
      }else{
        any(x >= min.fraction)
      }
    }) %>% 
    which()
  
  if (length(remain_idx) == 0) {
    stop(paste("No peaks meet min.fraction:", min.fraction.qc))
  }
  
  cat(crayon::yellow(
    length(remain_idx),
    "out of",
    nrow(ms1_data),
    "peaks are remained.\n"
  ))
  
  ms1_data <- ms1_data[remain_idx, , drop = FALSE]
  
  object@process.info$filterPeaks$min.fraction <-
    min.fraction
  
  object@process.info$filterPeaks$which.group <-
    paste(which.group, collapse = ";")
  
  rm(list = c("remain_idx"))
  
  cat(crayon::yellow(paste(rep("-", 20), collapse = ""), "\n"))
  cat(crayon::yellow("Removing peaks according to blank samples...\n"))
  
  ##--------------------------------------------------------------------------
  ##remove peaks according to blank
  if ("Blank" %in% sample_info$class & min.subject.blank.ratio > 1) {
    blank_data <-
      `==`(sample_info$class, "Blank") %>%
      which(.) %>%
      `[`(sample_info$sample.name, .) %>%
      dplyr::select(.data = ms1_data, .)
    
    peak_mean_int_blank <-
      apply(blank_data, 1, function(x)
        mean(x, na.rm = TRUE))
    
    peak_mean_int_blank[is.na(peak_mean_int_blank)] <- 0
    
    subject_data <- 
      ms1_data %>% 
      dplyr::select(
        sample_info$sample.name[sample_info$class %in% which.group]
      )
    
    peak_mean_int_subject <-
      apply(subject_data, 1, function(x)
        mean(x, na.rm = TRUE))
    peak_mean_int_subject[is.na(peak_mean_int_subject)] <-
      0
    
    ratio <- peak_mean_int_subject / peak_mean_int_blank
    
    ratio[is.na(ratio)] <- 0
    
    ratio[is.infinite(ratio)] <- min.subject.blank.ratio + 1
    
    remain_idx <- which(ratio > min.subject.blank.ratio)
    if (length(remain_idx) == 0) {
      stop(paste(
        "No peaks meet min.subject.blank.ratio:",
        min.subject.blank.ratio
      ))
    }
    cat(length(remain_idx),
        "out of",
        nrow(ms1_data),
        "peaks are remained.\n")
    object@process.info$filterPeaks$min.subject.blank.ratio <-
      min.subject.blank.ratio
    ms1_data <- ms1_data[remain_idx, , drop = FALSE]
    rm(
      list = c(
        "blank_data",
        "subject_data",
        "peak_mean_int_subject",
        "peak_mean_int_blank",
        "ratio",
        "remain_idx"
      )
    )
  }else{
    cat(crayon::yellow("No Blanks in your data.\n"))
  }
  
  cat(crayon::yellow(paste(rep("-", 20), collapse = ""), "\n"))
  cat(crayon::green("Removing peaks according to QC dilution samples...\n"))
  ###remove peaks according to dilution
  if ("QC.DL" %in% sample_info$class & dl.qc.r2.cutoff > 0) {
    qc_dl_sample <-
      `==`(sample_info$class, "QC.DL") %>%
      which(.) %>%
      `[`(sample_info$sample.name, .) %>%
      match(., colnames(ms1_data)) %>%
      `[`(ms1_data, , .)
    qc_dl_sample <-
      qc_dl_sample[, order(colnames(qc_dl_sample))]
    cat(crayon::yellow(
      "The QC_DL sample names are:",
      paste(colnames(qc_dl_sample), collapse = "; "),
      "\n" 
    ))
    
    dl_name <-
      stringr::str_extract_all(string = colnames(qc_dl_sample),
                               pattern = "DL[0-9]{1,2}") %>%
      unlist()
    
    qc_dl_sample <-
      lapply(sort(unique(dl_name)), function(x) {
        which(x == dl_name) %>%
          `[`(qc_dl_sample, , .)
      })
    
    qc_dl_sample <- lapply(qc_dl_sample, function(x) {
      temp <- apply(x, 1, function(x)
        mean(x, na.rm = TRUE))
      temp[is.na(temp)] <- 0
      temp
    })
    
    qc_dl_sample <- do.call(cbind, qc_dl_sample)
    colnames(qc_dl_sample) <- sort(unique(dl_name))
    qc_dl_sample <-
      as.data.frame(qc_dl_sample, stringsAsFactors = FALSE)
    dl_grade <-
      stringr::str_extract(string = sort(unique(dl_name)),
                           pattern = "[0-9]{1,2}") %>%
      as.numeric(.)
    ####construct linear regression
    remain_idx <- apply(qc_dl_sample, 1, function(y) {
      y <- as.numeric(y)
      temp.lm <- lm(y ~ dl_grade)
      (coefficients(temp.lm)[2] < 0 &
          summary(temp.lm)$r.squared > dl.qc.r2.cutoff)
    }) %>%
      which(.)
    
    if (length(remain_idx) == 0) {
      stop(paste("No peaks meet dl.qc.r2.cutoff:", dl.qc.r2.cutoff))
    }
    
    cat(crayon::yellow(
      length(remain_idx),
      "out of",
      nrow(ms1_data),
      "peaks are remained.\n" 
    ))
    
    object@process.info$filterPeaks$dl.qc.r2.cutoff <-
      dl.qc.r2.cutoff
    ms1_data <- ms1_data[remain_idx, , drop = FALSE]
  }else{
    # cat(crayon::yellow("No DL.QC samples in your data.\n"))
  }
  
  ms1_data <- list(ms1_data)
  object@ms1.data <- ms1_data
  cat(crayon::bgRed("All done.\n"))
  invisible(object)
}






#' @title filterPeak
#' @description Filter peaks.
#' @author Xiaotao Shen
#' \email{shenxt1990@@163.com}
#' @param object A metflowClass object.
#' @param min.fraction.qc Peaks minimum fraction in QC samples.
#' @param min.fraction Peaks minimun fraction in subject samples.
#' @param min.subject.qc.ratio Peak intensity ratio in subject and blank samples.
#' @param dl.qc.r2.cutoff R2 cutoff for dilution QC.
#' @param silence.deprecated Silence deprecated information or not.
#' @return A new metflowClass object.
#' @export

filterPeak = function(
  object,
  min.fraction.qc = 0.8,
  min.fraction = 0.8,
  min.subject.qc.ratio = 2,
  dl.qc.r2.cutoff = 0.7,
  silence.deprecated = FALSE
){
  if(!silence.deprecated){
    cat(crayon::yellow("`filterPeak()` is deprecated, please use `filter_peaks()`"))
  }
  if (class(object) != "metflowClass") {
    stop("Only the metflowClass is supported!\n")
  }
  
  ms1_data <- object@ms1.data
  
  if (length(ms1_data) > 1) {
    stop("Please algin your peak tables first!\n")
  }
  ms1_data <- ms1_data[[1]]
  # sample <- ms1_data[,match(sample_info$sample.name, colnames(ms1_data))]
  # tags <- ms1_data[,-match(sample_info$sample.name, colnames(ms1_data))]
  sample_info <- object@sample.info
  object@process.info$filterPeaks <- list()
  
  cat(paste(rep("-", 20), collapse = ""), "\n")
  cat("Removing peaks according to NA in QC samples...\n")
  #remove peaks according to NA in QC samples
  if ("QC" %in% sample_info$class) {
    qc_sample <-
      `==`(sample_info$class, "QC") %>%
      which(.) %>%
      `[`(sample_info$sample.name, .) %>%
      match(., colnames(ms1_data)) %>%
      `[`(ms1_data, , .)
    
    na.fraction <- apply(qc_sample, 1, function(x) {
      sum(!is.na(x)) / ncol(qc_sample)
    })
    
    remain_idx <- which(na.fraction > min.fraction.qc)
    if (length(remain_idx) == 0) {
      stop(paste("No peaks meet min.fraction.qc:", min.fraction.qc))
    }
    cat(length(remain_idx),
        "out of",
        nrow(ms1_data),
        "peaks are remained.\n")
    ms1_data <- ms1_data[remain_idx, , drop = FALSE]
    object@process.info$filterPeaks$min.fraction.qc <-
      min.fraction.qc
    rm(list = c("remain_idx"))
  }
  
  
  cat(paste(rep("-", 20), collapse = ""), "\n")
  cat("Removing peaks according to NA in subject samples...\n")
  
  ##remove peaks according to NA in subject samples
  subject_name <-
    which(sample_info$class == "Subject") %>%
    `[`(sample_info$sample.name, .)
  
  subject_data <- subject_name %>%
    match(., colnames(ms1_data)) %>%
    `[`(ms1_data, , .)
  
  subject_group <- subject_name %>%
    match(., sample_info$sample.name) %>%
    `[`(sample_info$group, .)
  
  na.fraction <-
    lapply(unique(subject_group), function(group) {
      temp_subject_data <-
        subject_data[, which(subject_group == group), drop = FALSE]
      apply(temp_subject_data, 1, function(x)
        sum(!is.na(x)) / ncol(temp_subject_data))
    })
  
  na.fraction <- do.call(cbind, na.fraction)
  remain_idx <- apply(na.fraction, 1, function(x) {
    any(x >  min.fraction)
  }) %>%
    which(.)
  
  if (length(remain_idx) == 0) {
    stop(paste("No peaks meet min.fraction:", min.fraction))
  }
  cat(length(remain_idx),
      "out of",
      nrow(ms1_data),
      "peaks are remained.\n")
  ms1_data <- ms1_data[remain_idx, , drop = FALSE]
  subject_data <-
    subject_data[remain_idx, , drop = FALSE]
  object@process.info$filterPeaks$min.fraction <-
    min.fraction
  rm(list = c(
    "remain_idx",
    "na.fraction",
    "subject_group",
    "subject_name"
  ))
  
  cat(paste(rep("-", 20), collapse = ""), "\n")
  cat("Removing peaks according to blank samples...\n")
  ##remove peaks according to blank
  if ("Blank" %in% sample_info$class) {
    blank_data <-
      `==`(sample_info$class, "Blank") %>%
      which(.) %>%
      `[`(sample_info$sample.name, .) %>%
      dplyr::select(.data = ms1_data, .)
    peak_mean_int_blank <-
      apply(blank_data, 1, function(x)
        mean(x, na.rm = TRUE))
    peak_mean_int_blank[is.na(peak_mean_int_blank)] <- 0
    
    peak_mean_int_subject <-
      apply(subject_data, 1, function(x)
        mean(x, na.rm = TRUE))
    peak_mean_int_subject[is.na(peak_mean_int_subject)] <-
      0
    
    ratio <- peak_mean_int_subject / peak_mean_int_blank
    ratio[is.na(ratio)] <- 0
    remain_idx <- which(ratio > min.subject.qc.ratio)
    if (length(remain_idx) == 0) {
      stop(paste(
        "No peaks meet min.subject.qc.ratio:",
        min.subject.qc.ratio
      ))
    }
    cat(length(remain_idx),
        "out of",
        nrow(ms1_data),
        "peaks are remained.\n")
    object@process.info$filterPeaks$min.subject.qc.ratio <-
      min.subject.qc.ratio
    ms1_data <- ms1_data[remain_idx, , drop = FALSE]
    rm(
      list = c(
        "blank_data",
        "subject_data",
        "peak_mean_int_subject",
        "peak_mean_int_blank",
        "ratio",
        "remain_idx"
      )
    )
  }
  
  cat(paste(rep("-", 20), collapse = ""), "\n")
  cat("Removing peaks according to QC dilution samples...\n")
  ###remove peaks according to dilution
  if ("QC.DL" %in% sample_info$class) {
    qc_dl_sample <-
      `==`(sample_info$class, "QC.DL") %>%
      which(.) %>%
      `[`(sample_info$sample.name, .) %>%
      match(., colnames(ms1_data)) %>%
      `[`(ms1_data, , .)
    qc_dl_sample <-
      qc_dl_sample[, order(colnames(qc_dl_sample))]
    cat("The QC_DL sample names are:",
        paste(colnames(qc_dl_sample), collapse = "; "),
        "\n")
    
    dl_name <-
      stringr::str_extract_all(string = colnames(qc_dl_sample),
                               pattern = "DL[0-9]{1,2}") %>%
      unlist()
    
    qc_dl_sample <-
      lapply(sort(unique(dl_name)), function(x) {
        which(x == dl_name) %>%
          `[`(qc_dl_sample, , .)
      })
    
    qc_dl_sample <- lapply(qc_dl_sample, function(x) {
      temp <- apply(x, 1, function(x)
        mean(x, na.rm = TRUE))
      temp[is.na(temp)] <- 0
      temp
    })
    
    qc_dl_sample <- do.call(cbind, qc_dl_sample)
    colnames(qc_dl_sample) <- sort(unique(dl_name))
    qc_dl_sample <-
      as.data.frame(qc_dl_sample, stringsAsFactors = FALSE)
    dl_grade <-
      stringr::str_extract(string = sort(unique(dl_name)),
                           pattern = "[0-9]{1,2}") %>%
      as.numeric(.)
    ####construct linear regression
    remain_idx <- apply(qc_dl_sample, 1, function(y) {
      y <- as.numeric(y)
      temp.lm <- lm(y ~ dl_grade)
      (coefficients(temp.lm)[2] < 0 &
          summary(temp.lm)$r.squared > dl.qc.r2.cutoff)
    }) %>%
      which(.)
    
    if (length(remain_idx) == 0) {
      stop(paste("No peaks meet dl.qc.r2.cutoff:", dl.qc.r2.cutoff))
    }
    
    cat(length(remain_idx),
        "out of",
        nrow(ms1_data),
        "peaks are remained.\n")
    
    object@process.info$filterPeaks$dl.qc.r2.cutoff <-
      dl.qc.r2.cutoff
    ms1_data <- ms1_data[remain_idx, , drop = FALSE]
  }
  
  ms1_data <- list(ms1_data)
  object@ms1.data <- ms1_data
  cat("All is done.\n")
  invisible(object)
}




#' @title filterSample
#' @description Filter samples.
#' @author Xiaotao Shen
#' \email{shenxt1990@@163.com}
#' @param object A metflowClass object.
#' @param min.fraction.peak Peaks minimun fraction in subject samples.
#' @param silence.deprecated Silence deprecated information or not.
#' @return A new metflowClass object.
#' @export

filterSample = 
  function(object,
           min.fraction.peak = 0.8,
           silence.deprecated = FALSE){
    if(!silence.deprecated){
      cat(crayon::yellow("`filterSample()` is deprecated, please use `filter_samples()`"))
    }
    
    if (class(object) != "metflowClass") {
      stop("Only the metflowClass is supported!\n")
    }
    
    ms1_data <- object@ms1.data
    
    if (length(ms1_data) > 1) {
      stop("Please algin your peak tables first!\n")
    }
    ms1_data <- ms1_data[[1]]
    qc_data <- get_data(object = object, slot = "QC")
    subject_data <-
      get_data(object = object, slot = "Subject")
    subject_qc_data <- cbind(qc_data, subject_data)
    subject_qc_data <- tibble::as_tibble(subject_qc_data)
    class <-
      c(rep("QC", ncol(qc_data)), rep("Subject", ncol(subject_data)))
    
    na.fraction <- apply(subject_qc_data, 2, function(x) {
      sum(is.na(x) / nrow(subject_qc_data))
    })
    
    
    remove.idx.na.fraction <-
      which(na.fraction > 1 - min.fraction.peak)
    
    cat(
      "Samples with MV ratio larger than",
      min.fraction.peak,
      ":\n",
      paste(names(remove.idx.na.fraction), collapse = "; ")
    )
    cat("\n")
    
    # na.fraction <- sort(na.fraction)
    na.fraction <-
      data.frame(
        peak.name = names(na.fraction),
        index = 1:length(na.fraction),
        class = class,
        na.fraction,
        stringsAsFactors = FALSE
      )
    
    na.fraction <-
      left_join(na.fraction,
                object@sample.info[, c(1, 2)],
                by = c("peak.name" = "sample.name"))
    
    plot <- ggplot(data = na.fraction) +
      geom_point(aes(
        x = injection.order,
        y = na.fraction * 100,
        colour = class
      ),
      size = 2) +
      scale_colour_discrete(
        breaks = c("QC", "Subject"),
        labels = c("QC", "Subject"),
        name = "Class"
      ) +
      scale_colour_manual(values = c("#E64B35FF", "#4DBBD5FF")) +
      labs(x = "Injection order", y = "Missing value ratio (%)") +
      geom_hline(
        yintercept = 100 - min.fraction.peak * 100,
        color = "red",
        linetype = 2
      ) +
      ggrepel::geom_text_repel(
        data = dplyr::filter(na.fraction,
                             na.fraction > 1 - min.fraction.peak),
        mapping = aes(x = injection.order, y = na.fraction * 100,
                      label = peak.name)
      ) +
      theme_bw() +
      theme(
        axis.title = element_text(size = 15),
        axis.text = element_text(size = 12),
        legend.title = element_text(size = 15),
        legend.text = element_text(size = 12)
      )
    
    plot
    
    if (length(remove.idx.na.fraction) > 0) {
      remove.name <- colnames(subject_qc_data)[remove.idx.na.fraction]
      
      object@sample.info <-
        object@sample.info %>%
        dplyr::filter(., !(sample.name %in% remove.name))
      
      object@ms1.data[[1]] <-
        object@ms1.data[[1]] %>%
        dplyr::select(., -one_of(remove.name))
    }
    
    object@process.info$filterSample <- list()
    object@process.info$filterSample$min.fraction.peak <-
      min.fraction.peak
    object@process.info$filterSample$plot <- plot
    cat("All is done!\n")
    invisible(object)
  }



#' @title filterRSD
#' @description Filter peaks according to RSD.
#' @author Xiaotao Shen
#' \email{shenxt1990@@163.com}
#' @param object A metflowClass object.
#' @param slot What group samples are used to calculate RSD.
#' @param rsd.cutoff RSD cutoff.
#' @param silence.deprecated Silence deprecated information or not.
#' @return A new metflowClass object.
#' @export

filterRSD = function(object,
                     slot = c("Subject", "QC"),
                     rsd.cutoff = 30,
                     silence.deprecated = FALSE){
  
  if(!silence.deprecated){
    cat(crayon::yellow("`filterRSD()` is deprecated, please use `filter_rsd()`"))
  }
  
  slot <- match.arg(slot)
  if (class(object) != "metflowClass") {
    stop("Only the metflowClass is supported!\n")
  }
  if (length(object@ms1.data) > 1) {
    stop("Please align batch first.\n")
  }
  
  rsd <- calculate_rsd(object = object, slot = slot)
  
  remain_idx <- which(rsd < rsd.cutoff)
  object@ms1.data <-
    list(object@ms1.data[[1]][remain_idx, , drop = FALSE])
  
  object@process.info$filterRSD <- list()
  object@process.info$filterRSD$slot <- slot
  object@process.info$filterRSD$rsd.cutoff <- rsd.cutoff
  invisible(object)
}