R/qc_ids.R

In protti: Bottom-Up Proteomics and LiP-MS Quality Control and Data Analysis Tools

#' Check number of precursor, peptide or protein IDs
#'
#' Returns a plot or table of the number of IDs for each sample. The default settings remove
#' grouping variables without quantitative information (intensity is NA). These will not be
#' counted as IDs.
#'
#' @param data a data frame containing at least sample names and precursor/peptide/protein IDs.
#' @param sample a character column in the \code{data} data frame that contains the sample name.
#' @param grouping a character column in the \code{data} data frame that contains either precursor or
#' peptide identifiers.
#' @param intensity a character column in the \code{data} data frame that contains raw or log2
#' transformed intensities. If \code{remove_na_intensities = FALSE}, this argument is optional.
#' @param remove_na_intensities a logical value that specifies if sample/grouping combinations with
#' intensities that are NA (not quantified IDs) should be dropped from the data frame. Default is
#' TRUE since we are usually interested in the number of quantifiable IDs.
#' @param condition optional, a column in the \code{data} data frame that contains condition information
#' (e.g. "treated" and "control"). If this column is provided, the bars in the plot will be coloured
#' according to the condition.
#' @param title optional, a character value that specifies the plot title (default is "ID count
#' per sample").
#' @param plot a logical value that indicates whether the result should be plotted.
#' @param interactive a logical value that specifies whether the plot should be interactive
#' (default is FALSE).
#'
#' @return A bar plot with the height corresponding to the number of IDs, each bar represents one
#' sample (if \code{plot = TRUE}). If \code{plot = FALSE} a table with ID counts is returned.
#' @import dplyr
#' @import ggplot2
#' @importFrom magrittr %>%
#' @importFrom tidyr drop_na
#' @importFrom plotly ggplotly
#' @importFrom utils data
#' @importFrom stringr str_sort
#' @export
#'
#' @examples
#' set.seed(123) # Makes example reproducible
#'
#' # Create example data
#' data <- create_synthetic_data(
#'   n_proteins = 100,
#'   frac_change = 0.05,
#'   n_replicates = 3,
#'   n_conditions = 2,
#'   method = "effect_random"
#' )
#'
#' # Calculate number of identifications
#' qc_ids(
#'   data = data,
#'   sample = sample,
#'   grouping = peptide,
#'   intensity = peptide_intensity_missing,
#'   condition = condition,
#'   plot = FALSE
#' )
#'
#' # Plot number of identifications
#' qc_ids(
#'   data = data,
#'   sample = sample,
#'   grouping = peptide,
#'   intensity = peptide_intensity_missing,
#'   condition = condition,
#'   plot = TRUE
#' )
qc_ids <-
  function(data,
           sample,
           grouping,
           intensity,
           remove_na_intensities = TRUE,
           condition = NULL,
           title = "ID count per sample",
           plot = TRUE,
           interactive = FALSE) {
    protti_colours <- "placeholder" # assign a placeholder to prevent a missing global variable warning
    utils::data("protti_colours", envir = environment()) # then overwrite it with real data
    if (remove_na_intensities == TRUE) {
      if (missing(intensity)) {
        stop(strwrap("Please provide a column containing intensities
or set remove_na_intensities to FALSE",
          prefix = "\n", initial = ""
        ))
      }
      data <- data %>%
        tidyr::drop_na({{ intensity }})
    }
    if (!missing(condition)) {
      data <- data %>%
        dplyr::mutate({{ condition }} := as.character({{ condition }}))
    }

    result <- data %>%
      dplyr::distinct({{ sample }}, {{ grouping }}, {{ condition }}) %>%
      dplyr::group_by({{ sample }}) %>%
      dplyr::mutate(count = dplyr::n()) %>%
      dplyr::select(-c({{ grouping }})) %>%
      dplyr::distinct() %>%
      dplyr::ungroup()

    if (plot == TRUE) {
      plot <- result %>%
        dplyr::mutate({{ sample }} := factor({{ sample }},
          levels = unique(stringr::str_sort({{ sample }}, numeric = TRUE))
        )) %>%
        ggplot2::ggplot(aes(x = {{ sample }}, y = .data$count, fill = {{ condition }})) +
        ggplot2::geom_col(col = "black", size = 1) +
        {
          if (missing(condition)) ggplot2::geom_col(fill = "#5680C1", col = "black")
        } +
        ggplot2::labs(
          title = title,
          x = "",
          y = "Count",
          fill = "Condition"
        ) +
        ggplot2::theme_bw() +
        ggplot2::scale_fill_manual(values = protti_colours) +
        ggplot2::theme(
          plot.title = ggplot2::element_text(size = 20),
          axis.title.x = ggplot2::element_text(size = 15),
          axis.text.y = ggplot2::element_text(size = 15),
          axis.text.x = ggplot2::element_text(size = 12, angle = 75, hjust = 1),
          axis.title.y = ggplot2::element_text(size = 15),
          legend.title = ggplot2::element_text(size = 15),
          legend.text = ggplot2::element_text(size = 15)
        )
      if (interactive == TRUE) {
        return(plotly::ggplotly(plot))
      } else {
        return(plot)
      }
    } else {
      return(result)
    }
  }