R/qc_data_completeness.R

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

#' Data completeness
#'
#' Calculates the percentage of data completeness. That means, what percentage of all detected
#' precursors is present in each sample.
#'
#' @param data a data frame containing at least the input variables.
#' @param sample a character column in the \code{data} data frame that contains the sample names.
#' @param grouping a character column in the \code{data} data frame that contains either precursor
#' or peptide identifiers.
#' @param intensity a numeric column in the \code{data} data frame that contains any intensity
#' intensity values that missingness should be determined for.
#' @param digestion optional, a character column in the \code{data} data frame that indicates the
#' mode of digestion (limited proteolysis or tryptic digest). Alternatively, any other variable
#' by which the data should be split can be provided.
#' @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 that displays the percentage of data completeness over all samples.
#' If \code{plot = FALSE} a data frame is returned. If \code{interactive = TRUE}, the plot is
#' interactive.
#' @import dplyr
#' @import ggplot2
#' @importFrom purrr map2_df
#' @importFrom tidyr complete
#' @importFrom plotly ggplotly
#' @importFrom magrittr %>%
#' @importFrom rlang .data := new_formula enquo
#' @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"
#' )
#'
#' # Determine data completeness
#' qc_data_completeness(
#'   data = data,
#'   sample = sample,
#'   grouping = peptide,
#'   intensity = peptide_intensity_missing,
#'   plot = FALSE
#' )
#'
#' # Plot data completeness
#' qc_data_completeness(
#'   data = data,
#'   sample = sample,
#'   grouping = peptide,
#'   intensity = peptide_intensity_missing,
#'   plot = TRUE
#' )
qc_data_completeness <- function(data,
                                 sample,
                                 grouping,
                                 intensity,
                                 digestion = NULL,
                                 plot = TRUE,
                                 interactive = FALSE) {
  . <- NULL

  if (!missing(digestion)) {
    result <- data %>%
      split(dplyr::pull(data, {{ digestion }})) %>%
      purrr::map2_df(
        .y = names(.),
        .f = ~ .x %>%
          dplyr::distinct({{ sample }}, {{ grouping }}, {{ intensity }}) %>%
          tidyr::complete({{ sample }}, {{ grouping }}) %>%
          dplyr::group_by({{ sample }}) %>%
          dplyr::summarise(completeness = sum(!is.na({{ intensity }})) / dplyr::n() * 100, .groups = "drop") %>%
          dplyr::mutate({{ digestion }} := .y)
      )
  } else {
    result <- data %>%
      dplyr::distinct({{ sample }}, {{ grouping }}, {{ intensity }}) %>%
      tidyr::complete({{ sample }}, {{ grouping }}) %>%
      dplyr::group_by({{ sample }}) %>%
      dplyr::summarise(completeness = sum(!is.na({{ intensity }})) / dplyr::n() * 100, .groups = "drop")
  }

  if (plot == FALSE) {
    return(result)
  }

  completeness_plot <- result %>%
    dplyr::mutate({{ sample }} := factor({{ sample }},
      levels = unique(stringr::str_sort({{ sample }}, numeric = TRUE))
    )) %>%
    ggplot2::ggplot(ggplot2::aes({{ sample }}, .data$completeness)) +
    ggplot2::geom_col(fill = "#5680C1", col = "black", size = 1) +
    {
      if (interactive == FALSE) {
        geom_text(
          data = result,
          aes(label = round(.data$completeness, digits = 1)),
          position = position_stack(vjust = 0.5)
        )
      }
    } +
    ggplot2::scale_y_continuous(limits = c(0, 100)) +
    {
      if (!missing(digestion)) {
        ggplot2::facet_wrap(rlang::new_formula(NULL, rlang::enquo(digestion)), scales = "free", ncol = 2)
      }
    } +
    ggplot2::labs(
      title = "Data completeness per .raw file",
      x = "",
      y = "Data Completeness [%]"
    ) +
    ggplot2::theme_bw() +
    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),
      panel.border = ggplot2::element_rect(fill = NA),
      strip.text = ggplot2::element_text(size = 15),
      strip.background = element_rect(fill = "white")
    )

  if (interactive == FALSE) {
    return(completeness_plot)
  }

  suppressWarnings(plotly::ggplotly(completeness_plot))
}