R/get_summary_Report.R

In mpwR: Standardized Comparison of Workflows in Mass Spectrometry-Based Bottom-Up Proteomics

#' Summary report
#'
#' Generates a summary report
#'
#' For each submitted data a summary report including information about achieved identifications (ID), data completeness (DC), missed cleavages (MC), and both quantitative (LFQ) and retention time (RT) precision is generated.
#'
#' @param input_list A list with data frames including ID, DC, MC, LFQ and RT information.
#' @param CV_RT_th_hold Numeric. User-specified threshold for CV value of retention time precision. Default is 5.
#' @param CV_LFQ_Pep_th_hold Numeric. User-specified threshold for CV value of quantitative precision. Default is 20.
#' @param CV_LFQ_PG_th_hold Numeric. User-specified threshold for CV value of quantitative precision. Default is 20.
#'
#' @author Oliver Kardell
#'
#' @return This function returns a list. For each analysis a respective data frame is stored in the list with the following information:
#' \itemize{
#'  \item Analysis - analysis name.
#'  \item "Median ProteinGroup.IDs abs." - median number of proteingroup identifications.
#'  \item "Median Protein.IDs abs." - median number of protein identifications.
#'  \item "Median Peptide.IDs abs." - median number of peptide identifications.
#'  \item "Median Precursor.IDs abs." - median number of precursor identifications.
#'  \item "Full profile - Precursor.IDs abs." - number of precursor identifications for full profiles.
#'  \item "Full profile - Peptide.IDs abs." - number of peptide identifications for full profiles.
#'  \item "Full profile - Protein.IDs abs." - number of protein identifications for full profiles.
#'  \item "Full profile - ProteinGroup.IDs abs." - number of proteingroup identifications for full profiles.
#'  \item "Full profile - Precursor.IDs %" - number of precursor identifications for full profiles in percentage.
#'  \item "Full profile - Peptide.IDs %" - number of peptide identifications for full profiles in percentage.
#'  \item "Full profile - Protein.IDs %" - number of protein identifications for full profiles in percentage.
#'  \item "Full profile - ProteinGroup.IDs %" - number of proteinGroup identifications for full profiles in percentage.
#'  \item "Precursor.IDs abs. with a CV Retention time < X %" - number of precursor identifications with a CV value for retention time precision under user-specified threshold X.
#'  \item "Proteingroup.IDs abs. with a CV LFQ < X %" - number of proteingroup identifications with a CV value for quantitative precision under user-specified threshold X.
#'  \item "Peptide.IDs abs. with a CV LFQ < X %" - number of peptide identifications with a CV value for quantitative precision under user-specified threshold X.
#'  \item "Peptide IDs with zero missed cleavages abs." - number of peptide identifications with zero missed cleavages.
#'  \item "Peptide IDs with zero missed cleavages %" - number of peptide identifications with zero missed cleavages in percentage.
#' }
#'
#' @export
#'
#' @examples
#' # Load libraries
#' library(tibble)
#'
#' # Example data
#' data <- list(
#' DIANN = list(
#'  filename = "B",
#'  software = "DIA-NN",
#'  data = list(
#'    "DIA-NN" = tibble::tibble(
#'      "Run_mpwR" = c("R01", "R01", "R02", "R03", "R01"),
#'      "Precursor.IDs_mpwR" = c("A1", "A1", "A1", "A1", "B2"),
#'      "Retention.time_mpwR" = c(3, 3.5, 4, 5, 4),
#'      "ProteinGroup_LFQ_mpwR" = c(3, 4, 5, 4, 4),
#'      "Peptide.IDs_mpwR" = c("A", "A", "A", "A", "B"),
#'      "Protein.IDs_mpwR" = c("A", "A", "A", "A", "B"),
#'      "ProteinGroup.IDs_mpwR" = c("A", "A", "A", "A", "B"),
#'      "Stripped.Sequence_mpwR" = c("ABCR", "AKCR", "ABKCK", "ARKAR", "ABCDR")
#'    )
#'  )
#' )
#' )
#'
#' # Result
#' output <- get_summary_Report(
#'   input_list = data
#' )

get_summary_Report <- function(input_list,
                                CV_RT_th_hold = 5,
                                CV_LFQ_Pep_th_hold = 20,
                                CV_LFQ_PG_th_hold = 20) {

  output_list <- list()

  for (i in seq_len(length(input_list))) {
    if (input_list[[i]][["software"]] == "DIA-NN") {
      output_list[[i]] <- generate_summary_Report(input_df = input_list[[i]][["data"]][["DIA-NN"]], analysis_name = input_list[[i]][["filename"]], software = "DIA-NN", CV_RT_th_hold = CV_RT_th_hold, CV_LFQ_Pep_th_hold = CV_LFQ_Pep_th_hold,  CV_LFQ_PG_th_hold =  CV_LFQ_PG_th_hold)
      names(output_list)[i] <- input_list[[i]][["filename"]]
      next
    } else if (input_list[[i]][["software"]] == "Spectronaut") {
      output_list[[i]] <- generate_summary_Report(input_df = input_list[[i]][["data"]][["Spectronaut"]], analysis_name = input_list[[i]][["filename"]], software = "Spectronaut", CV_RT_th_hold = CV_RT_th_hold, CV_LFQ_Pep_th_hold = CV_LFQ_Pep_th_hold,  CV_LFQ_PG_th_hold =  CV_LFQ_PG_th_hold)
      names(output_list)[i] <- input_list[[i]][["filename"]]
      next
    } else if (input_list[[i]][["software"]] == "MaxQuant") {
      output_list[[i]] <- generate_summary_Report(input_df = input_list[[i]][["data"]][["ev"]], input_MQ_peptide = input_list[[i]][["data"]][["pep"]], input_MQ_proteingroup = input_list[[i]][["data"]][["pg"]], analysis_name = input_list[[i]][["filename"]], software = "MaxQuant", CV_RT_th_hold = CV_RT_th_hold, CV_LFQ_Pep_th_hold = CV_LFQ_Pep_th_hold,  CV_LFQ_PG_th_hold =  CV_LFQ_PG_th_hold)
      names(output_list)[i] <- input_list[[i]][["filename"]]
      next
    } else if (input_list[[i]][["software"]] == "PD") {
      output_list[[i]] <- generate_summary_Report(input_df = input_list[[i]][["data"]][["psm"]], input_PD_peptide = input_list[[i]][["data"]][["pep"]], input_PD_protein = input_list[[i]][["data"]][["prot"]], input_PD_proteingroup = input_list[[i]][["data"]][["pg"]], analysis_name = input_list[[i]][["filename"]], software = "PD", CV_RT_th_hold = CV_RT_th_hold, CV_LFQ_Pep_th_hold = CV_LFQ_Pep_th_hold,  CV_LFQ_PG_th_hold =  CV_LFQ_PG_th_hold)
      names(output_list)[i] <- input_list[[i]][["filename"]]
      next
    } else if (input_list[[i]][["software"]] == "Generic") {
      output_list[[i]] <- generate_summary_Report(input_df = input_list[[i]][["data"]][["Generic"]], analysis_name = input_list[[i]][["filename"]], software = "Generic", CV_RT_th_hold = CV_RT_th_hold, CV_LFQ_Pep_th_hold = CV_LFQ_Pep_th_hold,  CV_LFQ_PG_th_hold =  CV_LFQ_PG_th_hold)
      names(output_list)[i] <- input_list[[i]][["filename"]]
      next
    }
  }

  output_df <- bind_rows(output_list)

  return(output_df)
}