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Data Completeness Report

get_DC_Report.Rd

Generates a data completeness report from precursor to proteingroup-level

Usage

get_DC_Report(input_list, metric = c("absolute", "percentage"))

Arguments

input_list

A list with data frames and respective level information.

metric

"absolute" for absolute numbers or "percentage" for displaying percentages. Default is absolute.

Value

This function returns a list. For each analysis a respective data frame including missing value information per level is stored in the generated list.

  • Analysis - analysis name.

  • Nr.Missing.Values - number of missing values.

  • Precursor.IDs - number of precursor identification per missing value entry - absolute or in percentage.

  • Peptide.IDs - number of peptide identification per missing value entry - absolute or in percentage.

  • Protein.IDs - number of protein identification per missing value entry - absolute or in percentage.

  • ProteinGroup.IDs - number of proteingroup identification per missing value entry - absolute or in percentage.

  • Profile - categorical entries: "unique", "sparse", "shared with at least 50%" or "complete".

Details

For each submitted data a data completeness report is generated highlighting missing values on precursor-, peptide-, protein- and proteingroup-level.

Author

Oliver Kardell

Examples

# Load libraries
library(tibble)
library(stringr)

# Example data
data <- list(
DIANN = list(
  filename = "B",
  software = "DIA-NN",
  data = list(
     "DIA-NN" = tibble::tibble(
        Run_mpwR = rep(c("A","B"), times = 10),
        Precursor.IDs_mpwR = rep(c("A2", "A3", "B2", "B3", "C1"), each = 4),
        Protein.IDs_mpwR = rep(c("A2", "A3", "B2", "B3", "C1"), each = 4),
        Peptide.IDs_mpwR = rep(c("A", "A", "B", "B", "C"), each = 4),
        ProteinGroup.IDs_mpwR = rep(c("A2", "A3", "B2", "B3", "C1"), each = 4)
     )
  )
)
)

# Result
output <- get_DC_Report(
  input_list = data,
  metric = "absolute"
)