qc_data_completeness: Data completeness

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

Data completeness

Description

Calculates the percentage of data completeness. That means, what percentage of all detected precursors is present in each sample.

Usage

qc_data_completeness(
  data,
  sample,
  grouping,
  intensity,
  digestion = NULL,
  plot = TRUE,
  interactive = FALSE
)

Arguments

data	a data frame containing at least the input variables.
sample	a character column in the data data frame that contains the sample names.
grouping	a character column in the data data frame that contains either precursor or peptide identifiers.
intensity	a numeric column in the data data frame that contains any intensity intensity values that missingness should be determined for.
digestion	optional, a character column in the 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.
plot	a logical value that indicates whether the result should be plotted.
interactive	a logical value that specifies whether the plot should be interactive (default is FALSE).

Value

A bar plot that displays the percentage of data completeness over all samples. If plot = FALSE a data frame is returned. If interactive = TRUE, the plot is interactive.

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
)

protti documentation built on Jan. 22, 2023, 1:11 a.m.