protti (version 0.9.0)

qc_peptide_type: Check peptide type percentage share

Description

Calculates the percentage share of each peptide types (fully-tryptic, semi-tryptic, non-tryptic) for each sample.

Usage

qc_peptide_type(
  data,
  sample,
  peptide,
  pep_type,
  intensity,
  remove_na_intensities = TRUE,
  method = "count",
  plot = FALSE,
  interactive = FALSE
)

Value

A data frame that contains the calculated percentage shares of each peptide type per sample. The count column contains the number of peptides with a specific type. The peptide_type_percent column contains the percentage share of a specific peptide type.

Arguments

data

a data frame that contains at least the input columns.

sample

a character or factor column in the data data frame that contains the sample names.

peptide

a character column in the data data frame that contains the peptide sequence.

pep_type

a character column in the data data frame that contains the peptide type. Can be obtained using the find_peptide and assign_peptide_type function together.

intensity

a numeric column in the data data frame that contains the corresponding raw or normalised intensity values (not log2) for each peptide or precursor. Required when "intensity" is chosen as the method.

remove_na_intensities

a logical value that specifies if sample/peptide combinations with intensities that are NA (not quantified IDs) should be dropped from the data frame for analysis of peptide type distributions. Default is TRUE since we are usually interested in the peptide type distribution of quantifiable IDs. This is only relevant for method = "count".

method

a character value that indicates the method used for evaluation. method = "intensity" calculates the peptide type percentage by intensity, whereas method = "count" calculates the percentage by peptide ID count. Default is method = count.

plot

a logical value that indicates whether the result should be plotted.

interactive

a logical value that indicates whether the plot should be interactive.

Examples

# Load libraries
library(dplyr)

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"
) %>%
  mutate(intensity_non_log2 = 2^peptide_intensity_missing)

# Determine peptide type percentages
qc_peptide_type(
  data = data,
  sample = sample,
  peptide = peptide,
  pep_type = pep_type,
  intensity = intensity_non_log2,
  method = "intensity",
  plot = FALSE
)

# Plot peptide type
qc_peptide_type(
  data = data,
  sample = sample,
  peptide = peptide,
  pep_type = pep_type,
  intensity = intensity_non_log2,
  method = "intensity",
  plot = TRUE
)