qc_cvs function

Check CV distribution

Calculates and plots the coefficients of variation for the selected grouping.

qc_cvs(
  data,
  grouping,
  condition,
  intensity,
  plot = TRUE,
  plot_style = "density",
  max_cv = 200
)

Arguments

• data: a data frame containing at least peptide, precursor or protein identifiers, information on conditions and intensity values for each peptide, precursor or protein.

• grouping: a character column in the data data frame that contains the grouping variables (e.g. peptides, precursors or proteins).

• condition: a character or factor column in the data data frame that contains condition information (e.g. "treated" and "control").

• intensity: a numeric column in the data data frame that contains the corresponding raw or untransformed normalised intensity values for each peptide or precursor.

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

• plot_style: a character value that indicates the plotting style. plot_style = "boxplot"

  plots a boxplot, whereas plot_style = "density" plots the CV density distribution. plot_style = "violin" returns a violin plot. Default is plot_style = "density".

• max_cv: a numeric value that specifies the maximum percentage of CVs that should be included in the returned plot. The default value is max_cv = 200.

Returns

Either a data frame with the median CVs in % or a plot showing the distribution of the CVs is returned.

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)

# Calculate coefficients of variation
qc_cvs(
  data = data,
  grouping = peptide,
  condition = condition,
  intensity = intensity_non_log2,
  plot = FALSE
)

# Plot coefficients of variation
# Different plot styles are available
qc_cvs(
  data = data,
  grouping = peptide,
  condition = condition,
  intensity = intensity_non_log2,
  plot = TRUE,
  plot_style = "violin"
)