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

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

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 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".

Value

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 )
#> # A tibble: 2 × 3 #> condition median_cv median_cv_combined #> <chr> <dbl> <dbl> #> 1 condition_2 6.06 7.49 #> 2 condition_1 6.07 7.49
# 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" )