Visualize the effect of normalization

This function visualizes the impact of normalization on the data

Usage

norm_plot(
  original,
  normalized,
  type = "box",
  text_size = 10,
  palette = "viridis",
  save = FALSE,
  file_path = NULL,
  file_name = "Norm_plot",
  file_type = "pdf",
  dpi = 80,
  plot_width = 10,
  plot_height = 7
)

Arguments

original: A raw_df object (output of create_df) containing missing values, or an imp_df object after imputing the missing values with impute_na.
normalized: A norm_df object after normalizing the data frame provided as original using normalize_data.
type: Type of plot to generate. Choices are "box" or "density." Default is "box."
text_size: Text size for plot labels, axis labels etc. Default is 10.
palette: Viridis color palette option for plots. Default is "viridis". See viridis for available options.
save: Logical. If TRUE saves a copy of the plot in the directory provided in file_path.
file_path: A string containing the directory path to save the file.
file_name: File name to save the plot. Default is "Norm_plot."
file_type: File type to save the plot. Default is "pdf".
dpi: Plot resolution. Default is 80.
plot_width: Width of the plot. Default is 10.
plot_height: Height of the plot. Default is 7.

Value

A ggplot2 plot object.

Details

Given two data frames, one with data prior to normalization (original), and the other, after normalization (normalized), norm_plot generates side-by-side plots to visualize the effect of normalization on the protein intensity data.

Author

Chathurani Ranathunge

Examples

# \donttest{
## Generate a raw_df object with default settings. No technical replicates.
raw_df <- create_df(
  prot_groups = "https://raw.githubusercontent.com/caranathunge/promor_example_data/main/pg1.txt",
  exp_design = "https://raw.githubusercontent.com/caranathunge/promor_example_data/main/ed1.txt"
)
#> 0 empty row(s) removed.
#> 0 empty column(s) removed.
#> 80 protein(s) (rows) only identified by site removed.
#> 65 reverse protein(s) (rows) removed.
#> 42 protein potential contaminant(s) (rows) removed.
#> 1923 protein(s) identified by 2 or fewer unique peptides removed.
#> Zeros have been replaced with NAs.
#> Data have been log-transformed.

## Impute missing values in the data frame using the default minProb
## method.
imp_df <- impute_na(raw_df)

## Normalize the imp_df object using the default quantile method
norm_df <- normalize_data(imp_df)

## Visualize normalization using box plots
norm_plot(original = imp_df, normalized = norm_df)
#> Warning: argument is not numeric or logical: returning NA
#> Warning: argument is not numeric or logical: returning NA
#> Warning: argument is not numeric or logical: returning NA
#> Warning: argument is not numeric or logical: returning NA
#> Warning: argument is not numeric or logical: returning NA
#> Warning: argument is not numeric or logical: returning NA
#> Warning: argument is not numeric or logical: returning NA
#> Warning: argument is not numeric or logical: returning NA
#> Warning: argument is not numeric or logical: returning NA
#> Warning: argument is not numeric or logical: returning NA
#> Warning: argument is not numeric or logical: returning NA
#> Warning: argument is not numeric or logical: returning NA


## Visualize normalization using density plots
norm_plot(imp_df, norm_df, type = "density")

# }