promor (version 0.2.1)

feature_plot: Visualize feature (protein) variation among conditions

Description

This function visualizes protein intensity differences among conditions (classes) using box plots or density distribution plots.

Usage

feature_plot(
  model_df,
  type = "box",
  text_size = 10,
  palette = "viridis",
  n_row,
  n_col,
  save = FALSE,
  file_path = NULL,
  file_name = "Feature_plot",
  file_type = "pdf",
  dpi = 80,
  plot_width = 7,
  plot_height = 7
)

Value

A ggplot2 object

Arguments

model_df

A model_df object from performing pre_process.

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.

n_row

Number of rows to print the plots.

n_col

Number of columns to print the plots.

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 "Feature_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 7.

plot_height

Height of the plot. Default is 7.

Author

Chathurani Ranathunge

Details

This function visualizes condition-wise differences in protein intensity using boxplots and/or density plots.

See Also

• pre_process, rem_feature

Examples

## Create a model_df object with default settings.
covid_model_df <- pre_process(covid_fit_df, covid_norm_df)

## Feature variation - box plots
feature_plot(covid_model_df, type = "box", n_row = 4, n_col = 2)

## Density plots
feature_plot(covid_model_df, type = "density")

## Change color palette
feature_plot(covid_model_df, type = "density", n_row = 4, n_col = 2, palette = "rocket")