impute_plot: Visualize the impact of imputation

In promor: Proteomics Data Analysis and Modeling Tools

Visualize the impact of imputation

Description

This function generates density plots to visualize the impact of missing data imputation on the data.

Usage

impute_plot(
  original,
  imputed,
  global = TRUE,
  text_size = 10,
  palette = "viridis",
  n_row,
  n_col,
  save = FALSE,
  file_path = NULL,
  file_name = "Impute_plot",
  file_type = "pdf",
  plot_width = 7,
  plot_height = 7,
  dpi = 80
)

Arguments

original	A raw_df object (output of create_df) containing missing values or a norm_df object containing normalized protein intensity data.
imputed	An imp_df object obtained from running impute_na on the same data frame provided as original.
global	Logical. If TRUE (default), a global density plot is produced. If FALSE, sample-wise density plots are produced.
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	Used if global = FALSE to indicate the number of rows to print the plots.
n_col	Used if global = FALSE to indicate the 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 density plot/s. Default is "Impute_plot."
file_type	File type to save the density plot/s. Default is "pdf".
plot_width	Width of the plot. Default is 7.
plot_height	Height of the plot. Default is 7.
dpi	Plot resolution. Default is 80.

Details

• Given two data frames, one with missing values and the other, an imputed data frame (imp_df object) of the same data set, impute_plot generates global or sample-wise density plots to visualize the impact of imputation on the data set.

• Note, when sample-wise option is selected (global = FALSE), n_col and n_row can be used to specify the number of columns and rows to print the plots.

• If you choose to specify n_row and n_col, make sure that n_row * n_col matches the total number of samples in the data frame.

Value

A ggplot2 plot object.

Author(s)

Chathurani Ranathunge

Examples

## 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"
)

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

## Visualize the impact of missing data imputation with a global density
## plot.
impute_plot(original = raw_df, imputed = imp_df)

## Make sample-wise density plots
impute_plot(raw_df, imp_df, global = FALSE)

## Print plots in user-specified numbers of rows and columns
impute_plot(raw_df, imp_df, global = FALSE, n_col = 2, n_row = 3)

promor documentation built on July 26, 2023, 5:39 p.m.