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Visualize missing data

Source: R/handleNA.R
heatmap_na.Rd

This function visualizes the patterns of missing value occurrence using a heatmap.

Usage

heatmap_na(
  raw_df,
  protein_range,
  sample_range,
  reorder_x = FALSE,
  reorder_y = FALSE,
  x_fun = mean,
  y_fun = mean,
  palette = "viridis",
  label_proteins = FALSE,
  text_size = 10,
  save = FALSE,
  file_type = "pdf",
  file_path = NULL,
  file_name = "Missing_data_heatmap",
  plot_width = 15,
  plot_height = 15,
  dpi = 80
)

Arguments

raw_df

A raw_df object (output from create_df).

protein_range

The range or subset of proteins (rows) to plot. If not provided, all the proteins (rows) in the data frame will be used.

sample_range

The range of samples to plot. If not provided, all the samples (columns) in the data frame will be used.

reorder_x

Logical. If TRUE samples on the x axis are reordered using the function given in x_fun. Default is FALSE.

reorder_y

Logical. If TRUE proteins in the y axis are reordered using the function given in y_fun. Default is FALSE.

x_fun

Function to reorder samples along the x axis. Possible options are mean and sum. Default is mean.

y_fun

Function to reorder proteins along the y axis. Possible options are mean and sum. Default is mean.

palette

Viridis color palette option for plots. Default is "viridis". See viridis for available options.

label_proteins

If TRUE proteins on the y axis will be labeled with their Majority Protein IDs. Default is FALSE.

text_size

Text size for axis labels. Default is 10.

save

Logical. If TRUE saves a copy of the plot in the directory provided in file_path.

file_type

File type to save the heatmap. Default is "pdf".

file_path

A string containing the directory path to save the file.

file_name

File name to save the heatmap. Default is "Missing_data_heatmap".

plot_width

Width of the plot. Default is 15.

plot_height

Height of the plot. Default is 15.

dpi

Plot resolution. Default is 80.

Value

A ggplot2 plot object.

Details

This function visualizes patterns of missing value occurrence using a heatmap. The user can choose to reorder the axes using the available functions (x_fun, y_fun) to better understand the underlying cause of missing data.

See also

create_df

Author

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"
)
#> 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.

## Missing data heatmap with default settings.
heatmap_na(raw_df)


## Missing data heatmap with x and y axes reordered by the mean (default) of
## protein intensity.
heatmap_na(raw_df,
  reorder_x = TRUE, reorder_y = TRUE
)


## Missing data heatmap with x and y axes reordered by the sum of
## protein intensity.
heatmap_na(raw_df,
  reorder_x = TRUE, reorder_y = TRUE, x_fun = sum,
  y_fun = sum
)


## Missing data heatmap for a subset of the proteins with x and y axes
## reordered by the mean (default) of protein intensity and the y axis
## labeled with protein IDs.
heatmap_na(raw_df,
  protein_range = 1:30,
  reorder_x = TRUE, reorder_y = TRUE,
  label_proteins = TRUE
)