show_missing_values: show_missing_values

In tidymass/massdataset: massdataset

show_missing_values

Description

Show the missing value distributation.

Usage

show_missing_values(
  object,
  show_row_names = FALSE,
  show_column_names = TRUE,
  row_names_gp = gpar(fontsize = 12),
  column_names_gp = gpar(fontsize = 12),
  column_names_rot,
  cell_color = "transparent",
  row_names_side = "right",
  percentage = FALSE,
  sample_na_cutoff = 50,
  variable_na_cutoff = 50,
  only_outlier_samples = FALSE,
  only_outlier_variables = FALSE,
  return_as_ggplot = FALSE,
  ...
)

Arguments

object	(required) mass_dataset class object.
show_row_names	show row names or not. see?ComplexHeatmap::Heatmap
show_column_names	show column names or not. see?ComplexHeatmap::Heatmap
row_names_gp	row names gp, see?ComplexHeatmap
column_names_gp	column names gp, see?ComplexHeatmap
column_names_rot	column names rot see?ComplexHeatmap::Heatmap
cell_color	Cell color.
row_names_side	Row names side. left or right.
percentage	percentage or not.
sample_na_cutoff	Na cutoff for samples.
variable_na_cutoff	Na cutoff for variables
only_outlier_samples	Only show the outlier samples?
only_outlier_variables	Only show the outlier variables?
return_as_ggplot	Return plot as ggplot2 object?
...	Other parameters for ComplexHeatmap::Heatmap

Value

A ggplot2 class object

Author(s)

Xiaotao Shen [e-mail]

Examples

data("expression_data")
data("sample_info")
data("variable_info")

object =
  create_mass_dataset(
    expression_data = expression_data,
    sample_info = sample_info,
    variable_info = variable_info,
  )

object

##show missing values plot
show_missing_values(object)

show_missing_values(object[1:10,], cell_color = "white")

###only show subject samples
object %>%
  activate_mass_dataset(what = "sample_info") %>%
  filter(class == "Subject") %>%
  show_missing_values()

###only show QC samples
object %>%
  activate_mass_dataset(what = "expression_data") %>%
  dplyr::select(contains("QC")) %>%
  show_missing_values()

###only show features with mz < 100
object %>%
  activate_mass_dataset(what = "variable_info") %>%
  dplyr::filter(mz < 100) %>%
  show_missing_values(cell_color = "white",
                      show_row_names = TRUE,
                      row_names_side = "left",
                      percentage = TRUE,
                      sample_na_cutoff = 50,
                      variable_na_cutoff = 20)

tidymass/massdataset documentation built on Sept. 14, 2024, 4:41 p.m.