Show the missing value distributation.
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,
...
)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?
- ...
Other parameters for ComplexHeatmap::Heatmap
Value
A ggplot2 class object
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
#> --------------------
#> massdataset version: 1.0.12
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information (extract_process_info())
#> 1 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2022-08-07 19:33:12
##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)
