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Perform and plot a 3D Principal Components Analysis

Source: R/plot_pca.R
plot_pca_3d.Rd

Perform and plot a 3D Principal Components Analysis

3D PCA for counts dataframe

Usage

plot_pca_3d(
  moo_counts,
  count_type = NULL,
  sub_count_type = NULL,
  sample_metadata = NULL,
  feature_id_colname = NULL,
  sample_id_colname = NULL,
  samples_to_rename = NULL,
  group_colname = "Group",
  label_colname = "Label",
  principal_components = c(1, 2, 3),
  point_size = 8,
  label_font_size = 24,
  color_values = c("#5954d6", "#e1562c", "#b80058", "#00c6f8", "#d163e6", "#00a76c",
    "#ff9287", "#008cf9", "#006e00", "#796880", "#FFA500", "#878500"),
  plot_title = "PCA 3D",
  plot_filename = "pca_3D.html",
  print_plots = options::opt("print_plots"),
  save_plots = options::opt("save_plots"),
  plots_subdir = "pca"
)

## S7 method for class <MOSuite::multiOmicDataSet>
plot_pca_3d(
  moo_counts,
  count_type = NULL,
  sub_count_type = NULL,
  sample_metadata = NULL,
  feature_id_colname = NULL,
  sample_id_colname = NULL,
  samples_to_rename = NULL,
  group_colname = "Group",
  label_colname = "Label",
  principal_components = c(1, 2, 3),
  point_size = 8,
  label_font_size = 24,
  color_values = c("#5954d6", "#e1562c", "#b80058", "#00c6f8", "#d163e6", "#00a76c",
    "#ff9287", "#008cf9", "#006e00", "#796880", "#FFA500", "#878500"),
  plot_title = "PCA 3D",
  plot_filename = "pca_3D.html",
  print_plots = options::opt("print_plots"),
  save_plots = options::opt("save_plots"),
  plots_subdir = "pca"
)

## S7 method for class <data.frame>
plot_pca_3d(
  moo_counts,
  count_type = NULL,
  sub_count_type = NULL,
  sample_metadata = NULL,
  feature_id_colname = NULL,
  sample_id_colname = NULL,
  samples_to_rename = NULL,
  group_colname = "Group",
  label_colname = "Label",
  principal_components = c(1, 2, 3),
  point_size = 8,
  label_font_size = 24,
  color_values = c("#5954d6", "#e1562c", "#b80058", "#00c6f8", "#d163e6", "#00a76c",
    "#ff9287", "#008cf9", "#006e00", "#796880", "#FFA500", "#878500"),
  plot_title = "PCA 3D",
  plot_filename = "pca_3D.html",
  print_plots = options::opt("print_plots"),
  save_plots = options::opt("save_plots"),
  plots_subdir = "pca"
)

Arguments

moo_counts

counts dataframe

count_type

the type of counts to use. Ignored when moo_counts is already a dataframe.

sub_count_type

used if count_type is a list in the counts slot: specify the sub count type within the list.

sample_metadata

sample metadata as a data frame or tibble.

feature_id_colname

The column from the counts data containing feature IDs. If NULL, first column is used.

sample_id_colname

The column from sample metadata containing sample names. If NULL, first column is used.

samples_to_rename

optional named mapping in old_name: new_name format for display labels.

group_colname

The column from sample metadata containing sample group information.

label_colname

The column from sample metadata containing sample labels.

principal_components

vector with numbered principal components to plot

point_size

size for ggplot2::geom_point()

label_font_size

font size used for labels in the interactive figure.

color_values

vector of colors as hex values or names recognized by R.

plot_title

title for the plot

plot_filename

output filename when saving plots.

print_plots

whether to print plot to the active graphics device.

save_plots

whether to save plot to disk.

plots_subdir

output subdirectory for saved plots.

Value

plotly::plot_ly figure

See also

Other PCA functions: calc_pca(), plot_pca(), plot_pca_2d()