Perform and plot a 2D Principal Components Analysis

Usage

plot_pca_2d(
  moo_counts,
  count_type = NULL,
  sub_count_type = NULL,
  sample_metadata = NULL,
  sample_id_colname = NULL,
  feature_id_colname = NULL,
  group_colname = "Group",
  label_colname = "Label",
  samples_to_rename = NULL,
  color_values = NULL,
  principal_components = c(1, 2),
  legend_position = "top",
  point_size = 5,
  legend_font_size = NULL,
  label_font_size = 3,
  label_offset_x_ = 2,
  label_offset_y_ = 2,
  log_transform = FALSE,
  log_transform_pseudocount = 0.5,
  log_transform_base = "ln",
  interactive_plots = FALSE,
  plots_subdir = "pca",
  plot_filename = "pca_2D.png",
  print_plots = options::opt("print_plots"),
  save_plots = options::opt("save_plots"),
  ...
)

## S7 method for class <MOSuite::multiOmicDataSet>
plot_pca_2d(
  moo_counts,
  count_type = NULL,
  sub_count_type = NULL,
  sample_metadata = NULL,
  sample_id_colname = NULL,
  feature_id_colname = NULL,
  group_colname = "Group",
  label_colname = "Label",
  samples_to_rename = NULL,
  color_values = NULL,
  principal_components = c(1, 2),
  legend_position = "top",
  point_size = 5,
  legend_font_size = NULL,
  label_font_size = 3,
  label_offset_x_ = 2,
  label_offset_y_ = 2,
  log_transform = FALSE,
  log_transform_pseudocount = 0.5,
  log_transform_base = "ln",
  interactive_plots = FALSE,
  plots_subdir = "pca",
  plot_filename = "pca_2D.png",
  print_plots = options::opt("print_plots"),
  save_plots = options::opt("save_plots"),
  ...
)

## S7 method for class <data.frame>
plot_pca_2d(
  moo_counts,
  count_type = NULL,
  sub_count_type = NULL,
  sample_metadata = NULL,
  sample_id_colname = NULL,
  feature_id_colname = NULL,
  group_colname = "Group",
  label_colname = "Label",
  samples_to_rename = NULL,
  color_values = NULL,
  principal_components = c(1, 2),
  legend_position = "top",
  point_size = 5,
  legend_font_size = NULL,
  label_font_size = 3,
  label_offset_x_ = 2,
  label_offset_y_ = 2,
  log_transform = FALSE,
  log_transform_pseudocount = 0.5,
  log_transform_base = "ln",
  interactive_plots = FALSE,
  plots_subdir = "pca",
  plot_filename = "pca_2D.png",
  print_plots = options::opt("print_plots"),
  save_plots = options::opt("save_plots"),
  ...
)

Arguments

moo_counts: counts dataframe or multiOmicDataSet containing count_type & sub_count_type in the counts slot
count_type: the type of counts to use when moo_counts is a multiOmicDataSet; ignored for data frame input.
sub_count_type: used when count_type refers to a list of count matrices; ignored for data frame input.
sample_metadata: sample metadata as a data frame or tibble.
sample_id_colname: The column from the sample metadata containing the sample names. The names in this column must exactly match the names used as the sample column names of your input Counts Matrix. (Default: NULL - first column in the sample metadata will be used.)
feature_id_colname: The column from the counts dataa containing the Feature IDs (Usually Gene or Protein ID). This is usually the first column of your input Counts Matrix. Only columns of Text type from your input Counts Matrix will be available to select for this parameter. (Default: NULL - first column in the counts matrix will be used.)
group_colname: The column from the sample metadata containing the sample group information. This is usually a column showing to which experimental treatments each sample belongs (e.g. WildType, Knockout, Tumor, Normal, Before, After, etc.).
label_colname: The column from the sample metadata containing the sample labels as you wish them to appear on the PCA plot. If NULL, no labels are added to PCA points. This can be the same Sample Names Column. However, you may desire different labels to display on your figure (e.g. shorter labels are sometimes preferred on plots). In that case, select the column with your preferred Labels here. The selected column should contain unique names for each sample.
samples_to_rename: If you do not have a Plot Labels Column in your sample metadata table, you can use this parameter to rename samples manually for display on the PCA plot. Use "Add item" to add each additional sample for renaming. Use the following format to describe which old name (in your sample metadata table) you want to rename to which new name: old_name: new_name
color_values: vector of colors as hex values or names recognized by R. Unnamed colors are assigned by factor level order when the grouping column is a factor; otherwise, they follow the order in which groups first appear in the metadata column. Defaults to NULL; when NULL, mosuite_palette is used for data.frame dispatch and stored colors are used for multiOmicDataSet dispatch.
principal_components: vector with numbered principal components to plot
legend_position: passed to in legend.position ggplot2::theme()
point_size: size for ggplot2::geom_point()
legend_font_size: font size for the PCA legend text. If NULL, the size is scaled automatically based on the number and length of legend labels.
label_font_size: font size for text labels on the PCA plot.
label_offset_x_: horizontal offset for text labels on the PCA plot.
label_offset_y_: vertical offset for text labels on the PCA plot.
log_transform: If TRUE, apply log(x + log_transform_pseudocount, base = log_transform_base) to sample count columns before PCA. Use this for count-like data such as raw, clean, filt, or CPM-like counts; leave it FALSE for already normalized/log-scale or batch-corrected values to avoid double transformation.
log_transform_pseudocount: Pseudocount added before log-transforming counts when log_transform is TRUE.
log_transform_base: Logarithm base to use when log_transform is TRUE. Use a numeric value, or "e", "ln", or "natural" for natural log. Default is "ln" to match the original PCA transform.
interactive_plots: set to TRUE to make the PCA plot interactive with plotly.
plots_subdir: subdirectory in figures/ where PCA plots are saved.
plot_filename: output filename for the PCA plot image.
print_plots: Whether to print plots during analysis (Defaults to FALSE, overwritable using option 'moo_print_plots' or environment variable 'MOO_PRINT_PLOTS')
save_plots: Whether to save plots to files during analysis (Defaults to TRUE, overwritable using option 'moo_save_plots' or environment variable 'MOO_SAVE_PLOTS')
...: arguments forwarded to method

Value

ggplot object

Perform and plot a 2D Principal Components Analysis

Usage

Arguments

Value

See also