Perform and plot a 2D Principal Components Analysis

Usage

plot_pca_2d(
  counts_dat,
  sample_metadata,
  sample_id_colname = NULL,
  feature_id_colname = NULL,
  group_colname = "Group",
  label_colname = "Label",
  samples_to_rename = NULL,
  color_values = c("#5954d6", "#e1562c", "#b80058", "#00c6f8", "#d163e6", "#00a76c",
    "#ff9287", "#008cf9", "#006e00", "#796880", "#FFA500", "#878500"),
  principal_components = c(1, 2),
  legend_position = "top",
  point_size = 1,
  add_label = TRUE,
  label_font_size = 3,
  label_offset_x_ = 2,
  label_offset_y_ = 2,
  interactive_plots = FALSE
)

Arguments

counts_dat: data frame of feature counts (e.g. expected feature counts from RSEM).
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 in the plots produced by this template. 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. (Default: NULL – sample_id_colname will be used.)
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
principal_components: vector with numbered principal components to plot (Default: c(1,2))
legend_position: passed to in legend.position ggplot2::theme()
point_size: size for ggplot2::geom_point()
add_label: whether to add text labels for the points
label_font_size: label font size for the PCA plot
label_offset_x_: label offset x for the PCA plot
label_offset_y_: label offset y for the PCA plot
interactive_plots: set to TRUE to make PCA and Histogram plots interactive with plotly, allowing you to hover your mouse over a point or line to view sample information. The similarity heat map will not display if this toggle is set to TRUE. Default is FALSE.

Value

ggplot object

Perform and plot a 2D Principal Components Analysis

Usage

Arguments

Value

See also