multiple_pca_plot: Plots multiple components of a PCA

In martahidalgo/hipathia: HiPathia: High-throughput Pathway Analysis

Plots multiple components of a PCA

Description

Plots multiple components of a PCA analysis computed with do_pca

Usage

multiple_pca_plot(
  fit,
  group = NULL,
  sample_colors = NULL,
  comps = seq_len(3),
  plot_variance = FALSE,
  legend = TRUE,
  cex = 2,
  pch = 20,
  main = "Multiple PCA plot",
  save_png = NULL
)

Arguments

fit	princomp object as returned by do_pca
group	Vector with the group to which each sample belongs. The samples must be ordered as in path_vals. By default, all samples will be assigned to the same class.
sample_colors	Named character vector of colors. The names of the colors must be the classes in group. Each sample will be assigned the color corresponding to its class, taken from the group vector. By default a color will be assigned automatically to each class.
comps	Vector with the components to be plot
plot_variance	Logical, whether to plot the cumulative variance.
legend	Boolean, whether to plot a legend in the plot. Default is TRUE.
cex	Graphical parameter from par() function.
pch	Graphical parameter from par() function.
main	Main title of the image
save_png	Path to the file where the image as PNG will be saved. By default, the image is not saved.

Value

Plots multiple components of a PCA

Examples

data(path_vals)
sample_group <- brca_design[colnames(path_vals),"group"]
pca_model <- do_pca(path_vals[seq_len(ncol(path_vals)),])
multiple_pca_plot(pca_model, sample_group, cex = 3, plot_variance = TRUE)

martahidalgo/hipathia documentation built on Jan. 12, 2023, 1:44 p.m.