PCA_plot: Principal component analysis plot

In espors/idepGolem: Integrated Differential Expression and Pathway Analysis

Principal component analysis plot

Description

Draw a PCA plot with designated PCA components on axis

Usage

PCA_plot(
  data,
  sample_info,
  PCAx = 1,
  PCAy = 2,
  selected_color = "Names",
  selected_shape = "Names",
  plots_color_select
)

Arguments

data	Matrix of gene data that has been through pre_process()
sample_info	Matrix of sample information from experiment design file
PCAx	Integer designating the PC to be plotted on the x axis
PCAy	Integer designating the PC to be plotted on on the y axis
selected_color	String designating factor to color points by. Should be one of the design factors from the design file or "Names" as default which automatically detects groups from gene data file
selected_shape	String designating factor to shape points by. Should be one of the design factors from the design file or "Names" as default which automatically detects groups from gene data file
plots_color_select	Vector of colors for plots

Value

A ggplot object as a PCA plot

See Also

PCA_plot_3d() for three-dimensional version

Other PCA functions: MDS_plot(), PCA_Scree(), PCA_biplot(), PCA_plot_3d(), PCAtools_eigencorplot(), pc_factor_correlation(), t_SNE_plot()

Other plots: chr_counts_ggplot(), chr_normalized_ggplot(), cor_plot(), draw_sample_tree(), eda_boxplot(), eda_density(), eda_scatter(), gene_counts_ggplot(), individual_plots(), k_means_elbow(), mean_sd_plot(), rRNA_counts_ggplot(), sd_density(), t_SNE_plot(), total_counts_ggplot()

espors/idepGolem documentation built on Oct. 27, 2024, 4:56 a.m.