dot-biplot: Plot PCA results as biplot, scree and matrix plots

In a3609640/eIF4F.analysis: A R package to analyze eIF4F subunits in human cancers

Plot PCA results as biplot, scree and matrix plots

Description

A helper function draws biplot, screen and matrix plots for PCA results

Usage

.biplot(res.pca, df, sample_type, column_name, color, folder)

Arguments

res.pca	PCA results from selected RNAseq data
df	selected RNAseq data with sample type annotation
sample_type	selected sample types for plot label
column_name	column name of annotation of primary diseases or healthy tissues for color of individuals
color	color scheme used for individual sample on the PCA
folder	sub directory name to store the output files

Details

This function

• uses RNAseq data generated from .get_df_subset() for PCA

• calls .biplot_title() to generate plot title using the input argument sample_type

It should not be used directly, only inside .plot_PCA_TCGA_GTEX(), .plot_PCA_TCGA_GTEX_tumor(), or .plot_PCA_CPTAC_LUAD() function.

Side effects:

(1) PCA biplots (PCA score plot + loading plot) on screen and as pdf files: PCA score plot shows the clusters of samples based on their similarity and loading plot shows how strongly each characteristic influences a principal component. (2) matrix plots on screen and as pdf files to show the quality of representation of the variables. (3) scree plots on screen and as pdf files to display how much variation each principal component captures from the data.

See Also

Other helper function for PCA plotting: .biplot_title(), .selected_biplot()

Examples

## Not run: 
.biplot(
  res.pca = .RNAseq_PCA(df[[1]], 10), df = df[[2]],
  sample_type = "Metastatic Tumor (TCGA)",
  y = "primary.disease", color = col_vector, folder = "TCGA"
)

## End(Not run)

a3609640/eIF4F.analysis documentation built on Jan. 2, 2023, 11:19 p.m.