dot-selected_biplot: Plot subgroups of PCA results as biplots

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

Plot subgroups of PCA results as biplots

Description

A helper function draws biplots of selected PCA results from TCGA and GTEX combined data.

Usage

.selected_biplot(res.pca, df, sample_type, column_name, color)

Arguments

res.pca	PCA results from from TCGA and GTEX combined RNAseq data
df	combined RNAseq data with sample type annotation
sample_type	sample types for plot labeling
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(.TCGA_GTEX_sampletype_subset, "All") 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() 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.

See Also

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

Examples

## Not run: 
.selected_biplot(
  res.pca = .RNAseq_PCA(df[[1]], 10), df = df[[2]],
  x = "Healthy Tissue (GTEx)", y = "sample.type", color = "#D55E00"
)
.selected_biplot(
  res.pca = .RNAseq_PCA(df[[1]], 10), df = df[[2]],
  x = "Healthy Tissue (GTEx)", y = "primary.site", color = col_vector
)

## End(Not run)

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