pca_plot {TOmicsVis}

PCA dimensional reduction visualization for RNA-Seq.

Description

PCA dimensional reduction visualization for RNA-Seq.

Usage

pca_plot(
  sample_gene,
  group_sample,
  xPC = 1,
  yPC = 2,
  multi_shape = TRUE,
  point_size = 5,
  point_alpha = 0.8,
  text_size = 5,
  fill_alpha = 0.05,
  border_alpha = 0,
  sci_fill_color = "Sci_AAAS",
  legend_pos = "right",
  legend_dir = "vertical",
  ggTheme = "theme_light"
)

Arguments

sample_gene

Dataframe: All genes in all samples expression dataframe of RNA-Seq (1st-col: Genes, 2nd-col~: Samples).

group_sample

Dataframe: Samples and groups for gene expression (1st-col: Samples, 2nd-col: Groups).

xPC

Numeric: PC index at x axis. Default: 1, options: 1, 2, 3, ...

yPC

Numeric: PC index at y axis. Default: 2, options: 2, 3, 4, ...

multi_shape

Logical: groups as shapes. Default: TRUE, options: TRUE, FALSE.

point_size

Numeric: PCA plot point size. Default: 5, min: 0.

point_alpha

Numeric: point color alpha. Default: 0.80, min: 0.00, max: 1.00.

text_size

Numeric: PCA plot annotation size. Default: 5, min: 0.

fill_alpha

Numeric: ellipse fill color alpha. Default: 0.10, min: 0.00, max: 1.00.

border_alpha

Numeric: ellipse border color alpha. Default: 0.10, min: 0.00, max: 1.00.

sci_fill_color

Character: ggsci color pallet. Default: "Sci_AAAS", options: "Sci_AAAS", "Sci_NPG", "Sci_Simpsons", "Sci_JAMA", "Sci_GSEA", "Sci_Lancet", "Sci_Futurama", "Sci_JCO", "Sci_NEJM", "Sci_IGV", "Sci_UCSC", "Sci_D3", "Sci_Material".

legend_pos

Character: legend position. Default: "right", options: "none", "left", "right", "bottom", "top".

legend_dir

Character: legend director. Default: "vertical", options: "horizontal", "vertical".

ggTheme

Character: ggplot2 theme. Default: "theme_light", options: "theme_default", "theme_bw", "theme_gray", "theme_light", "theme_linedraw", "theme_dark", "theme_minimal", "theme_classic", "theme_void".

Value

Plot: PCA dimensional reduction visualization for RNA-Seq.

Author(s)

benben-miao

Examples

# 1. Library package TOmicsVis
library(TOmicsVis)

# 2. Load example datasets
data(gene_expression)
head(gene_expression)

data(samples_groups)
head(samples_groups)

# 3. Default parameters
pca_plot(gene_expression, samples_groups)

# 4. Set multi_shape = FALSE
pca_plot(gene_expression, samples_groups, multi_shape = FALSE)

# 5. Set sci_fill_color = "Sci_NPG", fill_alpha = 0.10
pca_plot(gene_expression, samples_groups, sci_fill_color = "Sci_NPG", fill_alpha = 0.10)