TOmicsVis (version 2.0.0)

chord_plot: Chord plot for visualizing the relationships of pathways and genes.

Description

Chord plot is used to visualize complex relationships between samples and genes, as well as between pathways and genes.

Usage

chord_plot(
  data,
  multi_colors = "VividColors",
  color_seed = 10,
  color_alpha = 0.3,
  link_visible = TRUE,
  link_dir = -1,
  link_type = "diffHeight",
  sector_scale = "Origin",
  width_circle = 3,
  dist_name = 3,
  label_dir = "Vertical",
  dist_label = 0.3,
  label_scale = 0.8
)

Value

Plot: chord plot is used to visualize complex relationships between samples and genes, as well as between pathways and genes.

Arguments

data

Dataframe: Shared DEGs of all paired comparisons in all samples expression dataframe of RNA-Seq. (1st-col: Genes, 2nd-col~: Samples).

multi_colors

Character: color palette. Default: "VividColors", options: "VividColors", "RainbowColors".

color_seed

Numeric: rand seed for VividColors. Default: 10.

color_alpha

Numeric: color alpha. Default: 0.50, min: 0.00, max: 1.00.

link_visible

Logical: links visible. Default: TRUE, options: TRUE, FALSE.

link_dir

Numeric: links direction, use with link_type. Default: -1, options: -1, 0, 1, 2.

link_type

Character: links type, use with link_dir. Default: "diffHeight", options: "diffHeight", "arrows".

sector_scale

Character: sector scale method. Default: "Origin", options: "Origin", "Scale".

width_circle

Numeric: outside circle width. Default: 3.0, min: 0.0, max: 10.0.

dist_name

Numeric: the distance of name and circle. Default: 3.0, min: 0.0, max: 10.0.

label_dir

Character: label director. Default: "Vertical", options: "Horizontal", "Vertical".

dist_label

Numeric: the distance of label and circle. Default: 0.3, min: 0.0.

label_scale

Numeric: labels font size sclae. Default: 0.8, min: 0, max: NULL.

Author

benben-miao

Examples

# 1. Library TOmicsVis package
library(TOmicsVis)

# 2. Use example dataset
data(gene_expression2)
head(gene_expression2)

# 3. Default parameters
chord_plot(gene_expression2[1:20,])