ordination_plot: Ordination plot

In OmicFlow: Fast and Efficient (Automated) Analysis of Sparse Omics Data

Ordination plot

Description

Creates an ordination plot pre-computed principal components from wcmdscale. This function is built into the class omics with method ordination() and inherited by other omics classes, such as; metagenomics and proteomics.

Usage

ordination_plot(
  data,
  col_name,
  pair,
  dist_explained = NULL,
  dist_metric = NULL
)

Arguments

data	A data.frame or data.table of Principal Components as columns and rows as loading scores.
col_name	A categorical variable to color the contrasts (e.g. "groups").
pair	A vector of character variables indicating what dimension names (e.g. PC1, NMDS2).
dist_explained	A vector of numeric values of the percentage dissimilarity explained for the dimension pairs, default is NULL.
dist_metric	A character variable indicating what metric is used (e.g. unifrac, bray-curtis), default is NULL.

Value

A ggplot2 object to be further modified

Examples

library(ggplot2)

# Mock principal component scores
set.seed(123)
mock_data <- data.frame(
  SampleID = paste0("Sample", 1:10),
  PC1 = rnorm(10, mean = 0, sd = 1),
  PC2 = rnorm(10, mean = 0, sd = 1),
  groups = rep(c("Group1", "Group2"), each = 5)
)

# Basic usage
ordination_plot(
  data = mock_data,
  col_name = "groups",
  pair = c("PC1", "PC2")
)

# Adding variance/dissimilarity explained.
ordination_plot(
  data = mock_data,
  col_name = "groups",
  pair = c("PC1", "PC2"),
  dist_explained = c(45, 22),
  dist_metric = "bray-curtis"
)

OmicFlow documentation built on Sept. 9, 2025, 5:24 p.m.