pairwise_adonis: Pairwise adonis2 (PERMANOVA) computation

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

Pairwise adonis2 (PERMANOVA) computation

Description

Computes pairwise adonis2, given a distance matrix and a vector of labels. This function is built into the class omics with method ordination() and inherited by other omics classes, such as; metagenomics and proteomics.

Usage

pairwise_adonis(x, groups, p.adjust.method = "bonferroni", perm = 999)

Arguments

x	A distance matrix in the form of dist. Obtained from a dissimilarity metric, in the case of similarity metric please use 1-dist
groups	A character vector (column from a table) of labels.
p.adjust.method	P adjust method see p.adjust
perm	Number of permutations to compare against the null hypothesis of adonis2 (default: perm=999).

Value

A data.frame of

• pairs that are used

• Degrees of freedom (Df)

• Sums of Squares of H_0

• F.Model of H_0

• R2 of H_0

• p value of F^p > F

• p adjusted

See Also

adonis2

Examples

# Create random data
set.seed(42)
mock_data <- matrix(rnorm(15 * 10), nrow = 15, ncol = 10)

# Create euclidean dissimilarity matrix
mock_dist <- dist(mock_data, method = "euclidean")

# Define group labels, should be equal to number of columns and rows to dist
mock_groups <- rep(c("A", "B", "C"), each = 5)

# Compute pairwise adonis (PERMANOVA)
result <- pairwise_adonis(x = mock_dist, 
                          groups = mock_groups, 
                          p.adjust.method = "bonferroni", 
                          perm = 99)

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