OmicFlow (version 1.5.0)

manhattan: Compute Manhattan Dissimilarity from a Dense or Sparse Matrix.

Description

Calculates the Manhattan dissimilarity of a Matrix pairwise for each column.

Usage

manhattan(x, weighted = TRUE, threads = 1)

Value

A column x column dist object.

Arguments

x

A matrix, sparseMatrix or Matrix.

weighted

A boolean value, to use abundances (weighted = TRUE) or absence/presence (weighted=FALSE) (default: TRUE).

threads

A wholenumber, the number of threads to use in setThreadOptions (default: 1).

Details

The Manhattan dissimilarity between two samples \(A\) and \(B\), each of length \(n\), is defined as:

\(d(A, B) = \sum_{i}^n |A_i - B_i|\)

where \(A_i\) and \(B_i\) are the abundances of the \(i\)-th feature in sample \(A\) and \(B\), respectively. When weighted is set to FALSE, counts are replaced by presence/absence data.

References

Deza, M. M., & Deza, E. (2009). Encyclopedia of Distances. Springer Science & Business Media., 313.

Examples

library("OmicFlow")

metadata_file <- system.file("extdata", "metadata.tsv", package = "OmicFlow")
counts_file <- system.file("extdata", "counts.tsv", package = "OmicFlow")
features_file <- system.file("extdata", "features.tsv", package = "OmicFlow")
tree_file <- system.file("extdata", "tree.newick", package = "OmicFlow")

taxa <- metagenomics$new(
    metaData = metadata_file,
    countData = counts_file,
    featureData = features_file,
    treeData = tree_file
)

taxa$feature_subset(Kingdom == "Bacteria")
taxa$normalize()

manhattan(taxa$countData)