clr: Centred log-ratio transform

In larssnip/midiv: MiDiv-lab bioinformatics

Centred log-ratio transform

Description

Transforms readcount data with Aitchisons transform.

Usage

clr(sample.readcounts, n.pseudo = 1)

Arguments

n.pseudo	number of pseudo-readcounts to add.
sample.readcount	A vector with readcount data for one sample.

Details

This is a standard implementation of the Aitchisons centered log-ratio transform (Aitchison 1986) for compositional data. Readcount data can be seen as compositional data since the total number of readcounts in a sample does not carry any information about the biology, but is simply an effect of sequencing depth. Thus, the information in the data lies in the relative values, not the absolute. By transforming such data with this function, you get data who are better suited for a number of downstream analyses, e.g. typically analyses making use of sum-of-squares type of statistics, like PCA, PLS, ANOVA or clustering with euclidean distances.

The transform does not accept zeros in any cell of the sample.readcounts. To cope with this you add pseudo-counts. By default 1 additional readcount is assigned to all cells in readcount.mat. You may change this value, and it need not be an integer. The rationale behind this is that we *a priori* assumes a uniform distribution of the taxa, and the more pseudo-counts you add, the more weight you give to this prior.

The main reason this takes a vector as input, not a full matrix, is that readcount data are stored in several ways, bot in tables and matrices. Also, having a per-sample oriented implementation makes it straightforward to use this in conjunction with the phyloseq R package, and the function transform_sample_counts

Value

A vector of same size as the input, but with transformed readcounts.

Author(s)

Lars Snipen.

References

Aitchison J. The Statistical Analysis of Compositional Data. London, UK: Chapman & Hall; 1986.

larssnip/midiv documentation built on Jan. 20, 2025, 6:22 p.m.