Abstract
Microbiome composition data collected through amplicon sequencing are count data on taxa in which the total count per sample (the library size) is an artefact of the sequencing platform, and as a result, such data are compositional. To avoid library size dependency, one common way of analysing multivariate compositional data is to perform a principal component analysis (PCA) on data transformed with the centred log‐ratio, hereafter called a log‐ratio PCA. Two aspects typical of amplicon sequencing data are the large differences in library size and the large number of zeroes. In this study, we show on real data and by simulation that, applied to data that combine these two aspects, log‐ratio PCA is nevertheless heavily dependent on the library size. This leads to a reduction in power when testing against any explanatory variable in log‐ratio redundancy analysis. If there is additionally a correlation between the library size and the explanatory variable, then the type 1 error becomes inflated. We explore putative solutions to this problem.
Highlights
Microbiome composition data collected through amplicon sequencing are count data on taxa in which the total count per sample is a technical, ill-understood artefact, which carries no biological information, and as a result, such data are compositional
Some people have advocated the use of compositional data analyses in analysing such data (Gloor et al, 2017; Tsilimigras & Fodor, 2016). This implies transforming the data with the centred log-ratio transformation followed by a standard least-squares method such as principal component analysis (PCA)
Two aspects typical for amplicon sequencing data complicate the use of log-ratio PCA: the high amount of zeroes combined with a large variability in the library size
Summary
Microbiome composition data collected through amplicon sequencing are count data on taxa in which the total count per sample (the library size) is a technical, ill-understood artefact, which carries no biological information, and as a result, such data are compositional. Some people have advocated the use of compositional data analyses in analysing such data (Gloor et al, 2017; Tsilimigras & Fodor, 2016) For multivariate analysis, this implies transforming the data with the centred log-ratio transformation (clr) followed by a standard least-squares method such as principal component analysis (PCA). The data (counts or proportions) are logarithmically transformed and double-centred, followed by a PCA. This is often called log-ratio PCA or log-ratio analysis (Aitchison, 1983; Greenacre, 2018). Two aspects typical for amplicon sequencing data complicate the use of log-ratio PCA: the high amount of zeroes combined with a large variability in the library size.
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