Data from: Methods for normalizing microbiome data: an ecological perspective

1. Microbiome sequencing data often need to be normalized due to
differences in read depths, and recommendations for microbiome analyses
generally warn against using proportions or rarefying to normalize data
and instead advocate alternatives, such as upper quartile, CSS, edgeR-TMM,
or DESeq-VS. Those recommendations are, however, based on studies that
focused on differential abundance testing and variance standardization,
rather than community-level comparisons (i.e., beta diversity), Also,
standardizing the within-sample variance across samples may suppress
differences in species evenness, potentially distorting community-level
patterns. Furthermore, the recommended methods use log transformations,
which we expect to exaggerate the importance of differences among rare
OTUs, while suppressing the importance of differences among common OTUs.
2. We tested these theoretical predictions via simulations and a
real-world data set. 3. Proportions and rarefying produced more accurate
comparisons among communities and were the only methods that fully
normalized read depths across samples. Additionally, upper quartile, CSS,
edgeR-TMM, and DESeq-VS often masked differences among communities when
common OTUs differed, and they produced false positives when rare OTUs
differed. 4. Based on our simulations, normalizing via proportions may be
superior to other commonly used methods for comparing ecological
communities.