Donald T. McKnight1, Roger Huerlimann1, Deborah S. Bower1,2, Lin Schwarzkopf1, Ross A. Alford1, Kyall R. Zenger1
1College of Science and Engineering, James Cook University, Townsville, Queensland, Australia
2School of Environmental and Rural Science, University of New England, Armidale, New South Wales, Australia
Tóm tắt
Abstract
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.
We tested these theoretical predictions via simulations and a real‐world dataset.
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.
Based on our simulations, normalizing via proportions may be superior to other commonly used methods for comparing ecological communities.
