Current practice in the normalization of microbiome count data is inefficient in the statistical
sense. For apparently historical reasons, the common approach is either to use simple�…

Background: Data from 16S amplicon sequencing present challenges to ecological and
statistical interpretation. In particular, library sizes often vary over several ranges of�…

The recent development of cost‐effective high‐throughput DNA sequencing technologies
has tremendously increased microbiome research. However, it has been well documented�…

High-throughput sequencing technologies allow easy characterization of the human
microbiome, but the statistical methods to analyze microbiome data are still in their infancy�…

Background The correct identification of differentially abundant microbial taxa between
experimental conditions is a methodological and computational challenge. Recent work has�…

Differential abundance testing is a critical task in microbiome studies that is complicated by
the sparsity of data matrices. Here we adapt for microbiome studies a solution from the field�…

Differential abundance analysis is at the core of statistical analysis of microbiome data. The
compositional nature of microbiome sequencing data makes false positive control�…

Background Data from 16S ribosomal RNA (rRNA) amplicon sequencing present
challenges to ecological and statistical interpretation. In particular, library sizes often vary�…

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�…

Normalization is the first critical step in microbiome sequencing data analysis used to
account for variable library sizes. Current RNA-Seq based normalization methods that have�…