Correlation and association analyses are one of the most widely used statistical methods in
research fields, including microbiome and integrative multiomics studies. Correlation and …

The multi‐batch reanalysis approach of jointly reevaluating gene/genome sequences from
different works has gained particular relevance in the literature in recent years. The large …

Background Differential abundance analysis (DAA) is one central statistical task in
microbiome data analysis. A robust and powerful DAA tool can help identify highly confident …

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

Motivation Research supports the potential use of microbiome as a predictor of some
diseases. Motivated by the findings that microbiome data is complex in nature, and there is …

A critical challenge in microbiome data analysis is the existence of many non-biological
zeros, which distort taxon abundance distributions, complicate data analysis, and jeopardize …

Modern high-throughput sequencing technologies provide low-cost microbiome survey data
across all habitats of life at unprecedented scale. At the most granular level, the primary data …

Microbiome data consists of operational taxonomic unit (OTU) counts characterized by zero-
inflation, over-dispersion, and grouping structure among samples. Currently, statistical …

In biomedical applications of machine learning, relevant information often has a rich
structure that is not easily encoded as real-valued predictors. Examples of such data include …

Background The ability to quantitatively predict ecophysiological functions of microbial
communities provides an important step to engineer microbiota for desired functions related …