Jun Chen

BMDD improves microbiome data imputation by modeling bimodal abundance distributions, outperforming existing methods in accuracy and uncertainty estimation.

The paper identifies distinct gut microbiome signatures during acute SARS-CoV-2 infection that predict long COVID, showing microbiome profiles better predict risk than clinical variables.

scMayoMap provides a user-friendly tool for accurate cell type annotation in scRNA-seq data, outperforming existing methods across multiple datasets.

This study benchmarks differential abundance analysis methods for correlated microbiome data, finding linear model-based methods more robust than generalized linear model-based ones.

This paper introduces ConQuR, a method to remove batch effects in microbiome data by using conditional quantile regression, demonstrating its effectiveness in preserving signals and improving analysis.

GMPR is a normalization method for microbiome sequencing data that addresses zero-inflation, outperforming existing methods in simulations and real datasets.