The S-U algorithm for missing data problems. - Glen A. Satten; Somnath Datta (Comput. Stat., 2000)

Marginal Analyses of Multistage Data. - Glen A. Satten; Somnath Datta (Advances in Survival Analysis, 2003)

An empirical bayes adjustment to increase the sensitivity of detecting differentially expressed genes in microarray experiments. - Susmita Datta; Glen A. Satten; Dale J. Benos; Jiazeng Xia; Martin J. Heslin; Somnath Datta (Bioinform., 2004)

Standardization and denoising algorithms for mass spectra to classify whole-organism bacterial specimens. - Glen A. Satten; Somnath Datta; Hercules Moura; Adrian R. Woolfitt; Maria da G. Carvalho; George M. Carlone; Barun K. De; Antonis Pavlopoulos; John R. Barr 002 (Bioinform., 2004)

Robust inference of population structure from next-generation sequencing data with systematic differences in sequencing. - Peizhou Liao; Glen A. Satten; Yi-Juan Hu (Bioinform., 2018)

Testing hypotheses about the microbiome using the linear decomposition model (LDM). - Yi-Juan Hu; Glen A. Satten (Bioinform., 2020)

A rarefaction-based extension of the LDM for testing presence-absence associations in the microbiome. - Yi-Juan Hu; Andrea Lane; Glen A. Satten (Bioinform., 2021)

A rarefaction-without-resampling extension of PERMANOVA for testing presence-absence associations in the microbiome. - Yi-Juan Hu; Glen A. Satten (Bioinform., 2022)

Integrative analysis of relative abundance data and presence-absence data of the microbiome using the LDM. - Zhengyi Zhu; Glen A. Satten; Yi-Juan Hu (Bioinform., 2022)

Testing microbiome associations with survival times at both the community and individual taxon levels. - Yingtian Hu; Yunxiao Li; Glen A. Satten; Yi-Juan Hu (PLoS Comput. Biol., 2022)

Compositional analysis of microbiome data using the linear decomposition model (LDM). - Yi-Juan Hu; Glen A. Satten (Bioinform., 2023)