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Glen A. Satten

Centers for Disease Control and Prevention · US
Area of research
Statistics and Probability · Genetics
Research interest
Research interests include Statistical Methods and Inference, Statistical Methods and Bayesian Inference, Genetic Associations and Epidemiology, and Gut microbiota and health.
h-index
52
citations
18,918
works
221
NIH funding
primary concept
email

Recent publications

DTH: A nonparametric test for homogeneity of multivariate dispersions.
2026cited by 0position: contributordoi
MIDASim: a fast and simple simulator for realistic microbiome data
Microbiome 2024cited by 17position: lastdoi
Impact of Experimental Bias on Compositional Analysis of Microbiome Data.
2023cited by 0position: contributordoi
Integrative analysis of microbial 16S gene and shotgun metagenomic sequencing data improves statistical efficiency
2023cited by 0position: contributordoi
Ranked severe maternal morbidity index for population-level surveillance at delivery hospitalization based on hospital discharge data
2023cited by 0position: contributordoi
LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control
Proceedings of the National Academy of Sciences 2022cited by 51position: middledoi
LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control.
2022cited by 42position: contributordoi
A rarefaction-without-resampling extension of PERMANOVA for testing presence–absence associations in the microbiome
Bioinformatics 2022cited by 20position: lastdoi
Testing microbiome associations with survival times at both the community and individual taxon levels.
2022cited by 9position: contributordoi
Associations between microbial communities and key chemical constituents in U.S. domestic moist snuff.
2022cited by 5position: contributordoi
Efficient estimation of indirect effects in case-control studies using a unified likelihood framework.
2022cited by 4position: contributordoi
What Can We Learn about the Bias of Microbiome Studies from Analyzing Data from Mock Communities?
2022cited by 2position: contributordoi
Vaginal Microbiome Composition in Early Pregnancy and Risk of Spontaneous Preterm and Early Term Birth Among African American Women
Frontiers in Cellular and Infection Microbiology 2021cited by 87position: middledoi
Constraining PERMANOVA and LDM to within-set comparisons by projection improves the efficiency of analyses of matched sets of microbiome data
Microbiome 2021cited by 33position: middledoi
A rarefaction-based extension of the LDM for testing presence–absence associations in the microbiome
Bioinformatics 2021cited by 26position: lastdoi
A rarefaction-based extension of the LDM for testing presence-absence associations in the microbiome.
2021cited by 20position: contributordoi
LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control
2021cited by 1position: contributordoi
Testing hypotheses about the microbiome using the linear decomposition model (LDM)
Bioinformatics 2020cited by 113position: lastdoi
Stability of the vaginal, oral, and gut microbiota across pregnancy among African American women: the effect of socioeconomic status and antibiotic exposure
PeerJ 2019cited by 56position: middledoi
Changes in vaginal community state types reflect major shifts in the microbiome
Microbial Ecology in Health and Disease 2017cited by 111position: middledoi
PhredEM: a phred-score-informed genotype-calling approach for next-generation sequencing studies
Genetic Epidemiology 2017cited by 62position: middledoi
Dysbiosis, inflammation, and response to treatment: a longitudinal study of pediatric subjects with newly diagnosed inflammatory bowel disease
Genome Medicine 2016cited by 283position: middledoi
Testing Rare-Variant Association without Calling Genotypes Allows for Systematic Differences in Sequencing between Cases and Controls
PLoS Genetics 2016cited by 35position: lastdoi
A Permutation Procedure to Correct for Confounders in Case-Control Studies, Including Tests of Rare Variation
The American Journal of Human Genetics 2012cited by 67position: lastdoi

Grants

No grants ingested yet.

Frequent collaborators

· 11 papers (2021–2026)Yi‐Juan Hu · Peking University8 papers (2016–2022)Yi-Juan Hu · Peking University5 papers (2021–2023)Michelle L. Wright · National Institutes of Health3 papers (2017–2021)Alicia K. Smith · Emory University2 papers (2019–2021)Anna K. Knight · Emory Healthcare2 papers (2019–2021)Ni Zhao · Fu Wai Hospital2 papers (2022–2026)Cherie C. Hill · Emory University2 papers (2019–2021)Andrew S. Allen · Duke University2 papers (2012–2016)Elizabeth J. Corwin · Columbia University2 papers (2019–2021)Timothy D. Read · Emory University2 papers (2019–2021)Jennifer G. Mullé · Rutgers, The State University of New Jersey2 papers (2016–2019)Peizhou Liao · Emory University2 papers (2016–2017)Anne L. Dunlop · Emory University2 papers (2019–2021)Cary G. Sauer · Emory University1 papers (2016–2016) · 1 papers (2017–2017)Pankaj Chopra · Emory University1 papers (2016–2016)Richard Duncan · United Nations Children's Fund1 papers (2012–2012)H. Richard Johnston · Emory University1 papers (2016–2016)Guanhua Chen · University of Hawaiʻi at Mānoa1 papers (2017–2017)