Glen A. Satten

LOCOM2 improves differential abundance analysis in microbiome studies by controlling error rates and handling challenges like relative abundance data and unbalanced designs.

The paper introduces a non-parametric test for homogeneity of multivariate dispersions, showing it has higher power than existing methods in certain situations.

This paper introduces QuanT, a non-parametric method to identify unmeasured heterogeneity in microbiome data, improving downstream analysis accuracy.

CAFT is a compositional log-linear model for microbiome data with zero cells that improves differential abundance analysis by handling zeros as censored observations and controlling FDR.

MIDASim simulates realistic microbiome data by capturing taxon correlations, sparsity, and compositionality, outperforming existing methods in accuracy and efficiency.

MERIT improves the statistical efficiency of Monte-Carlo hypothesis testing by controlling the Monte-Carlo error rate, outperforming the GH method in sensitivity and specificity.

The paper introduces LDM-clr, an extension of the LDM method for analyzing microbiome data using centered-log-ratio transformed counts, enabling compositional differential abundance analysis with covariates.

This paper evaluates how experimental bias affects microbiome compositional analysis and shows that LOCOM is robust to both main and interaction biases compared to other methods.

This paper introduces Com-2seq, a method that integrates 16S and shotgun metagenomic data to improve statistical efficiency in testing differential abundance.

LOCOM is a logistic regression method for differential abundance testing in microbiome data that controls FDR and is robust to experimental biases.

The paper introduces a non-stochastic PERMANOVA extension for microbiome presence-absence analyses to address library size confounding without rarefaction, showing improved performance over resampling-based methods.

The paper introduces a unified likelihood framework for mediation analysis in case-control studies to improve efficiency of indirect effect estimates by leveraging exposure-mediator relationships.

The paper introduces an omnibus test based on LDM to analyze microbiome data across different scales, improving power for various association mechanisms.

The paper develops new statistical methods to test microbiome associations with censored survival outcomes at both community and individual taxon levels, showing improved performance over existing methods.

The paper extends the LDM to test presence-absence associations in microbiome data while accounting for library size confounding without using rarefaction.

The paper introduces the linear decomposition model (LDM) to unify global and individual OTU hypothesis testing in microbiome studies while controlling FDR and offering flexibility for various analyses.

The paper introduces a strategy for analyzing matched-set microbiome data using PERMANOVA and LDM, showing improved performance in simulations and real-world applications.