Generalized UniFrac Distances, Distance-Based Multivariate Methods and Feature-Based Univariate Methods for Microbiome Data Analysis
A linear Model-based Permutation Test for Differential Abundance Analy...
Permutational Multivariate Analysis of Variance Using Distance Matrice...
Distance-based Intra-Class Correlation Coefficient
Asymptotic Standard Error of Distance-based Intra-Class Correlation Co...
Bootstrap Standard Error of Distance-based Intra-Class Correlation Coe...
Distance-based Multivariate Analysis of Variance (Analytical P-value C...
Geometric Mean of Pairwise Ratios (GMPR) Normalization for Zero-inflat...
Generalized UniFrac distances for comparing microbial communities.
Permutational Multivariate Analysis of Variance Using Multiple Distanc...
Permutational Multivariate Analysis of Variance Using Multiple Distanc...
Rarefy a Count Table to Equal Sequencing Depth
A Semiparametric Model-based Microbiome Sequencing Data Simulator for ...
A Semiparametric Model-based Microbiome Sequencing Data Simulator for ...
A Plot Function for Visualizing the ZicoSeq Results
A suite of methods for powerful and robust microbiome data analysis including data normalization, data simulation, community-level association testing and differential abundance analysis. It implements generalized UniFrac distances, Geometric Mean of Pairwise Ratios (GMPR) normalization, semiparametric data simulator, distance-based statistical methods, and feature-based statistical methods. The distance-based statistical methods include three extensions of PERMANOVA: (1) PERMANOVA using the Freedman-Lane permutation scheme, (2) PERMANOVA omnibus test using multiple matrices, and (3) analytical approach to approximating PERMANOVA p-value. Feature-based statistical methods include linear model-based methods for differential abundance analysis of zero-inflated high-dimensional compositional data.