GUniFrac: Generalized UniFrac Distances, Distance-Based Multivariate Methods and Feature-Based Univariate Methods for Microbiome Data Analysis (original) (raw)
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.
Version: | 1.8 |
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Depends: | R (≥ 3.5.0) |
Imports: | Rcpp (≥ 0.12.13), vegan, ggplot2, matrixStats, Matrix, ape, parallel, stats, utils, statmod, rmutil, dirmult, MASS, ggrepel, foreach, modeest, inline, methods |
LinkingTo: | Rcpp |
Suggests: | ade4, knitr, markdown, ggpubr |
Published: | 2023-09-14 |
DOI: | 10.32614/CRAN.package.GUniFrac |
Author: | Jun Chen, Xianyang Zhang, Lu Yang, Lujun Zhang |
Maintainer: | Jun Chen <chen.jun2 at mayo.edu> |
License: | GPL-3 |
NeedsCompilation: | yes |
In views: | Phylogenetics |
CRAN checks: | GUniFrac results |
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