Rarefy

GUniFrac is also sensitive to different sequencing depth. To compare
microbiomes on an equal basis, rarefaction might be used.

 normalization

 Microbiome

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.

Jun Chen

GUniFrac

Generalized UniFrac Distances, Distance-Based Multivariate
Methods and Feature-Based Univariate Methods for Microbiome
Data Analysis

Rarefy function

<dl> <dt>otu.tab</dt>
<dd>OTU count table, row - n sample, column - q OTU</dd> <dt>depth</dt>
<dd>required sequencing depth; If not specified, the lowest sequencing
depth is used.</dd></dl>

Arguments

Jun Chen &lt;chen.jun2@mayo.edu&gt;

Author

Rarefy a Count Table to Equal Sequencing Depth — Rarefy

<dl>

 <dt>otu.tab</dt>
<dd>OTU count table, row - n sample, column - q OTU</dd>

 <dt>depth</dt>
<dd>required sequencing depth; If not specified, the lowest sequencing
depth is used.</dd>

</dl>

Rarefy: Rarefy a Count Table to Equal Sequencing Depth

Description

Usage

Value

Arguments

Author

References

Examples