Dataset Information

fast.adonis: a computationally efficient non-parametric multivariate analysis of microbiome data for large-scale studies.

ABSTRACT:

Motivation

Nonparametric multivariate analysis has been widely used to identify variables associated with a dissimilarity matrix and to quantify their contribution. For very large studies ( n≥5000 ) and many explanatory variables, existing software packages (e.g. adonis and adonis2 in vegan) are computationally intensive when conducting sequential multivariate analysis with permutations or bootstrapping. Moreover, for subjects from a complex sampling design, we need to adjust for sampling weights to derive an unbiased estimate.

Results

We implemented an R function fast.adonis to overcome these computational challenges in large-scale studies. fast.adonis generates results consistent with adonis/adonis2 but much faster. For complex sampling studies, fast.adonis integrates sampling weights algebraically to mimic the source population; thus, analysis can be completed very fast without requiring a large amount of memory.

Availability and implementation

fast.adonis is implemented using R and is publicly available at https://github.com/jennylsl/fast.adonis.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

SUBMITTER: Li S

PROVIDER: S-EPMC9710578 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

<i>fast.adonis</i>: a computationally efficient non-parametric multivariate analysis of microbiome data for large-scale studies.

Li Shilan S Vogtmann Emily E Graubard Barry I BI Gail Mitchell H MH Abnet Christian C CC Shi Jianxin J

Bioinformatics advances 20220610 1

<h4>Motivation</h4>Nonparametric multivariate analysis has been widely used to identify variables associated with a dissimilarity matrix and to quantify their contribution. For very large studies ( n ≥ 5000 ) and many explanatory variables, existing software packages (e.g. <i>adonis</i> and <i>adonis2</i> in <i>vegan</i>) are computationally intensive when conducting sequential multivariate analysis with permutations or bootstrapping. Moreover, for subjects from a complex sampling design, we ne ...[more]

PMID: 36704711

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