<i>fast.adonis</i>: a computationally efficient non-parametric multivariate analysis of microbiome data for large-scale studies
Shilan Li, Emily Vogtmann, Barry I. Graubard, Mitchell H. Gail, Christian C. Abnet, Jianxin Shi
Abstract
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.