Key points are not available for this paper at this time.
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.
Li et al. (Sat,) studied this question.