Stochastic variational variable selection for high-dimensional microbiome data.
Tung DangKie KumaishiErika UsuiShungo KoboriTakumi SatoYusuke TodaYuji YamasakiHisashi TsujimotoYasunori IchihashiHiroyoshi IwataPublished in: Microbiome (2022)
SVVS demonstrates a better performance and significantly faster computation than those of the existing methods in all cases of testing datasets. In particular, SVVS is the only method that can analyze massive high-dimensional microbial data with more than 50,000 microbial species and 1000 samples. Furthermore, a core set of representative microbial species is identified using SVVS that can improve the interpretability of Bayesian mixture models for a wide range of microbiome studies. Video Abstract.