A unified framework for unconstrained and constrained ordination of microbiome read count data.

ABSTRACT: Explorative visualization techniques provide a first summary of microbiome read count datasets through dimension reduction. A plethora of dimension reduction methods exists, but many of them focus primarily on sample ordination, failing to elucidate the role of the bacterial species. Moreover, implicit but often unrealistic assumptions underlying these methods fail to account for overdispersion and differences in sequencing depth, which are two typical characteristics of sequencing data. We combine log-linear models with a dispersion estimation algorithm and flexible response function modelling into a framework for unconstrained and constrained ordination. The method is able to cope with differences in dispersion between taxa and varying sequencing depths, to yield meaningful biological pa

SUBMITTER: Hawinkel S 

PROVIDER: S-EPMC6373939 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature