Amalgams: data-driven amalgamation for the dimensionality reduction of compositional data.

ABSTRACT: Many next-generation sequencing datasets contain only relative information because of biological and technical factors that limit the total number of transcripts observed for a given sample. It is not possible to interpret any one component in isolation. The field of compositional data analysis has emerged with alternative methods for relative data based on log-ratio transforms. However, these data often contain many more features than samples, and thus require creative new ways to reduce the dimensionality of the data. The summation of parts, called amalgamation, is a practical way of reducing dimensionality, but can introduce a non-linear distortion to the data. We exploit this non-linearity to propose a powerful yet interpretable dimension method called data-driven amalgamation. Our new

SUBMITTER: Quinn TP 

PROVIDER: S-EPMC7671324 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature