Enhanced Feature Selection for Microbiome Data using FLORAL: Scalable Log-ratio Lasso Regression.

ABSTRACT: Identifying predictive biomarkers of patient outcomes from high-throughput microbiome data is of high interest, while existing computational methods do not satisfactorily account for complex survival endpoints, longitudinal samples, and taxa-specific sequencing biases. We present FLORAL (https://vdblab.github.io/FLORAL/), an open-source computational tool to perform scalable log-ratio lasso regression and microbial feature selection for continuous, binary, time-to-event, and competing risk outcomes, with compatibility of longitudinal microbiome data as time-dependent covariates. The proposed method adapts the augmented Lagrangian algorithm for a zero-sum constraint optimization problem while enabling a two-stage screening process for extended false-positive control. In extensive simulation

SUBMITTER: Fei T 

PROVIDER: S-EPMC10187229 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature