FLORAL: Scalable Log-ratio Lasso Regression Enhances Microbial Feature Selection

Identifying predictive microbial biomarkers of patient outcomes from high-throughput microbiome data is of high interest in contemporary cancer research. We present 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. 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 and real data analyses, FLORAL achieved consistently better false-positive control compared to other lasso-based approaches, and better sensitivity over popular differential abundance approaches for datasets with smaller sample size. We also demonstrate the practical utility of FLORAL in handling longitudinal microbiome data in a survival analysis of MSKCC allogeneic hematopoietic-cell transplant (allo-HCT) cohort. The R package is available on CRAN and at https://vdblab.github.io/FLORAL/.