Scalable log-ratio lasso regression for enhanced microbial feature selection with FLORAL

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, an open-source tool to perform scalable log-ratio lasso regression and microbial feature selection for continuous, binary, time-to-event, and competing risk outcomes, with compatibility for 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 enhanced 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 testing methods for datasets with smaller sample sizes. In a survival analysis of allogeneic hematopoietic cell transplant recipients, FLORAL demonstrated considerable improvement in microbial feature selection by utilizing longitudinal microbiome data over solely using baseline microbiome data.

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Publication Details

Journal
Cell Reports Methods
Published
2024-11-01
DOI
https://doi.org/10.1016/j.crmeth.2024.100899
Citations
7
Primary Topic
Advanced Chemical Sensor Technologies
Type
article
Field-Weighted Citation Impact
1.29

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article

Scalable log-ratio lasso regression for enhanced microbial feature selection with FLORAL

Marcel R.M. van den Brink, Anqi Dai, Oriana Miltiadous, Roni Shouval et al.
7 citations
Cell Reports Methods
Advanced Chemical Sensor Technologies
1.29
article

Scalable log-ratio lasso regression for enhanced microbial feature selection with FLORAL

Marcel R.M. van den Brink, Anqi Dai, Oriana Miltiadous, Roni Shouval, Jonathan U. Peled, Keimya Sadeghi, Mithat Gönen, Nicholas R. Waters, Sandeep S. Raj, Meng Lv, Doris M. Ponce, Miguel‐Angel Perales, Tyler Funnell, Mirae Baichoo, Teng Fei
article en
7 citations

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, an open-source tool to perform scalable log-ratio lasso regression and microbial feature selection for continuous, binary, time-to-event, and competing risk outcomes, with compatibility for 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 enhanced 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 testing methods for datasets with smaller sample sizes. In a survival analysis of allogeneic hematopoietic cell transplant recipients, FLORAL demonstrated considerable improvement in microbial feature selection by utilizing longitudinal microbiome data over solely using baseline microbiome data.

Cell Reports MethodsVol. 4(11)
City Of Hope National Medical Center (US), Memorial Sloan Kettering Cancer Center (US), Peking University (CN), Cornell University (US), Peking University People's Hospital (CN)
Parker Institute for Cancer Immunotherapy, Cycle for Survival, Seres Therapeutics, National Institutes of Health, Beijing Nova Program, National Institute on Aging, National Heart, Lung, and Blood Institute, National Cancer Institute, Hyundai Hope On Wheels
Openalex Percentile: Top 21%
Advanced Chemical Sensor Technologies
1.29
DOIOpen Access PDFOpenAlex
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