Scalable log-ratio lasso regression for enhanced microbial feature selection with FLORAL Journal Article
| Authors: | Fei, T.; Funnell, T.; Waters, N. R.; Raj, S. S.; Baichoo, M.; Sadeghi, K.; Dai, A.; Miltiadous, O.; Shouval, R.; Lv, M.; Peled, J. U.; Ponce, D. M.; Perales, M. A.; Gönen, M.; van den Brink, M. R. M. |
| Article Title: | Scalable log-ratio lasso regression for enhanced microbial feature selection with FLORAL |
| 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. © 2024 The Author(s) |
| Keywords: | survival analysis; survival rate; genetics; nonhuman; sensitivity analysis; hematopoietic stem cell transplantation; algorithms; algorithm; amplicon; computer simulation; rna 16s; software; signal detection; microorganism; longitudinal data; microflora; microbiota; microbiome; variable selection; humans; human; article; rna sequencing; lasso; population abundance; feature selection; least absolute shrinkage and selection operator; compositional data; cp: microbiology; cp: systems biology |
| Journal Title: | Cell Reports Methods |
| Volume: | 4 |
| Issue: | 11 |
| ISSN: | 2667-2375 |
| Publisher: | Cell Press |
| Date Published: | 2024-11-18 |
| Start Page: | 100899 |
| Language: | English |
| DOI: | 10.1016/j.crmeth.2024.100899 |
| PUBMED: | 39515336 |
| PROVIDER: | scopus |
| PMCID: | PMC11705925 |
| DOI/URL: | |
| Notes: | The MSK Cancer Center Support Grant (P30 CA008748) is acknowledge in the PDF -- Corresponding authors is MSK author: Teng Fe -- Source: Scopus |
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