Microbiome data analysis often relies on the identification of a subset of potential biomarkers associated with a clinical outcome of interest. Robust ZeroSum regression, an elastic-net penalized compositional regression built on the least trimmed squares estimator, is a variable selection procedure capable to cope with the high dimensionality of these data, their compositional nature, and, at the same time, it guarantees robustness against the presence of outliers. The necessity of discovering “true” effects and to improve clinical research quality and reproducibility has motivated us to propose a two-step robust compositional knockoff filter procedure, which allows selecting the set of relevant biomarkers, among the many measured features having a nonzero effect on the response, controlling the expected fraction of false positives. We demonstrate the effectiveness of our proposal in an extensive simulation study, and illustrate its usefulness in an application to intestinal microbiome analysis.

Monti, G., Filzmoser, P. (2022). A robust knockoff filter for sparse regression analysis of microbiome compositional data. COMPUTATIONAL STATISTICS [10.1007/s00180-022-01268-7].

A robust knockoff filter for sparse regression analysis of microbiome compositional data

Monti, G S
;
2022

Abstract

Microbiome data analysis often relies on the identification of a subset of potential biomarkers associated with a clinical outcome of interest. Robust ZeroSum regression, an elastic-net penalized compositional regression built on the least trimmed squares estimator, is a variable selection procedure capable to cope with the high dimensionality of these data, their compositional nature, and, at the same time, it guarantees robustness against the presence of outliers. The necessity of discovering “true” effects and to improve clinical research quality and reproducibility has motivated us to propose a two-step robust compositional knockoff filter procedure, which allows selecting the set of relevant biomarkers, among the many measured features having a nonzero effect on the response, controlling the expected fraction of false positives. We demonstrate the effectiveness of our proposal in an extensive simulation study, and illustrate its usefulness in an application to intestinal microbiome analysis.
Si
Articolo in rivista - Articolo scientifico
Scientifica
False discovery rate (FDR), High-dimensional regression, Knockoffs, Variable selection, Robustness
English
Monti, G., Filzmoser, P. (2022). A robust knockoff filter for sparse regression analysis of microbiome compositional data. COMPUTATIONAL STATISTICS [10.1007/s00180-022-01268-7].
Monti, G; Filzmoser, P
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10281/390674
Citazioni
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
Social impact