Motivation: High-throughput sequencing technologies generate a huge amount of data, permitting the quantification of microbiome compositions. The obtained data are essentially sparse compositional data vectors, namely vectors of bacterial gene proportions which compose the microbiome. Subsequently, the need for statistical and computational methods that consider the special nature of microbiome data has increased. A critical aspect in microbiome research is to identify microbes associated with a clinical outcome. Another crucial aspect with high-dimensional data is the detection of outlying observations, whose presence affects seriously the prediction accuracy. Results: In this article we connect robustness and sparsity in the context of variable selection in regression with compositional covariates with a continuous response. The compositional character of the covariates is taken into account by a linear log-contrast model, and elastic-net regularization achieves sparsity in the regression coefficient estimates. Robustness is obtained by performing trimming in the objective function of the estimator. A reweighting step increases the efficiency of the estimator, and it also allows for diagnostics in terms of outlier identification. The numerical performance of the proposed method is evaluated via simulation studies, and its usefulness is illustrated by an application to a microbiome study with the aim to predict caffeine intake based on the human gut microbiome composition. Availability: The R-package "RobZS" can be downloaded at https://github.com/giannamonti/RobZS. Supplementary information: Supplementary data are available at Bioinformatics online.

Monti, G., Filzmoser, P. (2021). Sparse least trimmed squares regression with compositional covariates for high dimensional data. BIOINFORMATICS, 37(21 (1 November 2021)), 3805-3814 [10.1093/bioinformatics/btab572].

Sparse least trimmed squares regression with compositional covariates for high dimensional data

Monti, G. S.
;
2021

Abstract

Motivation: High-throughput sequencing technologies generate a huge amount of data, permitting the quantification of microbiome compositions. The obtained data are essentially sparse compositional data vectors, namely vectors of bacterial gene proportions which compose the microbiome. Subsequently, the need for statistical and computational methods that consider the special nature of microbiome data has increased. A critical aspect in microbiome research is to identify microbes associated with a clinical outcome. Another crucial aspect with high-dimensional data is the detection of outlying observations, whose presence affects seriously the prediction accuracy. Results: In this article we connect robustness and sparsity in the context of variable selection in regression with compositional covariates with a continuous response. The compositional character of the covariates is taken into account by a linear log-contrast model, and elastic-net regularization achieves sparsity in the regression coefficient estimates. Robustness is obtained by performing trimming in the objective function of the estimator. A reweighting step increases the efficiency of the estimator, and it also allows for diagnostics in terms of outlier identification. The numerical performance of the proposed method is evaluated via simulation studies, and its usefulness is illustrated by an application to a microbiome study with the aim to predict caffeine intake based on the human gut microbiome composition. Availability: The R-package "RobZS" can be downloaded at https://github.com/giannamonti/RobZS. Supplementary information: Supplementary data are available at Bioinformatics online.
Articolo in rivista - Articolo scientifico
Lasso, elastic-net regularization, log-contrast model, sparsity, microbiome, compositional data;
English
3805
3814
10
Monti, G., Filzmoser, P. (2021). Sparse least trimmed squares regression with compositional covariates for high dimensional data. BIOINFORMATICS, 37(21 (1 November 2021)), 3805-3814 [10.1093/bioinformatics/btab572].
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: https://hdl.handle.net/10281/323818
Citazioni
  • Scopus ND
  • ???jsp.display-item.citation.isi??? 3
Social impact