Palarea-Albaladejo, Javier, Martín-Fernández, Josep Antoni, Ruiz-Gazen, Anne and Thomas-Agnan, Christine (2022) lrSVD: an efficient imputation algorithm for incomplete high-throughput compositional data. Journal of Chemometrics, vol. 36 (n° 12).

Full text not available from this repository.
Identification Number : 10.1002/cem.3459


Compositional methods have been successfully integrated into the chemometric toolkit to analyse and model different types of data generated by modern high-throughput technologies. Within this compositional framework, the focus is put on the relative information conveyed in the data by using log-ratio coordinate representations. However, log-ratios cannot be computed when the data matrix is not complete. A new computationally efficient data imputation algorithm based on compositional principles and aimed at high-throughput continuous-valued compositions is introduced that relies on a constrained low-rank matrix approximation of the data. Simulation and real metabolomics data are used to demonstrate its performance and ability to deal with different forms of incomplete data: zeros, nondetects, missing values or a combination of them. The computer routines lrSVD and lrSVDplus are implemented in the R package zCompositions to facilitate its use by practitioners.

Item Type: Article
Language: English
Date: December 2022
Refereed: Yes
Divisions: TSE-R (Toulouse)
Site: UT1
Date Deposited: 12 Jan 2023 14:14
Last Modified: 12 Jan 2023 14:14
OAI Identifier:
View Item