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Pàgina inicial > Articles > Articles publicats > kernInt : |
Data: | 2021 |
Resum: | The advent of next-generation sequencing technologies allowed relative quantification of microbiome communities and their spatial and temporal variation. In recent years, supervised learning (i. e. , prediction of a phenotype of interest) from taxonomic abundances has become increasingly common in the microbiome field. However, a gap exists between supervised and classical unsupervised analyses, based on computing ecological dissimilarities for visualization or clustering. Despite this, both approaches face common challenges, like the compositional nature of next-generation sequencing data or the integration of the spatial and temporal dimensions. Here we propose a kernel framework to place on a common ground the unsupervised and supervised microbiome analyses, including the retrieval of microbial signatures (taxa importances). We define two compositional kernels (Aitchison-RBF and compositional linear) and discuss how to transform non-compositional beta-dissimilarity measures into kernels. Spatial data is integrated with multiple kernel learning, while longitudinal data is evaluated by specific kernels. We illustrate our framework through a single point soil dataset, a human dataset with a spatial component, and a previously unpublished longitudinal dataset concerning pig production. The proposed framework and the case studies are freely available in the kernInt package at https://github. com/elies-ramon/kernInt. |
Ajuts: | Ministerio de Economía y Competitividad PID2019-108829RB-I00 Ministerio de Economía y Competitividad AGL2016-78709-R Ministerio de Economía y Competitividad AGL2017-88849-R Ministerio de Ciencia e Innovación RYC-2019-027244-I Ministerio de Economía y Competitividad SEV-2015-0533 Ministerio de Economía y Competitividad BFU2016-77236-P |
Drets: | Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. |
Llengua: | Anglès |
Document: | Article ; recerca ; Versió publicada |
Matèria: | Microbiome ; Metagenomics ; Kernel ; Supervised ; Unsupervised ; Spatio-temporal ; SVM ; Kpca |
Publicat a: | Frontiers in microbiology, Vol. 12 (January 2021) , art. 609048, ISSN 1664-302X |
14 p, 2.1 MB |