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Transformation and model choice for RNA-seq co-expression analysis.
Rau, Andrea; Maugis-Rabusseau, Cathy.
Afiliación
  • Rau A; GABI, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France.
  • Maugis-Rabusseau C; Institut de Mathématiques de Toulouse, INSA de Toulouse, Université de Toulouse, 31400 Toulouse, France.
Brief Bioinform ; 19(3): 425-436, 2018 05 01.
Article en En | MEDLINE | ID: mdl-28065917
ABSTRACT
Although a large number of clustering algorithms have been proposed to identify groups of co-expressed genes from microarray data, the question of if and how such methods may be applied to RNA sequencing (RNA-seq) data remains unaddressed. In this work, we investigate the use of data transformations in conjunction with Gaussian mixture models for RNA-seq co-expression analyses, as well as a penalized model selection criterion to select both an appropriate transformation and number of clusters present in the data. This approach has the advantage of accounting for per-cluster correlation structures among samples, which can be strong in RNA-seq data. In addition, it provides a rigorous statistical framework for parameter estimation, an objective assessment of data transformations and number of clusters and the possibility of performing diagnostic checks on the quality and homogeneity of the identified clusters. We analyze four varied RNA-seq data sets to illustrate the use of transformations and model selection in conjunction with Gaussian mixture models. Finally, we propose a Bioconductor package coseq (co-expression of RNA-seq data) to facilitate implementation and visualization of the recommended RNA-seq co-expression analyses.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Biología Computacional / Neocórtex / Drosophila melanogaster / Embrión de Mamíferos / Secuenciación de Nucleótidos de Alto Rendimiento / Feto Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Animals / Female / Humans / Male Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2018 Tipo del documento: Article País de afiliación: Francia

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Biología Computacional / Neocórtex / Drosophila melanogaster / Embrión de Mamíferos / Secuenciación de Nucleótidos de Alto Rendimiento / Feto Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Animals / Female / Humans / Male Idioma: En Revista: Brief Bioinform Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA Año: 2018 Tipo del documento: Article País de afiliación: Francia