title: A hierarchical poisson log-normal model for network inference from RNA sequencing data.
abstract: Gene network inference from transcriptomic data is an important methodological challenge and a key aspect of systems biology. Although several methods have been proposed to infer networks from microarray data, there is a need for inference methods able to model RNA-seq data, which are count-based and highly variable. In this work we propose a hierarchical Poisson log-normal model with a Lasso penalty to infer gene networks from RNA-seq data; this model has the advantage of directly modelling discrete data and accounting for inter-sample variance larger than the sample mean. Using real microRNA-seq data from breast cancer tumors and simulations, we compare this method to a regularized Gaussian graphical model on log-transformed data, and a Poisson log-linear graphical model with a Lasso penalty on power-transformed data. For data simulated with large inter-sample dispersion, the proposed model performs better than the other methods in terms of sensitivity, specificity and area under the ROC curve. These results show the necessity of methods specifically designed for gene network inference from RNA-seq data.
authors.affiliation: Département de Génétique Animale, INRA, Jouy-en-Josas, France ; Département de Génétique Animale, AgroParis Tech, Paris, France ; Département de Mathématiques, Université Paris-Sud 11, Orsay, France.
authors.forename: Mélina
authors.initials: M
authors.lastname: Gallopin
authors.forename: Andrea
authors.initials: A
authors.lastname: Rau
authors.forename: Florence
authors.initials: F
authors.lastname: Jaffrézic
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics* / Q000473:pathology; D003198:Computer Simulation; D005260:Female; D053263:Gene Regulatory Networks; D006801:Humans; D035683:MicroRNAs / Q000235:genetics*; D008954:Models, Biological; D015233:Models, Statistical; D012372:ROC Curve
chemical_list: D035683:MicroRNAs