Accéder directement au contenu Accéder directement à la navigation
Article dans une revue

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.
Type de document :
Article dans une revue
Liste complète des métadonnées

Littérature citée [32 références]  Voir  Masquer  Télécharger
Déposant : Archive Ouverte Prodinra <>
Soumis le : vendredi 29 mai 2020 - 20:18:11
Dernière modification le : samedi 30 mai 2020 - 03:20:03


Fichiers éditeurs autorisés sur une archive ouverte




Mélina Gallopin, Andrea Rau, Florence Jaffrézic. A Hierarchical Poisson Log-Normal Model for Network Inference from RNA Sequencing Data. PLoS ONE, Public Library of Science, 2013, 8, online (10), Non paginé. ⟨10.1371/journal.pone.0077503⟩. ⟨hal-01004715⟩



Consultations de la notice


Téléchargements de fichiers