Skip to Main content Skip to Navigation
New interface
Journal articles

A Hierarchical Poisson Log-Normal Model for Network Inference from RNA Sequencing Data

Melina Gallopin 1 Andrea Rau 2 Florence Jaffrezic 2 
1 SELECT - Model selection in statistical learning
Inria Saclay - Ile de France, LMO - Laboratoire de Mathématiques d'Orsay
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.
Document type :
Journal articles
Complete list of metadata

Cited literature [32 references]  Display  Hide  Download
Contributor : Melina Gallopin Connect in order to contact the contributor
Submitted on : Friday, December 20, 2013 - 11:48:59 PM
Last modification on : Tuesday, October 25, 2022 - 4:19:46 PM
Long-term archiving on: : Friday, March 21, 2014 - 4:40:16 AM


Files produced by the author(s)


  • HAL Id : hal-00921397, version 1


Melina Gallopin, Andrea Rau, Florence Jaffrezic. A Hierarchical Poisson Log-Normal Model for Network Inference from RNA Sequencing Data. PLoS ONE, 2013. ⟨hal-00921397⟩



Record views


Files downloads