Integrative multi-omics module network inference with Lemon-Tree

TL;DR

Lemon-Tree is an open-source software for multi-omics module network inference that outperforms existing tools and accurately identifies key cancer driver genes in glioblastoma datasets.

Contribution

We developed Lemon-Tree, a novel, modular software platform implementing ensemble methods for multi-omics module network inference, validated on large-scale cancer datasets.

Findings

Lemon-Tree compares favorably with state-of-the-art software.

It accurately identifies known glioblastoma oncogenes and tumor suppressors.

Predicted novel driver genes were validated through pathway and survival analyses.

Abstract

Module network inference is an established statistical method to reconstruct co-expression modules and their upstream regulatory programs from integrated multi-omics datasets measuring the activity levels of various cellular components across different individuals, experimental conditions or time points of a dynamic process. We have developed Lemon-Tree, an open-source, platform-independent, modular, extensible software package implementing state-of-the-art ensemble methods for module network inference. We benchmarked Lemon-Tree using large-scale tumor datasets and showed that Lemon-Tree algorithms compare favorably with state-of-the-art module network inference software. We also analyzed a large dataset of somatic copy-number alterations and gene expression levels measured in glioblastoma samples from The Cancer Genome Atlas and found that Lemon-Tree correctly identifies known glioblastoma oncogenes and tumor suppressors as master regulators in the inferred module network. Novel candidate driver genes predicted by Lemon-Tree were validated using tumor pathway and survival analyses. Lemon-Tree is available from http://lemon-tree.googlecode.com under the GNU General Public License version 2.0.