MODifieR: an Ensemble R Package for Inference of Disease Modules from Transcriptomics Networks.

Hendrik A de Weerd,Julia Åkesson,Daniel Muthas,Zelmina Lubovac-Pilav,Mika Gustafsson,David Martínez-Enguita,Tejaswi V S Badam,Lenore Cowen

doi:10.1093/bioinformatics/btaa235

Hendrik A de Weerd, Julia Åkesson + Show 6 more

Open Access

https://doi.org/10.1093/bioinformatics/btaa235

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Abstract

Complex diseases are due to the dense interactions of many disease-associated factors that dysregulate genes that in turn form the so-called disease modules, which have shown to be a powerful concept for understanding pathological mechanisms. There exist many disease module inference methods that rely on somewhat different assumptions, but there is still no gold standard or best-performing method. Hence, there is a need for combining these methods to generate robust disease modules. We developed MODule IdentiFIER (MODifieR), an ensemble R package of nine disease module inference methods from transcriptomics networks. MODifieR uses standardized input and output allowing the possibility to combine individual modules generated from these methods into more robust disease-specific modules, contributing to a better understanding of complex diseases. MODifieR is available under the GNU GPL license and can be freely downloaded from https://gitlab.com/Gustafsson-lab/MODifieR and as a Docker image from https://hub.docker.com/r/ddeweerd/modifier. Supplementary data are available at Bioinformatics online.

Highlights

Various algorithms have been proposed to infer groups of diseaseassociated genes using networks, i.e. disease modules
The DREAM community compared different module inference methods that were based on network topologies to identify disease-associated genes (Choobdar et al, 2019) which led to the tool MONET (Tomasoni et al, 2019)
Six methods are integrated from previous packages DIAMOnD (Ghiassian et al, 2015), DiffCoEx (Tesson et al, 2010), MCODE (Bader and Hogue, 2003), MODA (Li et al, 2016), ModuleDiscoverer (Vlaic et al, 2018) and WGCNA (Langfelder and Horvath, 2012), while three methods have been packaged from our previous publications, namely CliqueSum (Bruhn et al, 2014; Gustafsson et al, 2014) and CorrelationClique (Gawel et al, 2019; Hellberg et al, 2016)

Summary

Introduction

Various algorithms have been proposed to infer groups of diseaseassociated genes using networks, i.e. disease modules. The DREAM community compared different module inference methods that were based on network topologies to identify disease-associated genes (Choobdar et al, 2019) which led to the tool MONET (Tomasoni et al, 2019). No similar tools exist for extracting disease-specific modules by integrating gene expression differences of patients and controls. Related work proves the benefits of using a consensus approach by integrating results from individual network-based methods for determining gene–disease associations (Navlakha and Kingsford, 2010). Propose the R package MODule IdentiFIER (MODifieR), which lets the user run nine popular module inference methods within a unified framework and inspect the result. We illustrate the use of the tool by applying it to public gene expression datasets from the asthmatic cohorts of the U-BIOPRED project. (Supplementary Material S1)

Software implementation

Exporting objects

Module inference

Consensus modules

Conclusion