NATbox: a network analysis toolbox in R

Shweta S Chavan,Michael A Bauer,Marco Scutari,Radhakrishnan Nagarajan

doi:10.1186/1471-2105-10-s11-s14

Abstract

BackgroundThere has been recent interest in capturing the functional relationships (FRs) from high-throughput assays using suitable computational techniques. FRs elucidate the working of genes in concert as a system as opposed to independent entities hence may provide preliminary insights into biological pathways and signalling mechanisms. Bayesian structure learning (BSL) techniques and its extensions have been used successfully for modelling FRs from expression profiles. Such techniques are especially useful in discovering undocumented FRs, investigating non-canonical signalling mechanisms and cross-talk between pathways. The objective of the present study is to develop a graphical user interface (GUI), NATbox: Network Analysis Toolbox in the language R that houses a battery of BSL algorithms in conjunction with suitable statistical tools for modelling FRs in the form of acyclic networks from gene expression profiles and their subsequent analysis.ResultsNATbox is a menu-driven open-source GUI implemented in the R statistical language for modelling and analysis of FRs from gene expression profiles. It provides options to (i) impute missing observations in the given data (ii) model FRs and network structure from gene expression profiles using a battery of BSL algorithms and identify robust dependencies using a bootstrap procedure, (iii) present the FRs in the form of acyclic graphs for visualization and investigate its topological properties using network analysis metrics, (iv) retrieve FRs of interest from published literature. Subsequently, use these FRs as structural priors in BSL (v) enhance scalability of BSL across high-dimensional data by parallelizing the bootstrap routines.ConclusionNATbox provides a menu-driven GUI for modelling and analysis of FRs from gene expression profiles. By incorporating readily available functions from existing R-packages, it minimizes redundancy and improves reproducibility, transparency and sustainability, characteristic of open-source environments. NATbox is especially suited for interdisciplinary researchers and biologists with minimal programming experience and would like to use systems biology approaches without delving into the algorithmic aspects. The GUI provides appropriate parameter recommendations for the various menu options including default parameter choices for the user. NATbox can also prove to be a useful demonstration and teaching tool in graduate and undergraduate course in systems biology. It has been tested successfully under Windows and Linux operating systems. The source code along with installation instructions and accompanying tutorial can be found at http://bioinformatics.ualr.edu/natboxWiki/index.php/Main_Page.

Highlights

There has been recent interest in capturing the functional relationships (FRs) from high-throughput assays using suitable computational techniques
Prior to a detailed description of NATbox functionalities, we briefly review those of a closely related package BNArray [13] which was designed to provide a higherlevel abstraction of R-routines from existing packages for
It consists of four main modules (i) determine missing values in a given data using the functions LLSimpute, implemented in the R-package pcamethods [15] (ii) Learn acyclic network structure using Bayesian structure learning (BSL) routines from the R-package deal [16]

Summary

Introduction

There has been recent interest in capturing the functional relationships (FRs) from high-throughput assays using suitable computational techniques. Bayesian structure learning (BSL) techniques and its extensions have been used successfully for modelling FRs from expression profiles Such techniques are especially useful in discovering undocumented FRs, investigating non-canonical signalling mechanisms and cross-talk between pathways. Classical biological experiments have focused on understanding changes in the expression of single genes across distinct biological states Such differential gene expression analyses while useful may not provide sufficient insight into their interactions or functional relationships (FRs). This in turn can render the conclusions noisy as genes and FRs may exhibit considerable variations across studies Such an approach relies implicitly on prior information, may have limited use in discovering novel FRs. Recent studies have provided compelling evidence of non-canonical signalling mechanism and cross-talk between pathways [3,4] that demand inferring network structure from the given data as opposed to direct inference from documented/ curated pathways

Objectives

Results

Conclusion