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Interrogation of genome-wide networks in biology: comparison of knowledge-based and statistical methods

  • Sathyabaarathi Ravichandran
  • Nagasuma ChandraEmail author
Article

Abstract

Networks are used extensively in the study of biological systems to address a wide range of questions such as understanding the complex behaviour of a given system or identifying key alterations leading to a disease phenotype. Numerous network-based methods have been developed for inferring molecular interactions using transcriptomic and proteomic data. Different network methods come with their own advantages and limitations, and often give different results for the same data. A systematic study is essential to understand how the methods fare in terms of correctly predicting known biological processes and yielding testable biological hypotheses. To address this, we have carried out a comparison of four different methods to derive context-specific perturbations for two different case studies and evaluated their performance. The methods can be broadly classified into statistical inference and knowledge-based methods. Two of the four methods, WGCNA and ARACNE, belong to the broad class of data-driven approaches which do not rely on prior network information. On the other hand, ResponseNet and jActiveModules utilise knowledge-based protein–protein interaction networks and integrate condition-specific transcriptome or proteome data. We evaluated the interactions inferred through all the approaches and assessed their biological relevance based on three criteria: (1) enrichment of the gold standard gene sets, (2) comparison to gold standard pathways and (3) recovery of hub genes from the context-specific perturbed network, known to be related to the given condition. Comparing the performance of these four methods in two different cases, tuberculosis and melanoma, showed superior performance by ResponseNet, based on all three criteria.

Keywords

Perturbation network Omics Statistical inference methods Knowledge-based methods 

Abbreviations

ARACNE

The Algorithm for the Reconstruction of Accurate Cellular Networks

BNs

Bayesian Networks

DEG

Differentially expressed gene

FDR

False discovery rate

GSGS

Gold standard gene sets

GSPS

Gold standard pathway sets

HC

Healthy controls

hPPiN

Human protein–protein interaction network

HPRD

Human Protein Reference Database

MIM

Mutual information matrix

NS

Normal skin

PM

Primary melanoma

STRING

Search Tool for the Retrieval of Interacting Genes/Proteins

TAP

Top activated paths

TB

Tuberculosis

TPN

Top perturbed network

TRP

Top repressed paths

WGCNA

Weighted Gene Co-expression Network Analysis

Notes

Acknowledgements

We thank Department of Biotechnology (DBT), Government of India for the funding. Narmada Sambaturu and Amrisha Bhosle are acknowledged for proof reading the manuscript.

Supplementary material

12572_2018_242_MOESM1_ESM.docx (14.9 mb)
Supplementary material 1 (DOCX 15291 kb)

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Copyright information

© Indian Institute of Technology Madras 2019

Authors and Affiliations

  1. 1.IISc Mathematics InitiativeIndian Institute of ScienceBangaloreIndia
  2. 2.Department of BiochemistryIndian Institute of ScienceBangaloreIndia

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