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

  • Sathyabaarathi Ravichandran
  • Nagasuma ChandraEmail author


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.


Perturbation network Omics Statistical inference methods Knowledge-based methods 



The Algorithm for the Reconstruction of Accurate Cellular Networks


Bayesian Networks


Differentially expressed gene


False discovery rate


Gold standard gene sets


Gold standard pathway sets


Healthy controls


Human protein–protein interaction network


Human Protein Reference Database


Mutual information matrix


Normal skin


Primary melanoma


Search Tool for the Retrieval of Interacting Genes/Proteins


Top activated paths




Top perturbed network


Top repressed paths


Weighted Gene Co-expression Network Analysis



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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