Robust detection of hierarchical communities from Escherichia coli gene expression data

PLoS Comput Biol. 2012;8(2):e1002391. doi: 10.1371/journal.pcbi.1002391. Epub 2012 Feb 23.

Abstract

Determining the functional structure of biological networks is a central goal of systems biology. One approach is to analyze gene expression data to infer a network of gene interactions on the basis of their correlated responses to environmental and genetic perturbations. The inferred network can then be analyzed to identify functional communities. However, commonly used algorithms can yield unreliable results due to experimental noise, algorithmic stochasticity, and the influence of arbitrarily chosen parameter values. Furthermore, the results obtained typically provide only a simplistic view of the network partitioned into disjoint communities and provide no information of the relationship between communities. Here, we present methods to robustly detect co-regulated and functionally enriched gene communities and demonstrate their application and validity for Escherichia coli gene expression data. Applying a recently developed community detection algorithm to the network of interactions identified with the context likelihood of relatedness (CLR) method, we show that a hierarchy of network communities can be identified. These communities significantly enrich for gene ontology (GO) terms, consistent with them representing biologically meaningful groups. Further, analysis of the most significantly enriched communities identified several candidate new regulatory interactions. The robustness of our methods is demonstrated by showing that a core set of functional communities is reliably found when artificial noise, modeling experimental noise, is added to the data. We find that noise mainly acts conservatively, increasing the relatedness required for a network link to be reliably assigned and decreasing the size of the core communities, rather than causing association of genes into new communities.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Computational Biology / methods
  • Escherichia coli / genetics*
  • Escherichia coli Proteins / metabolism
  • Flagella / genetics
  • Gene Expression
  • Gene Expression Profiling*
  • Gene Expression Regulation, Bacterial
  • Gene Regulatory Networks
  • Genes, Bacterial
  • Models, Genetic
  • Models, Statistical
  • Oligonucleotide Array Sequence Analysis
  • Systems Biology

Substances

  • Escherichia coli Proteins