5 research outputs found
Robust Detection of Hierarchical Communities from Escherichia coli Gene Expression Data
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 coregulated 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.Comment: Due to appear in PLoS Computational Biology. Supplementary Figure S1
was not uploaded but is available by contacting the author. 27 pages, 5
figures, 15 supplementary file
Network deconvolution as a general method to distinguish direct dependencies in networks
Recognizing direct relationships between variables connected in a network is a pervasive problem in biological, social and information sciences as correlation-based networks contain numerous indirect relationships. Here we present a general method for inferring direct effects from an observed correlation matrix containing both direct and indirect effects. We formulate the problem as the inverse of network convolution, and introduce an algorithm that removes the combined effect of all indirect paths of arbitrary length in a closed-form solution by exploiting eigen-decomposition and infinite-series sums. We demonstrate the effectiveness of our approach in several network applications: distinguishing direct targets in gene expression regulatory networks; recognizing directly interacting amino-acid residues for protein structure prediction from sequence alignments; and distinguishing strong collaborations in co-authorship social networks using connectivity information alone. In addition to its theoretical impact as a foundational graph theoretic tool, our results suggest network deconvolution is widely applicable for computing direct dependencies in network science across diverse disciplines.National Institutes of Health (U.S.) (grant R01 HG004037)National Institutes of Health (U.S.) (grant HG005639)Swiss National Science Foundation (Fellowship)National Science Foundation (U.S.) (NSF CAREER Award 0644282