Phenotype-Driven Transitions In Regulatory Network Structure
Summary (2 min read)
INTRODUCTION
- 1,2 Despite the increasing power and depth of sequencing studies, identifying the causal mutations and singlenucleotide polymorphisms (SNPs) that are responsible for determining heritable traits and disease susceptibility remains challenging.
- Biological networks are known to have modular structure and contain closely interacting groups of nodes, or “communities”, that work together to carry out cellular functions.
- One way to address these issues and find more robust differences between networks is to identify changes in groups of nodes, rather than in individual edges.
- 17–20 However, these methods are limited to examining pre-defined gene modules and network features, and fail to take full advantage of the network structure.
RESULTS
- The modularity represents to what extent the proposed communities have more edges within them than expected in a randomly connected graph with the same degree properties; this null expectation is represented in the second term of the equation above.
- 31 Community comparison and edge subtraction Having arrived at a pair of inferred networks corresponding to different phenotypic states, there are two straightforward ways to compare the community structures based on the modularity metric (Fig. 1).
- The authors previously found that a gene signature associated with angiogenesis is able to classify ovarian cancer patients into a poorprognosis subtype.35.
- The authors also computed the correlation in expression among the genes in each ALPACA module.
- Finally, the authors ranked the genes by their contribution to the differential modularity and used Gene Set Enrichment Analysis (GSEA) to evaluate enrichment for GO terms across the whole network (see Materials and Methods).
DISCUSSION
- Biological networks have complex modular and hierarchical topologies that allow organisms to carry out the functions necessary for survival.
- ALPACA differs from other community Published in partnership with the Systems Biology Institute npj Systems Biology and Applications (2018) 16 detection methods in that it compares the structure of networks to each other rather than to a random background network and is thus better able to detect subtle differences in network modular structure.
- The differential modularity also incorporates increased and decreased edge weights across the entire network into a single, simple framework for module detection.
- This is because network-level analysis, and ALPACA in particular, helps organize both strongly and weakly differentially expressed genes into new modules that are under common regulatory control, identifying signaling pathways that could not have been distinguished if genes were ranked purely by differential expression.
- Genes annotated by the shown GO terms are labeled in large font Published in partnership with the Systems Biology Institute npj Systems Biology and Applications (2018) 16 “edgetic” perturbations, in order to discover functional changes in protein complexes and signaling associated with disease.
METHODS
- ALPACA algorithm ALPACA comprises the following two steps: Step 1: The input network consists of edges between regulators and target genes.
- The authors first used either CONDOR or Louvain method to find the community structure of the baseline and perturbed networks, in each case keeping only edges that had positive z-scores.
- The authors evaluated the results of each method on the simulated networks by comparing the ranks of true positives (the target genes in the added module) against a background consisting of target genes not in the added module.
- For the baseline network, the edges between groups A and B were set to weight 0.8 and for the perturbed network, the edges between groups A and B were set to weight 0.2.
- 38,52 Differential expression analysis was carried out using the R package limma, and p-values were adjusted for multiple testing using the Benjamini–Hochberg method.53.
AUTHOR CONTRIBUTIONS
- M.P. conceived of the project, performed analysis, and wrote the paper.
- J.Q. helped refine the analysis and wrote the paper.
- The authors declare no competing financial interests.
- Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations, also known as Publisher's note.
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References
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"Phenotype-Driven Transitions In Reg..." refers background in this paper
...Finally, modules 16 and 17 were enriched for various terms involving interferon response, interleukins, and regulation of the NFκB pathway, consistent with the theory that chronic inflammation is associated with risk of cancer (46)....
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13,548 citations
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"Phenotype-Driven Transitions In Reg..." refers methods in this paper
...We then used the Δw!" values as new edge weights to perform community detection using CONDOR or Louvain optimization (9, 30)....
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...First, we determine the community structure of the baseline network using established methods (9, 30)....
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...Second, we compute the differential modularity matrix D!" and apply the Louvain optimization algorithm to iteratively aggregate the nodes into modules (30)....
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...The Louvain algorithm is a particularly efficient way to find high-quality local optima of the modularity function (30)....
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12,882 citations
"Phenotype-Driven Transitions In Reg..." refers background or methods in this paper
...An exhaustive search is not possible for large networks, but many methods have been developed to find locally optimal community structure, including ones based on edge betweenness, label propagation, and random walks (27-29)....
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...RESULTS Modularity maximization and comparing community structures Many methods for determining the community structure of a network are based on maximizing the modularity (27): Q = 1 2m A!" − d!d! 2m !,! δ(C! ,C!) Here, A!" indicates the adjacency matrix of the network, m is the number of edges, d! is the degree of node i, and C! is the community assignment of node i....
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