2,943 research outputs found
Annotation Enrichment Analysis: An Alternative Method for Evaluating the Functional Properties of Gene Sets
Gene annotation databases (compendiums maintained by the scientific community
that describe the biological functions performed by individual genes) are
commonly used to evaluate the functional properties of experimentally derived
gene sets. Overlap statistics, such as Fisher's Exact Test (FET), are often
employed to assess these associations, but don't account for non-uniformity in
the number of genes annotated to individual functions or the number of
functions associated with individual genes. We find FET is strongly biased
toward over-estimating overlap significance if a gene set has an unusually high
number of annotations. To correct for these biases, we develop Annotation
Enrichment Analysis (AEA), which properly accounts for the non-uniformity of
annotations. We show that AEA is able to identify biologically meaningful
functional enrichments that are obscured by numerous false-positive enrichment
scores in FET, and we therefore suggest it be used to more accurately assess
the biological properties of gene sets
Mixing patterns and community structure in networks
Common experience suggests that many networks might possess community
structure - division of vertices into groups, with a higher density of edges
within groups than between them. Here we describe a new computer algorithm that
detects structure of this kind. We apply the algorithm to a number of
real-world networks and show that they do indeed possess non-trivial community
structure. We suggest a possible explanation for this structure in the
mechanism of assortative mixing, which is the preferential association of
network vertices with others that are like them in some way. We show by
simulation that this mechanism can indeed account for community structure. We
also look in detail at one particular example of assortative mixing, namely
mixing by vertex degree, in which vertices with similar degree prefer to be
connected to one another. We propose a measure for mixing of this type which we
apply to a variety of networks, and also discuss the implications for network
structure and the formation of a giant component in assortatively mixed
networks.Comment: 21 pages, 9 postscript figures, 2 table
Robustness of Network Measures to Link Errors
In various applications involving complex networks, network measures are
employed to assess the relative importance of network nodes. However, the
robustness of such measures in the presence of link inaccuracies has not been
well characterized. Here we present two simple stochastic models of false and
missing links and study the effect of link errors on three commonly used node
centrality measures: degree centrality, betweenness centrality, and dynamical
importance. We perform numerical simulations to assess robustness of these
three centrality measures. We also develop an analytical theory, which we
compare with our simulations, obtaining very good agreement.Comment: 9 pages, 9 figure
Dynamical Instability in Boolean Networks as a Percolation Problem
Boolean networks, widely used to model gene regulation, exhibit a phase
transition between regimes in which small perturbations either die out or grow
exponentially. We show and numerically verify that this phase transition in the
dynamics can be mapped onto a static percolation problem which predicts the
long-time average Hamming distance between perturbed and unperturbed orbits
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