16,957 research outputs found
A geometric interpretation of the permutation -value and its application in eQTL studies
Permutation -values have been widely used to assess the significance of
linkage or association in genetic studies. However, the application in
large-scale studies is hindered by a heavy computational burden. We propose a
geometric interpretation of permutation -values, and based on this geometric
interpretation, we develop an efficient permutation -value estimation method
in the context of regression with binary predictors. An application to a study
of gene expression quantitative trait loci (eQTL) shows that our method
provides reliable estimates of permutation -values while requiring less than
5% of the computational time compared with direct permutations. In fact, our
method takes a constant time to estimate permutation -values, no matter how
small the -value. Our method enables a study of the relationship between
nominal -values and permutation -values in a wide range, and provides a
geometric perspective on the effective number of independent tests.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS298 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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RGFGA: An efficient representation and crossover for grouping genetic algorithms
There is substantial research into genetic algorithms that are used to group large numbers of
objects into mutually exclusive subsets based upon some fitness function. However, nearly all
methods involve degeneracy to some degree.
We introduce a new representation for grouping genetic algorithms, the restricted growth function
genetic algorithm, that effectively removes all degeneracy, resulting in a more efficient search. A new crossover operator is also described that exploits a measure of similarity between chromosomes in a population. Using several synthetic datasets, we compare the performance of our representation and crossover with another well known state-of-the-art GA method, a strawman
optimisation method and a well-established statistical clustering algorithm, with encouraging results
Factorial graphical lasso for dynamic networks
Dynamic networks models describe a growing number of important scientific
processes, from cell biology and epidemiology to sociology and finance. There
are many aspects of dynamical networks that require statistical considerations.
In this paper we focus on determining network structure. Estimating dynamic
networks is a difficult task since the number of components involved in the
system is very large. As a result, the number of parameters to be estimated is
bigger than the number of observations. However, a characteristic of many
networks is that they are sparse. For example, the molecular structure of genes
make interactions with other components a highly-structured and therefore
sparse process.
Penalized Gaussian graphical models have been used to estimate sparse
networks. However, the literature has focussed on static networks, which lack
specific temporal constraints. We propose a structured Gaussian dynamical
graphical model, where structures can consist of specific time dynamics, known
presence or absence of links and block equality constraints on the parameters.
Thus, the number of parameters to be estimated is reduced and accuracy of the
estimates, including the identification of the network, can be tuned up. Here,
we show that the constrained optimization problem can be solved by taking
advantage of an efficient solver, logdetPPA, developed in convex optimization.
Moreover, model selection methods for checking the sensitivity of the inferred
networks are described. Finally, synthetic and real data illustrate the
proposed methodologies.Comment: 30 pp, 5 figure
Evidence against the Detectability of a Hippocampal Place Code Using Functional Magnetic Resonance Imaging
Individual hippocampal neurons selectively increase their firing rates in specific spatial locations. As a population, these neurons provide a decodable representation of space that is robust against changes to sensory- and path-related cues. This neural code is sparse and distributed, theoretically rendering it undetectable with population recording methods such as functional magnetic resonance imaging (fMRI). Existing studies nonetheless report decoding spatial codes in the human hippocampus using such techniques. Here we present results from a virtual navigation experiment in humans in which we eliminated visual- and path-related confounds and statistical limitations present in existing studies, ensuring that any positive decoding results would represent a voxel-place code. Consistent with theoretical arguments derived from electrophysiological data and contrary to existing fMRI studies, our results show that although participants were fully oriented during the navigation task, there was no statistical evidence for a place code
Consensus clustering and functional interpretation of gene-expression data
Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency in assigning related gene-expression profiles to clusters. Obtaining a consensus set of clusters from a number of clustering methods should improve confidence in gene-expression analysis. Here we introduce consensus clustering, which provides such an advantage. When coupled with a statistically based gene functional analysis, our method allowed the identification of novel genes regulated by NFκB and the unfolded protein response in certain B-cell lymphomas
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