438 research outputs found
Differential analysis of biological networks
In cancer research, the comparison of gene expression or DNA methylation
networks inferred from healthy controls and patients can lead to the discovery
of biological pathways associated to the disease. As a cancer progresses, its
signalling and control networks are subject to some degree of localised
re-wiring. Being able to detect disrupted interaction patterns induced by the
presence or progression of the disease can lead to the discovery of novel
molecular diagnostic and prognostic signatures. Currently there is a lack of
scalable statistical procedures for two-network comparisons aimed at detecting
localised topological differences. We propose the dGHD algorithm, a methodology
for detecting differential interaction patterns in two-network comparisons. The
algorithm relies on a statistic, the Generalised Hamming Distance (GHD), for
assessing the degree of topological difference between networks and evaluating
its statistical significance. dGHD builds on a non-parametric permutation
testing framework but achieves computationally efficiency through an asymptotic
normal approximation. We show that the GHD is able to detect more subtle
topological differences compared to a standard Hamming distance between
networks. This results in the dGHD algorithm achieving high performance in
simulation studies as measured by sensitivity and specificity. An application
to the problem of detecting differential DNA co-methylation subnetworks
associated to ovarian cancer demonstrates the potential benefits of the
proposed methodology for discovering network-derived biomarkers associated with
a trait of interest
Local asymptotics of selection models
Selection models are ubiquitous in statistics. In recent years, they have
regained considerable popularity as the working inferential models in many
selective inference problems. In this paper, we derive an asymptotic expansion
of the local likelihood ratios of selection models. We show that under mild
regularity conditions, they are asymptotically equivalent to a sequence of
Gaussian selection models. This generalizes the Local Asymptotic Normality
framework of Le Cam (1960). Furthermore, we derive the asymptotic shape of
Bayesian posterior distributions constructed from selection models, and show
that they can be significantly miscalibrated in a frequentist sense.Comment: 14 pages, 1 figur
Splitting strategies for post-selection inference
We consider the problem of providing valid inference for a selected parameter
in a sparse regression setting. It is well known that classical regression
tools can be unreliable in this context due to the bias generated in the
selection step. Many approaches have been proposed in recent years to ensure
inferential validity. Here, we consider a simple alternative to data splitting
based on randomising the response vector, which allows for higher selection and
inferential power than the former and is applicable with an arbitrary selection
rule. We provide a theoretical and empirical comparison of both methods and
extend the randomisation approach to non-normal settings. Our investigations
show that the gain in power can be substantial.Comment: 24 pages, 2 figure
Symbolism over Substance? Large Law Firms and Corporate Social Responsibility
First draft of a paper which now appears in the journal 'Legal Ethics' Volume 18(2)
This paper considers the individual CSR policies of the top 100 English Law firms (as ranked by the trade publication 'The Lawyer'), what the firms categorise as constituting CSR activity and the public disclosures they make. The research highlights that few firms explain why they are committed to CSR and the quality of disclosures varied so widely that meaningful comparison was not possible
A plant homologue to mammalian brain 14-3-3 protein and protein kinase C inhibitor
We have isolated cDNA clones of Spinacea oleracea L. and Oenothera hookeri of 930 and 1017 base pairs, respectively. The open reading frame deduced from the Oenothera sequence codes for a protein of a calculated molecular mass of 29 200. The primary amino acid sequence exhibits a very high degree (88%) of homology to the 14-3-3 protein from bovine brain, and protein kinase C inhibitor from sheep brain. Subsequently the plant protein was partially purified from leaf extract. The partially purified plant protein inhibited protein kinase C from sheep brain in a heterologous assay system. The active fraction consisted of 5–6 different polypeptides of similar molecular size. One of these proteins crossreacted with a peptide-specific antibody against protein kinase C inhibitor protein from sheep brain
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