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