Statistical Properties of the Estimator of the Rotary Coefficient

Accepted versio

Inference of tissue relative proportions of the breast epithelial cell types luminal progenitor, basal, and luminal mature

Hormone receptor negative breast cancers are highly aggressive, and are thought to originate from a subtype of epithelial cells called the luminal progenitor. In this paper, we show how to quantify the number of luminal progenitor cells as well as other epithelial subtypes in breast tissue samples using DNA and RNA based measurements. We find elevated levels of these hormone receptor negative luminal progenitor cells in breast tumour biopsies of hormone receptor negative cancers, as well as in healthy breast tissue samples from BRCA1 (FANCS) mutation carriers. We also find that breast tumours from carriers of heterozygous mutations in non-BRCA Fanconi Anaemia pathway genes are much more likely to be hormone receptor negative. These findings have implications for understanding hormone receptor negative breast cancers, and for breast cancer screening in carriers of heterozygous mutations of Fanconi Anaemia pathway genes

Inference of tissue relative proportions of the breast epithelial cell types luminal progenitor, basal, and luminal mature

Single-cell analysis has revolutionised genomic science in recent years. However, due to cost and other practical considerations, single-cell analyses are impossible for studies based on medium or large patient cohorts. For example, a single-cell analysis usually costs thousands of euros for one tissue sample from one volunteer, meaning that typical studies using single-cell analyses are based on very few individuals. While single-cell genomic data can be used to examine the phenotype of individual cells, cell-type deconvolution methods are required to track the quantities of these cells in bulk-tissue genomic data. Hormone receptor negative breast cancers are highly aggressive, and are thought to originate from a subtype of epithelial cells called the luminal progenitor. In this paper, we show how to quantify the number of luminal progenitor cells as well as other epithelial subtypes in breast tissue samples using DNA and RNA based measurements. We find elevated levels of cells which resemble these hormone receptor negative luminal progenitor cells in breast tumour biopsies of hormone receptor negative cancers, as well as in healthy breast tissue samples from BRCA1 (FANCS) mutation carriers. We also find that breast tumours from carriers of heterozygous mutations in non-BRCA Fanconi Anaemia pathway genes are much more likely to be hormone receptor negative. These findings have implications for understanding hormone receptor negative breast cancers, and for breast cancer screening in carriers of heterozygous mutations of Fanconi Anaemia pathway genes

Simulation Methodology for Inference on Physical Parameters of Complex Vector-Valued Signals

Complex-valued vector time series occur in diverse fields such as oceanography and meteorology, and\ud scientifically interpretable parameters may be estimated from them. We show that it is possible to make\ud inference such as confidence intervals on these parameters using a vector-valued circulant embedding\ud simulation method, combined with bootstrapping. We apply the methodology to three parameters of\ud interest in oceanography, and compare the resulting simulated confidence intervals with those computed\ud using analytic results. We conclude that the simulation scheme offers an inference approach either in the\ud absence of theoretical distributional results, or to check the effect of nuisance parameters where theoretical\ud results are available

Local linear graphon estimation using covariates

We consider local linear estimation of the graphon function which determines probabilities of pairwise edges between nodes within an unlabeled network. Real world networks are typically characterized by node heterogeneity with different nodes exhibiting different degrees of interaction. Existing approaches to graphon estimation are limited to local constant approximations, and are not designed to estimate heterogeneity across the full network. In this paper, we show how continuous node covariates can be employed to estimate heterogeneity in the network via a local linear graphon estimator. We derive the bias and variance of an oracle based local linear graphon estimator and thus obtain the mean integrated squared error optimal bandwidth rule. We also provide a plug-in bandwidth selection procedure, making local linear estimation for unlabeled networks practically feasible. Finite sample performance is investigated in a simulation study. We apply our method to a school friendship network and an email network to illustrate the advantages offered by our approach over existing methods

A widely linear multichannel Wiener Filter for wind prediction

The desire to improve short-term predictions of wind speed and direction has motivated the development of a spatial covariance-based predictor in a complex valued multichannel structure. Wind speed and direction are modeled as the magnitude and phase of complex time series and measurements from multiple geographic locations are embedded in a complex vector which is then used as input to a multichannel Wiener prediction filter. Building on a C-linear cyclo-stationary predictor, a new widely linear filter is developed and tested on hourly mean wind speed and direction measurements made at 13 locations in the UK over 6 years. The new predictor shows a reduction in mean squared error at all locations. Furthermore it is found that the scale of that reduction strongly depends on conditions local to the measurement site

Survival of elderly patients with stage 5 CKD: comparison of conservative management and renal replacement therapy

Background. Elderly patients with end-stage renal disease and severe extra-renal comorbidity have a poor prognosis on renal replacement therapy (RRT) and may opt to be managed conservatively (CM). Information on the survival of patients on this mode of therapy is limited