REIT Takeovers: An Evaluation of Barriers to Activity

The global financial crisis in September 2007 resulted in the most significant downturn in global real estate markets in decades. Listed real estate markets were particularly affected, as the combination of high leverage and collapsing real estate values led to massive erosion of shareholder value and investment confidence. The Australian real estate investment trust (A-REIT) market suffered one of the largest downturns relative to its global peers. Currently the downturn appears to be abating, with most A-REITs having undertaken a process of balance sheet reconstruction, via asset sales, equity raisings and debt reduction. However, many A-REITs continue to trade at a significant discount to underlying net asset values. Such circumstances typically provide takeover opportunities that may unlock significant value. However, despite the potentially attractive values, we have seen limited recent takeover activity in Australia. This paucity of A-REIT takeovers suggests there may be barriers to such activity. This research examines the barriers to takeovers currently existing in the A- REIT market

DiffVar: a new method for detecting differential variability with application to methylation in cancer and aging

Methylation of DNA is known to be essential to development and dramatically altered in cancers. The Illumina HumanMethylation450 BeadChip has been used extensively as a cost-effective way to profile nearly half a million CpG sites across the human genome. Here we present DiffVar, a novel method to test for differential variability between sample groups. DiffVar employs an empirical Bayes model framework that can take into account any experimental design and is robust to outliers. We applied DiffVar to several datasets from The Cancer Genome Atlas, as well as an aging dataset. DiffVar is available in the missMethyl Bioconductor R package

A cross-package Bioconductor workflow for analysing methylation array data [version 1; referees: 3 approved, 1 approved with reservations]

Methylation in the human genome is known to be associated with development and disease. The Illumina Infinium methylation arrays are by far the most common way to interrogate methylation across the human genome. This paper provides a Bioconductor workflow using multiple packages for the analysis of methylation array data. Specifically, we demonstrate the steps involved in a typical differential methylation analysis pipeline including: quality control, filtering, normalization, data exploration and statistical testing for probe-wise differential methylation. We further outline other analyses such as differential methylation of regions, differential variability analysis, estimating cell type composition and gene ontology testing. Finally, we provide some examples of how to visualise methylation array data

limma powers differential expression analyses for RNA-sequencing and microarray studies

limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously describe

gsSKAT: Rapid gene set analysis and multiple testing correction for rareâ variant association studies using weighted linear kernels

Nextâ generation sequencing technologies have afforded unprecedented characterization of lowâ frequency and rare genetic variation. Due to low power for singleâ variant testing, aggregative methods are commonly used to combine observed rare variation within a single gene. Causal variation may also aggregate across multiple genes within relevant biomolecular pathways. Kernelâ machine regression and adaptive testing methods for aggregative rareâ variant association testing have been demonstrated to be powerful approaches for pathwayâ level analysis, although these methods tend to be computationally intensive at highâ variant dimensionality and require access to complete data. An additional analytical issue in scans of large pathway definition sets is multiple testing correction. Gene set definitions may exhibit substantial genic overlap, and the impact of the resultant correlation in test statistics on Type I error rate control for large agnostic gene set scans has not been fully explored. Herein, we first outline a statistical strategy for aggregative rareâ variant analysis using component geneâ level linear kernel score test summary statistics as well as derive simple estimators of the effective number of tests for familyâ wise error rate control. We then conduct extensive simulation studies to characterize the behavior of our approach relative to direct application of kernel and adaptive methods under a variety of conditions. We also apply our method to two caseâ control studies, respectively, evaluating rare variation in hereditary prostate cancer and schizophrenia. Finally, we provide openâ source R code for public use to facilitate easy application of our methods to existing rareâ variant analysis results.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136750/1/gepi22036.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136750/2/gepi22036_am.pd