Size, power and false discovery rates

Modern scientific technology has provided a new class of large-scale simultaneous inference problems, with thousands of hypothesis tests to consider at the same time. Microarrays epitomize this type of technology, but similar situations arise in proteomics, spectroscopy, imaging, and social science surveys. This paper uses false discovery rate methods to carry out both size and power calculations on large-scale problems. A simple empirical Bayes approach allows the false discovery rate (fdr) analysis to proceed with a minimum of frequentist or Bayesian modeling assumptions. Closed-form accuracy formulas are derived for estimated false discovery rates, and used to compare different methodologies: local or tail-area fdr's, theoretical, permutation, or empirical null hypothesis estimates. Two microarray data sets as well as simulations are used to evaluate the methodology, the power diagnostics showing why nonnull cases might easily fail to appear on a list of ``significant'' discoveries.Comment: Published in at http://dx.doi.org/10.1214/009053606000001460 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Multiple testing procedures under confounding

While multiple testing procedures have been the focus of much statistical research, an important facet of the problem is how to deal with possible confounding. Procedures have been developed by authors in genetics and statistics. In this chapter, we relate these proposals. We propose two new multiple testing approaches within this framework. The first combines sensitivity analysis methods with false discovery rate estimation procedures. The second involves construction of shrinkage estimators that utilize the mixture model for multiple testing. The procedures are illustrated with applications to a gene expression profiling experiment in prostate cancer.Comment: Published in at http://dx.doi.org/10.1214/193940307000000176 the IMS Collections (http://www.imstat.org/publications/imscollections.htm) by the Institute of Mathematical Statistics (http://www.imstat.org

Serological profiles in nursery piglets colonized with Staphylococcus aureus

At present, the immune response of pigs in relation to Staphylococcus aureus carriage is poorly understood. This study aimed at investigating the dynamics of the anti-staphylococcal humoral immune response in methicillin-susceptible S. aureus (MSSA)-positive piglets and at assessing the effect of the experimental introduction of a methicillin-resistant S. aureus (MRSA) Sequence Type (ST) 398 strain. Therefore, serum samples were collected at different times from 31 weaned piglets originating from four different sows. Twenty-four out of the 31 piglets were challenged with MRSA ST398. The serum samples were analysed for IgG antibodies to 39 S. aureus antigens, using a multiplex bead-based assay (xMAP technology, Luminex Corporation). Though antibody responses showed broad inter-individual variability, serological results appeared to be clustered by litter of origin. For most antigens, an age-related response was observed with an apparent increase in antibody titres directed against staphylococcal microbial surface components recognizing adhesive matrix molecules (MSCRAMMs), which have been shown to play a role in S. aureus colonization. In most animals, antibody titres directed against staphylococcal toxins or immune-modulating proteins decreased with age, possibly reflecting absence of bacterial invasion. The introduction of MRSA ST398 did not elicit a significant humoral immune reaction. This study describes, for the first time, the humoral immune response in weaned pigs colonized with S. aureus

Biomarkers in solid organ transplantation: establishing personalized transplantation medicine.

Technological advances in molecular and in silico research have enabled significant progress towards personalized transplantation medicine. It is now possible to conduct comprehensive biomarker development studies of transplant organ pathologies, correlating genomic, transcriptomic and proteomic information from donor and recipient with clinical and histological phenotypes. Translation of these advances to the clinical setting will allow assessment of an individual patient's risk of allograft damage or accommodation. Transplantation biomarkers are needed for active monitoring of immunosuppression, to reduce patient morbidity, and to improve long-term allograft function and life expectancy. Here, we highlight recent pre- and post-transplantation biomarkers of acute and chronic allograft damage or adaptation, focusing on peripheral blood-based methodologies for non-invasive application. We then critically discuss current findings with respect to their future application in routine clinical transplantation medicine. Complement-system-associated SNPs present potential biomarkers that may be used to indicate the baseline risk for allograft damage prior to transplantation. The detection of antibodies against novel, non-HLA, MICA antigens, and the expression of cytokine genes and proteins and cytotoxicity-related genes have been correlated with allograft damage and are potential post-transplantation biomarkers indicating allograft damage at the molecular level, although these do not have clinical relevance yet. Several multi-gene expression-based biomarker panels have been identified that accurately predicted graft accommodation in liver transplant recipients and may be developed into a predictive biomarker assay

Novel translational approaches to the search for precision therapies for acute respiratory distress syndrome.

In the 50 years since acute respiratory distress syndrome (ARDS) was first described, substantial progress has been made in identifying the risk factors for and the pathogenic contributors to the syndrome and in characterising the protein expression patterns in plasma and bronchoalveolar lavage fluid from patients with ARDS. Despite this effort, however, pharmacological options for ARDS remain scarce. Frequently cited reasons for this absence of specific drug therapies include the heterogeneity of patients with ARDS, the potential for a differential response to drugs, and the possibility that the wrong targets have been studied. Advances in applied biomolecular technology and bioinformatics have enabled breakthroughs for other complex traits, such as cardiovascular disease or asthma, particularly when a precision medicine paradigm, wherein a biomarker or gene expression pattern indicates a patient's likelihood of responding to a treatment, has been pursued. In this Review, we consider the biological and analytical techniques that could facilitate a precision medicine approach for ARDS

Hawaiian Picture‐Winged Drosophila Exhibit Adaptive Population Divergence along a Narrow Climatic Gradient on Hawaii Island

1. Anthropogenic influences on global processes and climatic conditions are increasingly affecting ecosystems throughout the world. 2. Hawaii Island’s native ecosystems are well studied and local long‐term climatic trends well documented, making these ecosystems ideal for evaluating how native taxa may respond to a warming environment. 3.This study documents adaptive divergence of populations of a Hawaiian picture‐winged Drosophila, D. sproati, that are separated by only 7 km and 365 m in elevation. 4.Representative laboratory populations show divergent behavioral and physiological responses to an experimental low‐intensity increase in ambient temperature during maturation. The significant interaction of source population by temperature treatment for behavioral and physiological measurements indicates differential adaptation to temperature for the two populations. 5.Significant differences in gene expression among males were mostly explained by the source population, with eleven genes in males also showing a significant interaction of source population by temperature treatment. 6.The combined behavior, physiology, and gene expression differences between populations illustrate the potential for local adaptation to occur over a fine spatial scale and exemplify nuanced response to climate change

Identifying Three-Way Gene Interactions from Microarray Data using Kolmogorov-Smirnov and Cross-Match Tests

Human gene network is much more complex than just pairwise interaction among the genes. Zhang et al. [6] extracted microarray data from International Genomics Consortium (IGC), and presented the detection of three-way gene interactions in their paper using Fisher’s z-transformation test. Three-way gene interactions are closer than pairwise correlations in representing the complex gene structures. Additionally, it was more tractable than assessing four or more gene interactions. In this paper, we are simulating different models where Fisher’s test might not be as effective. Zhang et al.’s approach utilized Pearson’s correlation coefficients and involved detection of linear interactions only. Since gene interactions could show any kind of behavior, their evaluation approach might not work most of the time. Therefore, we are utilizing the dataset Zhang et al. provided in order to detect the three-way gene interaction using non-parametric tests like Kolmogorov-Smirnov and Cross-Match

A combined biomarker and clinical panel for chronic graft versus host disease diagnosis.

Whilst many chronic graft versus host disease (cGVHD) biomarkers have been previously reported, few have been verified in an independent cGVHD cohort. We aimed to verify the diagnostic accuracy of previously reported markers of cGVHD in a multi-centre Chronic GVHD Consortium. A total of 42 RNA and 18 protein candidate biomarkers were assessed amongst 59 cGVHD cases and 33 matched non-GVHD controls. Total RNA was isolated from PBMC, and RNA markers were quantified using PCR. Serum protein markers were quantified using ELISA. A combined 3 RNA biomarker (IRS2, PLEKHF1 and IL1R2) and 2 clinical variables (recipient CMV serostatus and conditioning regimen intensity) panel accurately (AUC 0.81) segregated cGVHD cases from controls. Other studied RNA and protein markers were not confirmed as accurate cGVHD diagnostic biomarkers. The studied markers failed to segregate higher risk cGVHD (per overall NIH 0-3 score, and overlap versus classic cGVHD status). These data support the need for multiple independent verification studies for the ultimate clinical application of cGVHD diagnostic biomarkers