Statistical methods for random rotations

The analysis of orientation data is a growing field in statistics. Though the rotationally symmetric location model for orientation data is simple, statistical methods for estimation and inference for the location parameter, S are limited. In this dissertation we develop point estimation and confidence region methods for the central orientation. Both extrinsic and intrinsic approaches to estimating the central orientation S have been proposed in the literature, but no rigorous comparison of the approaches is available. In Chapter 2 we consider both intrinsic and extrinsic estimators of the central orientation and compare their statistical properties in a simulation study. In particular we consider the projected mean, geometric mean and geometric median. In addition we introduce the projected median as a novel robust estimator of the location parameter. The results of a simulation study suggest the projected median is the preferred estimator because of its low bias and mean square error. Non-parametric confidence regions for the central orientation have been proposed in the literature, but they have undesirable coverage rates for small samples. In Chapter 3 we propose a nonparametric pivotal bootstrap to calibrate confidence regions for the central orientation. We demonstrate the benefits of using calibrated confidence regions in a simulation study and prove the proposed bootstrap method is consistent. Robust statistical methods for estimating the central orientation has received very little attention. In Chapter 4 we explore the finite sample and asymptotic properties of the projected median. In particular we derive the asymptotic distribution of the projected median and show it is SB-robust for the Cayley and matrix Fisher distributions. Confidence regions for the central orientation S are proposed, which can be shown to have preferable finite sample coverage rates compared to those based on the projected mean. Finally the rotations package is developed in Chapter 5, which contains functions for the statistical analysis of rotation data in SO(3)

rotations: An R Package for SO(3) Data

Abstract In this article we introduce the rotations package which provides users with the ability to simulate, analyze and visualize three-dimensional rotation data. More specifically it includes four commonly used distributions from which to simulate data, four estimators of the central orientation, six confidence region estimation procedures and two approaches to visualizing rotation data. All of these features are available for two different parameterizations of rotations: three-by-three matrices and quaternions. In addition, two datasets are included that illustrate the use of rotation data in practice

Evaluating the Impact of Spatial Ability in Virtual and Real World Environments

Survey agencies in the United States continue to move many map-based surveys from paper to handheld computers. With large highly diverse workforces, it is necessary to test software with a diverse population. The present work examines the performance of participants grouped by their level of spatial visualization. The participants were tested in either the field or in a fully immersive virtual environment. The methodology of the study is explained. The performance of the participants in the two environments is modeled with least squares regression. Results of the study are presented and discussed

Quality Control Analysis in Real-time (QC-ART) : A Tool for Real-time Quality Control Assessment of Mass Spectrometry-based Proteomics Data

Liquid chromatography-mass spectrometry (LC-MS)-based proteomics studies of large sample cohorts can easily require from months to years to complete. Acquiring consistent, high-quality data in such large-scale studies is challenging because of normal variations in instrumentation performance over time, as well as artifacts introduced by the samples themselves, such as those because of collection, storage and processing. Existing quality control methods for proteomics data primarily focus on post-hoc analysis to remove low-quality data that would degrade downstream statistics; they are not designed to evaluate the data in near real-time, which would allow for interventions as soon as deviations in data quality are detected. In addition to flagging analyses that demonstrate outlier behavior, evaluating how the data structure changes over time can aide in understanding typical instrument performance or identify issues such as a degradation in data quality because of the need for instrument cleaning and/or re-calibration. To address this gap for proteomics, we developed Quality Control Analysis in Real-Time (QC-ART), a tool for evaluating data as they are acquired to dynamically flag potential issues with instrument performance or sample quality. QC-ART has similar accuracy as standard post-hoc analysis methods with the additional benefit of real-time analysis. We demonstrate the utility and performance of QC-ART in identifying deviations in data quality because of both instrument and sample issues in near real-time for LC-MS-based plasma proteomics analyses of a sample subset of The Environmental Determinants of Diabetes in the Young cohort. We also present a case where QC-ART facilitated the identification of oxidative modifications, which are often underappreciated in proteomic experiments.Peer reviewe

When RON MET TAM in Mesothelioma: All Druggable for One, and One Drug for All?

Malignant pleural mesothelioma (MPM) is an aggressive inflammatory cancer with a poor survival rate. Treatment options are limited at best and drug resistance is common. Thus, there is an urgent need to identify novel therapeutic targets in this disease in order to improve patient outcomes and survival times. MST1R (RON) is a trans-membrane receptor tyrosine kinase (RTK), which is part of the c-MET proto-oncogene family. The only ligand recognized to bind MST1R (RON) is Macrophage Stimulating 1 (MST1), also known as Macrophage Stimulating Protein (MSP) or Hepatocyte Growth Factor-Like Protein (HGFL). In this study, we demonstrate that the MST1-MST1R (RON) signaling axis is active in MPM. Targeting this pathway with a small molecule inhibitor, LCRF-0004, resulted in decreased proliferation with a concomitant increase in apoptosis. Cell cycle progression was also affected. Recombinant MST1 treatment was unable to overcome the effect of LCRF-0004 in terms of either proliferation or apoptosis. Subsequently, the effect of an additional small molecular inhibitor, BMS-777607 (which targets MST1R (RON), MET, Tyro3, and Axl) also resulted in a decreased proliferative capacity of MPM cells. In a cohort of MPM patient samples, high positivity for total MST1R by IHC was an independent predictor of favorable prognosis. Additionally, elevated expression levels of MST1 also correlated with better survival. This study also determined the efficacy of LCRF-0004 and BMS-777607 in xenograft MPM models. Both LCRF-0004 and BMS-777607 demonstrated significant anti-tumor efficacy in vitro, however BMS-777607 was far superior to LCRF-0004. The in vivo and in vitro data generated by this study indicates that a multi-TKI, targeting the MST1R/MET/TAM signaling pathways, may provide a more effective therapeutic strategy for the treatment of MPM as opposed to targeting MST1R alone

Statistical methods for random rotations

The analysis of orientation data is a growing field in statistics. Though the rotationally symmetric location model for orientation data is simple, statistical methods for estimation and inference for the location parameter, S are limited. In this dissertation we develop point estimation and confidence region methods for the central orientation. Both extrinsic and intrinsic approaches to estimating the central orientation S have been proposed in the literature, but no rigorous comparison of the approaches is available. In Chapter 2 we consider both intrinsic and extrinsic estimators of the central orientation and compare their statistical properties in a simulation study. In particular we consider the projected mean, geometric mean and geometric median. In addition we introduce the projected median as a novel robust estimator of the location parameter. The results of a simulation study suggest the projected median is the preferred estimator because of its low bias and mean square error. Non-parametric confidence regions for the central orientation have been proposed in the literature, but they have undesirable coverage rates for small samples. In Chapter 3 we propose a nonparametric pivotal bootstrap to calibrate confidence regions for the central orientation. We demonstrate the benefits of using calibrated confidence regions in a simulation study and prove the proposed bootstrap method is consistent. Robust statistical methods for estimating the central orientation has received very little attention. In Chapter 4 we explore the finite sample and asymptotic properties of the projected median. In particular we derive the asymptotic distribution of the projected median and show it is SB-robust for the Cayley and matrix Fisher distributions. Confidence regions for the central orientation S are proposed, which can be shown to have preferable finite sample coverage rates compared to those based on the projected mean. Finally the rotations package is developed in Chapter 5, which contains functions for the statistical analysis of rotation data in SO(3).</p