Medical Biomathematics and Biometrics Commons^™

Massively Parallel Nonparametric Regression, With An Application To Developmental Brain Mapping, Philip T. Reiss, Lei Huang, Yin-Hsiu Chen, Lan Huo, Thaddeus Tarpey, Maarten Mennes

Lei Huang

We propose a penalized spline approach to performing large numbers of parallel nonparametric analyses of either of two types: restricted likelihood ratio tests of a parametric regression model versus a general smooth alternative, and nonparametric regression. Compared with naively performing each analysis in turn, our techniques reduce computation time dramatically. Viewing the large collection of scatterplot smooths produced by our methods as functional data, we develop a clustering approach to summarize and visualize these results. Our approach is applicable to ultra-high-dimensional data, particularly data acquired by neuroimaging; we illustrate it with an analysis of developmental trajectories of functional connectivity at …

Global Quantitative Assessment Of The Colorectal Polyp Burden In Familial Adenomatous Polyposis Using A Web-Based Tool, Patrick M. Lynch, Jeffrey S. Morris, William A. Ross, Miguel A. Rodriguez-Bigas, Juan Posadas, Rossa Khalaf, Diane M. Weber, Valerie O. Sepeda, Bernard Levin, Imad Shureiqi

Jeffrey S. Morris

Background: Accurate measures of the total polyp burden in familial adenomatous polyposis (FAP) are lacking. Current assessment tools include polyp quantitation in limited-field photographs and qualitative total colorectal polyp burden by video.

Objective: To develop global quantitative tools of the FAP colorectal adenoma burden.

Design: A single-arm, phase II trial.

Patients: Twenty-seven patients with FAP.

Intervention: Treatment with celecoxib for 6 months, with before-treatment and after-treatment videos posted to an intranet with an interactive site for scoring.

Main Outcome Measurements: Global adenoma counts and sizes (grouped into categories: less than 2 mm, 2-4 mm, and greater than 4 mm) were …

Varying-Smoother Models For Functional Responses, Philip T. Reiss, Lei Huang, Huaihou Chen, Stan Colcombe

Philip T. Reiss

This paper studies estimation of a smooth function f(x,v) when we are given functional responses of the form f(x, ·) + error, but scientific interest centers on the collection of functions f(·,v) for different v. The motivation comes from studies of human brain development, in which x denotes age whereas v refers to brain locations. Analogously to varying-coefficient models, in which the mean response is linear in x, the “varying-smoother” models that we consider exhibit nonlinear dependence on x that varies smoothly with v. We discuss three approaches to estimating varying-smoother models: (a) methods that employ a tensor product penalty; …

Piscine Myocarditis Virus (Pmcv) In Wild Atlantic Salmon Salmo Salar, Torstein Tengs Dr.

Dr. Torstein Tengs

Cardiomyopathy syndrome (CMS) is a severe cardiac disease of sea-farmed Atlantic salmon Salmo salar L., but CMS-like lesions have also been found in wild Atlantic salmon. In 2010 a double-stranded RNA virus of the Totiviridae family, provisionally named piscine myocarditis virus (PMCV), was described as the causative agent of CMS. In the present paper we report the first detection of PMCV in wild Atlantic salmon. The study is based on screening of 797 wild Atlantic salmon by real-time RT-PCR. The samples were collected from 35 different rivers along the coast of Norway, and all individuals included in the study were …

Paradoxical Results Of Adaptive False Discovery Rate Procedures In Neuroimaging Studies, Philip T. Reiss, Armin Schwartzman, Feihan Lu, Lei Huang, Erika Proal

Philip T. Reiss

Adaptive false discovery rate (FDR) procedures, which offer greater power than the original FDR procedure of Benjamini and Hochberg, are often applied to statistical maps of the brain. When a large proportion of the null hypotheses are false, as in the case of widespread effects such as cortical thinning throughout much of the brain, adaptive FDR methods can surprisingly reject more null hypotheses than not accounting for multiple testing at all—i.e., using uncorrected p-values. A straightforward mathematical argument is presented to explain why this can occur with the q-value method of Storey and colleagues, and a simulation study shows that …

Prevalence Of Tick Borne Encephalitis Virus In Tick Nymphs In Relation To Climatic Factors On The Southern Coast Of Norway, Torstein Tengs Dr.

Dr. Torstein Tengs

BACKGROUND

Tick-borne encephalitis (TBE) is among the most important vector borne diseases of humans in Europe and is currently identified as a major health problem in many countries. TBE endemic zones have expanded over the past two decades, as well as the number of reported cases within endemic areas. Multiple factors are ascribed for the increased incidence of TBE, including climatic change. The number of TBE cases has also increased in Norway over the past decade, and the human cases cluster along the southern coast of Norway. In Norway the distribution and prevalence of TBE virus (TBEV) in tick populations …

Function-On-Scalar Regression With The Refund Package, Philip T. Reiss

Philip T. Reiss

No abstract provided.

A Strain Of Piscine Myocarditis Virus (Pmcv) Infecting Argentina Silus (Ascanius), Torstein Tengs Dr.

Dr. Torstein Tengs

No abstract.

Quantification Of Piscine Reovirus (Prv) At Different Stages Of Atlantic Salmon Salmo Salar Production, Torstein Tengs Dr.

Dr. Torstein Tengs

The newly described piscine reovirus (PRV) appears to be associated with the development of heart and skeletal muscle inflammation (HSMI) in farmed Atlantic salmon Salmo salar L. PRV seems to be ubiquitous among fish in Norwegian salmon farms, but high viral loads and tissue distribution support a causal relationship between virus and disease. In order to improve understanding of the distribution of PRV in the salmon production line, we quantified PRV by using real-time PCR on heart samples collected at different points in the life cycle from pre-smolts to fish ready for slaughter. PRV positive pre-smolts were found in about …

Smoothness Selection For Penalized Quantile Regression Splines, Philip T. Reiss, Lei Huang

Philip T. Reiss

Modern data-rich analyses may call for fitting a large number of nonparametric quantile regressions. For example, growth charts may be constructed for each of a collection of variables, to identify those for which individuals with a disorder tend to fall in the tails of their age-specific distribution; such variables might serve as developmental biomarkers. When such analyses are carried out by penalized spline smoothing, reliable automatic selection of the smoothing parameter is particularly important. We show that two popular methods for smoothness selection may tend to overfit when estimating extreme quantiles as a smooth function of a predictor such as …

Semiparametric Methods For Mapping Brain Development, Philip T. Reiss, Yin-Hsiu Chen, Lan Huo

Philip T. Reiss

No abstract provided.

Statistical Methods For Proteomic Biomarker Discovery Based On Feature Extraction Or Functional Modeling Approaches, Jeffrey S. Morris

Jeffrey S. Morris

In recent years, developments in molecular biotechnology have led to the increased promise of detecting and validating biomarkers, or molecular markers that relate to various biological or medical outcomes. Proteomics, the direct study of proteins in biological samples, plays an important role in the biomarker discovery process. These technologies produce complex, high dimensional functional and image data that present many analytical challenges that must be addressed properly for effective comparative proteomics studies that can yield potential biomarkers. Specific challenges include experimental design, preprocessing, feature extraction, and statistical analysis accounting for the inherent multiple testing issues. This paper reviews various computational …

Integrative Bayesian Analysis Of High-Dimensional Multi-Platform Genomics Data, Wenting Wang, Veerabhadran Baladandayuthapani, Jeffrey S. Morris, Bradley M. Broom, Ganiraju C. Manyam, Kim-Anh Do

Jeffrey S. Morris

Motivation: Analyzing data from multi-platform genomics experiments combined with patients’ clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current integration approaches that treat the data are limited in that they do not consider the fundamental biological relationships that exist among the data from platforms.

Statistical Model: We propose an integrative Bayesian analysis of genomics data (iBAG) framework for identifying important genes/biomarkers that are associated with clinical outcome. This framework uses a hierarchical modeling technique to combine the data obtained from multiple platforms …

James-Stein Estimation And The Benjamini-Hochberg Procedure, Debashis Ghosh

Debashis Ghosh

For the problem of multiple testing, the Benjamini-Hochberg (B-H) procedure has become a very popular method in applications. Based on a spacings theory representation of the B-H procedure, we are able to motivate the use of shrinkage estimators for modifying the B-H procedure. Several generalizations in the paper are discussed, and the methodology is applied to real and simulated datasets.

Shrinkage In Adaptive Procedures For False Discovery Rate Estimation In Multiple Testing: Structure And Synthesis, Debashis Ghosh

Debashis Ghosh

There has been much interest in the study of adaptive estimation procedures for controlling the false discovery rate (FDR). In this article, we take the direct approach to estimation of FDR of Storey (2002) and show how it can reexpressed as a particular type of shrinkage estimator. This representation leads to natural conditions on finite-sample FDR control for a general class of shrinkage estimators. In addition, many previous proposals from the literature can be unified under this framework for which finite-sample FDR results can be developed. Some asymptotic results are also provided.

Introducing Functional Data Analysis To Neuroimaging, And Vice Versa, Philip T. Reiss

Philip T. Reiss

No abstract provided.

The Use Of Imputed Values In The Meta-Analysis Of Genome-Wide Association Studies., Shuo Jiao, Li Hsu, Carolyn Hutter, Ulrike Peters

Shuo Jiao

In genome-wide association studies (GWAS), it is a common practice to impute the genotypes of untyped single nucleotide polymorphism (SNP) by exploiting the linkage disequilibrium structure among SNPs. The use of imputed genotypes improves genome coverage and makes it possible to perform meta-analysis combining results from studies genotyped on different platforms. A popular way of using imputed data is the "expectation-substitution" method, which treats the imputed dosage as if it were the true genotype. In current practice, the estimates given by the expectation-substitution method are usually combined using inverse variance weighting (IVM) scheme in meta-analysis. However, the IVM is not …

Massively Parallel Nonparametrics [Hds 2011 Slides], Philip T. Reiss, Lei Huang

Philip T. Reiss

No abstract provided.

Flexible Dependence Of Functional Responses On Scalar Predictors, Philip T. Reiss, Lei Huang

Philip T. Reiss

No abstract provided.

Prevalence Of Piscine Myocarditis Virus (Pmcv) In Marine Fish Species, Torstein Tengs Dr.

Dr. Torstein Tengs

No abstract.

Generalized Benjamini-Hochberg Procedures Using Spacings, Debashis Ghosh

Debashis Ghosh

For the problem of multiple testing, the Benjamini-Hochberg (B-H) procedure has become a very popular method in applications. We show how the B-H procedure can be interpreted as a test based on the spacings corresponding to the p-value distributions. Using this equivalence, we develop a class of generalized B-H procedures that maintain control of the false discovery rate in finite-samples. We also consider the effect of correlation on the procedure; simulation studies are used to illustrate the methodology.

Software For Assumption Weighting For Meta-Analysis Of Genomic Data, Debashis Ghosh, Yihan Li

Debashis Ghosh

This is the software that accompanies Li and Ghosh, "Assumption weighting for incorporating heterogeneity into meta-analysis of genomic data."

A Causal Framework For Surrogate Endpoints With Semi-Competing Risks Data, Debashis Ghosh

Debashis Ghosh

In this note, we address the problem of surrogacy using a causal modelling framework that differs substantially from the potential outcomes model that pervades the biostatistical literature. The framework comes from econometrics and conceptualizes direct effects of the surrogate endpoint on the true endpoint. While this framework can incorporate the so-called semi-competing risks data structure, we also derive a fundamental non-identifiability result. Relationships to existing causal modelling frameworks are also discussed.

Propensity Score Modelling In Observational Studies Using Dimension Reduction Methods, Debashis Ghosh

Debashis Ghosh

Conditional independence assumptions are very important in causal inference modelling as well as in dimension reduction methodologies. These are two very strikingly different statistical literatures, and we study links between the two in this article. The concept of covariate sufficiency plays an important role, and we provide theoretical justication when dimension reduction and partial least squares methods will allow for valid causal inference to be performed. The methods are illustrated with application to a medical study and to simulated data.

Extracting Information From Functional Connectivity Maps Via Function-On-Scalar Regression, Philip T. Reiss, Maarten Mennes, Eva Petkova, Lei Huang, Matthew J. Hoptman, Bharat B. Biswal, Stanley J. Colcombe, Xi-Nian Zuo, Michael P. Milham

Lei Huang

Functional connectivity of an individual human brain is often studied by acquiring a resting state functional magnetic resonance imaging scan, and mapping the correlation of each voxel's BOLD time series with that of a seed region. As large collections of such maps become available, including multisite data sets, there is an increasing need for ways to distill the information in these maps in a readily visualized form. Here we propose a two-step analytic strategy. First, we construct connectivity-distance profiles, which summarize the connectivity of each voxel in the brain as a function of distance from the seed, a functional relationship …

Extracting Information From Functional Connectivity Maps Via Function-On-Scalar Regression, Philip T. Reiss, Maarten Mennes, Eva Petkova, Lei Huang, Matthew J. Hoptman, Bharat B. Biswal, Stanley J. Colcombe, Xi-Nian Zuo, Michael P. Milham

Philip T. Reiss

Functional connectivity of an individual human brain is often studied by acquiring a resting state functional magnetic resonance imaging scan, and mapping the correlation of each voxel's BOLD time series with that of a seed region. As large collections of such maps become available, including multisite data sets, there is an increasing need for ways to distill the information in these maps in a readily visualized form. Here we propose a two-step analytic strategy. First, we construct connectivity-distance profiles, which summarize the connectivity of each voxel in the brain as a function of distance from the seed, a functional relationship …

A Novel Totivirus And Piscine Reovirus (Prv) In Atlantic Salmon (Salmo Salar) With Cardiomyopathy Syndrome (Cms), Torstein Tengs

Dr. Torstein Tengs

BACKGROUNDCardiomyopathy syndrome (CMS) is a severe disease affecting large farmed Atlantic salmon. Mortality often appears without prior clinical signs, typically shortly prior to slaughter. We recently reported the finding and the complete genomic sequence of a novel piscine reovirus (PRV), which is associated with another cardiac disease in Atlantic salmon; heart and skeletal muscle inflammation (HSMI). In the present work we have studied whether PRV or other infectious agents may be involved in the etiology of CMS.RESULTSUsing high throughput sequencing on heart samples from natural outbreaks of CMS and from fish experimentally challenged with material from fish diagnosed with CMS …

Fast, Flexible Function-On-Scalar Regression, With An Application To Brain Development, Philip T. Reiss, Lei Huang

Philip T. Reiss

No abstract provided.

Heart And Skeletal Muscle Inflammation Of Farmed Salmon Is Associated With Infection With A Novel Reovirus, Torstein Tengs

Dr. Torstein Tengs

Atlantic salmon (Salmo salar L.) mariculture has been associated with epidemics of infectious diseases that threaten not only local production, but also wild fish coming into close proximity to marine pens and fish escaping from them. Heart and skeletal muscle inflammation (HSMI) is a frequently fatal disease of farmed Atlantic salmon. First recognized in one farm in Norway in 1999, HSMI was subsequently implicated in outbreaks in other farms in Norway and the United Kingdom. Although pathology and disease transmission studies indicated an infectious basis, efforts to identify an agent were unsuccessful. Here we provide evidence that HSMI is associated …

Non-Prejudiced Detection And Characterization Of Genetic Modifications, Torstein Tengs

Dr. Torstein Tengs

The application of gene technology is becoming widespread much thanks to the rapid increase in technology, resource, and knowledge availability. Consequently, the diversity and number of genetically modified organisms (GMOs) that may find their way into the food chain or the environment, intended or unintended, is rapidly growing. From a safety point of view the ability to detect and characterize in detail any GMO, independent of publicly available information, is fundamental. Pre-release risk assessments of GMOs are required in most jurisdictions and are usually based on application of technologies with limited ability to detect unexpected rearrangements and insertions. We present …