Hariharan K. Iyer

The Landscape of Long Noncoding RNAs in the Human Transcriptome

Long noncoding RNAs (lncRNAs) are emerging as important regulators of tissue physiology and disease processes including cancer. To delineate genome-wide lncRNA expression, we curated 7,256 RNA sequencing (RNA-seq) libraries from tumors, normal tissues and cell lines comprising over 43 Tb of sequence from 25 independent studies. We applied ab initio assembly methodology to this data set, yielding a consensus human transcriptome of 91,013 expressed genes. Over 68% (58,648) of genes were classified as lncRNAs, of which 79% were previously unannotated. About 1% (597) of the lncRNAs harbored ultraconserved elements, and 7% (3,900) overlapped disease-associated SNPs. To prioritize lineage-specific, disease-associated lncRNA expression, we employed non-parametric differential expression testing and nominated 7,942 lineage- or cancer-associated lncRNA genes. The lncRNA landscape characterized here may shed light on normal biology and cancer pathogenesis and may be valuable for future biomarker development.

One- and Two-Sided Tolerance Intervals for General Balanced Mixed Models and Unbalanced One-Way Random Models

In this article we develop procedures for one- and two-sided tolerance intervals for normal general linear models in which there exists a set of independent scaled chi-squared random variables. The proposed procedures are based on the concept of generalized pivotal quantities and are applicable to general mixed models provided that balanced data are available. However, this study focuses on situations involving unbalanced data. Specific attention is given to the unbalanced one-way random model. It is shown that the use of generalized pivotal quantities allows the construction of the tolerance intervals of interest fairly straightforward. Some practical examples are given to illustrate the proposed procedures. Furthermore, detailed statistical simulation studies are conducted to evaluate their performance, showing that the proposed procedures can be recommended for practical use.

Detecting cumulative watershed effects: the statistical power of pairing

Abstract The statistical power for detecting change in water quality should be a primary consideration when designing monitoring studies. However, some of the standard approaches for estimating sample size result in a power of less than 50%, and doubling the pre- and post-treatment sample size are necessary to increase the power to 80%. The ability to detect change can be improved by including an additional explanatory variable such as paired watershed measurements. However, published guidelines have not explicitly quantified the benefits of including an explanatory variable or the specific conditions that favor a paired watershed design. This paper (1) presents a power analysis for the statistical model (analysis of covariance) commonly used in paired watershed studies; (2) discusses the conditions under which it is beneficial to include an explanatory variable; and (3) quantifies the benefits of the paired watershed design. The results show that it is beneficial to include an explanatory variable when its correlation to the water quality variable of concern is as low as about 0.3. The ability to detect change increases non-linearly as the correlation increases. Power curves quantify sample size requirements as a function of the correlation and intrinsic variability. In general, the temporal and spatial variability of many watershed-scale characteristics, such as annual sediment loads, makes it very difficult to detect changes within time spans that are useful for land managers or conducive to adaptive management.

Fiducial Approach to Uncertainty Assessment Accounting for Error due to Instrument Resolution

This paper presents an approach for making inference on the parameters µ and σ of a Gaussian distribution in the presence of resolution errors. The approach is based on the principle of fiducial inference and requires a Monte Carlo method for computing uncertainty intervals. A small simulation study is carried out to evaluate the performance of the proposed procedure and compare it with some of the existing procedures. The results indicate that the fiducial procedure is comparable to the best of the competing procedures for inference on µ. However, unlike some of the competing procedures, the same Monte Carlo calculations also provide inference for σ and many other related quantities of interest. (Some figures in this article are in colour only in the electronic version)

OPTIMAL DESIGNS FOR RATIOS OF LINEAR-COMBINATIONS IN THE GENERAL LINEAR-MODEL

Abstract Optimal designs are discussed for estimating a ratio of two linear combinations of the vector of parameters in the general linear model. Criteria are proposed based on minimization of the asymptotic variance of an often used estimator. The results are applied to specific problems in linear and quadratic regression.

A COMPARISON OF CONFIDENCE-REGIONS AND DESIGNS IN ESTIMATION OF A RATIO

A computer simulation is performed to compare confidence regions arising from Fie1lers theorem and approximate large sample intervals in estimating the point of extremum in quadratic regression. In addition, two designs, one of which is motivated by optimal design theory, are compared. These comparisons are made by examining the confidence level and accuracy of the regions, as well as the concentration of the standard point estimator, in a variety of settings. The results have implications for the more general problem of estimating a ratio of linear combinations in the general linear model.

Uncertainty analysis for vector measurands using fiducial inference

This paper presents a method for constructing uncertainty regions for a vector measurand in the presence of both type-A and type-B errors. The method is based on the principle of fiducial inference and generally requires a Monte Carlo approach for computing uncertainty regions. A small simulation study is carried out to evaluate the performance of the proposed method. Computer programs written using public-domain software for computing uncertainty regions are listed. An example, involving complex S-parameter measurements, is used to illustrate the proposed method.

Computation of finite population parameters and approximate probability values for multi-response randomized block permutation procedures (mrbp)

An algorithm is presented for computing the finite population parameters and the approximate probability values associated with a recently-developed class of statistical inference techniques termed multi-response randomized block permutation procedures (MRBP).

TACO produces robust multisample transcriptome assemblies from RNA-seq

Accurate transcript structure and abundance inference from RNA sequencing (RNA-seq) data is foundational for molecular discovery. Here we present TACO, a computational method to reconstruct a consensus transcriptome from multiple RNA-seq data sets. TACO employs novel change-point detection to demarcate transcript start and end sites, leading to improved reconstruction accuracy compared with other tools in its class. The tool is available at http://tacorna.github.io and can be readily incorporated into RNA-seq analysis workflows.

Fiducial approach for assessing agreement between two instruments

This paper presents an approach for making inferences about the intercept and slope of a linear regression model when both variables are subject to measurement errors. The approach is based on the principle of fiducial inference. A procedure is presented for computing uncertainty regions for the intercept and slope that can be used to assess agreement between two instruments. Computer codes for performing these calculations, written using open-source software, are listed.