Hari Iyer

Transcriptome sequencing across a prostate cancer cohort identifies PCAT-1 , an unannotated lincRNA implicated in disease progression

Noncoding RNAs (ncRNAs) are emerging as key molecules in human cancer, with the potential to serve as novel markers of disease and to reveal uncharacterized aspects of tumor biology. Here we discover 121 unannotated prostate cancer–associated ncRNA transcripts (PCATs) by ab initio assembly of high-throughput sequencing of polyA+ RNA (RNA-Seq) from a cohort of 102 prostate tissues and cells lines. We characterized one ncRNA, PCAT-1, as a prostate-specific regulator of cell proliferation and show that it is a target of the Polycomb Repressive Complex 2 (PRC2). We further found that patterns of PCAT-1 and PRC2 expression stratified patient tissues into molecular subtypes distinguished by expression signatures of PCAT-1–repressed target genes. Taken together, our findings suggest that PCAT-1 is a transcriptional repressor implicated in a subset of prostate cancer patients. These findings establish the utility of RNA-Seq to identify disease-associated ncRNAs that may improve the stratification of cancer subtypes.High-throughput sequencing of polyA+ RNA (RNA-Seq) in human cancer shows remarkable potential to identify both novel markers of disease and uncharacterized aspects of tumor biology, particularly non-coding RNA (ncRNA) species. We employed RNA-Seq on a cohort of 102 prostate tissues and cells lines and performed ab initio transcriptome assembly to discover unannotated ncRNAs. We nominated 121 such Prostate Cancer Associated Transcripts (PCATs) with cancer-specific expression patterns. Among these, we characterized PCAT-1 as a novel prostate-specific regulator of cell proliferation and target of the Polycomb Repressive Complex 2 (PRC2). We further found that high PCAT-1 and PRC2 expression stratified patient tissues into molecular subtypes distinguished by expression signatures of PCAT-1-repressed target genes. Taken together, the findings presented herein identify PCAT-1 as a novel transcriptional repressor implicated in subset of prostate cancer patients. These findings establish the utility of RNA-Seq to identify disease-associated ncRNAs that may improve the stratification of cancer subtypes.

Fiducial Generalized Confidence Intervals

Generalized pivotal quantities (GPQs) and generalized confidence intervals (GCIs) have proven to be useful tools for making inferences in many practical problems. Although GCIs are not guaranteed to have exact frequentist coverage, a number of published and unpublished simulation studies suggest that the coverage probabilities of such intervals are sufficiently close to their nominal value so as to be useful in practice. In this article we single out a subclass of generalized pivotal quantities, which we call fiducial generalized pivotal quantities (FGPQs), and show that under some mild conditions, GCIs constructed using FGPQs have correct frequentist coverage, at least asymptotically. We describe three general approaches for constructing FGPQs—a recipe based on invertible pivotal relationships, and two extensions of it—and demonstrate their usefulness by deriving some previously unknown GPQs and GCIs. It is fair to say that nearly every published GCI can be obtained using one of these recipes. As an interesting byproduct of our investigations, we note that the subfamily of fiducial generalized pivots has a close connection with fiducial inference proposed by R. A. Fisher. This is why we refer to the proposed generalized pivots as fiducial generalized pivotal quantities. We demonstrate these concepts using several examples.

Linear trend analysis: a comparison of methods

In this paper, we present an overview of statistical approaches available for detecting and estimating linear trends in environmental data. We evaluate seven methods of trend detection and make recommendations based on a simulation study. We also illustrate the methods using real data.

Models and Confidence Intervals for True Values in Interlaboratory Trials

We consider the one-way random-effects model with unequal sample sizes and heterogeneous variances. Using the method of generalized confidence intervals, we develop a new confidence interval procedure for the mean. Additionally, we investigate two alternative models based on different sets of assumptions regarding between-group variability and derive generalized confidence interval procedures for the mean. These procedures are applicable to small samples. Statistical simulation is used to demonstrate that the coverage probabilities of these procedures are close enough to the nominal value so that they are useful in practice. Although the methods are quite general, the procedures are explained with the backdrop of interlaboratory studies.

Permutation techniques for analyzing multi-response data from randomized block experiments

A class of permutation techniques is presented for the randomized block design. This class is specifically devised for analyses involving multivariate data. A numerical example illustrates an application based on multivariate data. Many well known techniques are special cases of this class. Among these special cases are (i) the permutation version of the classical univariate technique for randomized blocks which 1s associated with analysis of variance, (ii) the Friedman randomized block test, (iii) one-sample matched-pair tests, (iv) the Pearson correlation measure, and (v) the Spearman rank correlation and foot-rule measures. Furthermore, variations and multivariate versions among this class suggest a variety of new techniques which have not received any previous attention.

A twin methodology for the study of genetic and environmental control of variation in human smoking behavior.

A method is presented for partitioning the variance associated with human smoking behavior into additive genetic, nonadditive genetic, prenatal environmental, postnatal familial environmental, and postnatal extrafamilial environmental components. Estimations can also be made of additive genetic and residual correlations between spouses and of the correlation between parental additive genetic effect and progeny nonadditive genetic and environmental effect. The variance estimates are free of the biases that might result from these correlations. The statistical genetic analysis is being applied to a large group of MZ and DZ twins, their spouses, and their adult children who live in southern Sweden. Blood samples from each subject will be used to identify their genetic constitution for a number of biochemical polymorphisms, some of which may be considered a priori to have possible relationships to smoking. Associations and genetic linkages between biochemical marker loci and quantitative behavioral traits will be sought. Traits of interest include a wide array of tobacco-use variables, motives for smoking, personality and cognitive variables, and other variables associated with drug use and health. Zygosity determinations based on biochemical polymorphisms have indicated MZ to DZ and DZ to MZ misclassification rates of 0% and 6.15%, respectively, when based solely on external morphology and questionnaire data. The nonpaternity ratio of the fathers with respect to their supposedly biological children is estimated to be 0.28%. Gene frequency estimates for 21 marker loci show that the sample of twins and their relatives is quite representative of the Swedish population at large. All loci were in Hardy-Weinberg-Castle equilibrium, with no evidence of assortative mating for biochemical traits. The MZ twins are significantly more concordant than the DZ twins with respect to whether they have ever had a smoking habit.

Fisher's combined p-value for detecting differentially expressed genes using Affymetrix expression arrays

BackgroundCurrently, most tests of differential gene expression using Affymetrix expression array data are performed using expression summary values representing each probe set on a microarray. Recently testing methods have been proposed which incorporate probe level information. We propose a new approach that uses Fishers method of combining evidence from multiple sources of information. Specifically, we combine p-values from probe level tests of significance.ResultsThe combined p method and other competing methods were compared using three spike-in datasets (where probe sets corresponding differentially spiked transcripts are known) and array data from a biological study validated with qRT-PCR. Based on power and false discovery rates computed for the spike-in datasets, we demonstrate that the combined p method compares favorably with other methods. We find that probe level testing methods select many of the same genes as differentially expressed. We illustrate the use of the combined p method for diagnostic purposes using examples.ConclusionCombined p is a promising alternative to existing methods of testing for differential gene expression. In addition, the combined p method is particularly well suited as a diagnostic tool for exploratory analysis of microarray data.

Fiducial Intervals for Variance Components in an Unbalanced Two-Component Normal Mixed Linear Model

In this article we propose a new method for constructing confidence intervals for σα2,σϵ2, and the intraclass correlation ρ==σα2(σα2++σε2) in a two-component mixed-effects linear model. This method is based on an extension of R. A. Fishers fiducial argument. We conducted a simulation study to compare the resulting interval estimates with other competing confidence interval procedures from the literature. Our results demonstrate that the proposed fiducial intervals have satisfactory performance in terms of coverage probability, as well as shorter average confidence interval lengths overall. We also prove that these fiducial intervals have asymptotically exact frequentist coverage probability. The computations for the proposed procedures are illustrated using real data examples.

Tolerance intervals for the distribution of true values in the presence of measurement errors

A tolerance-interval procedure for the distribution N(θ, σ2 1 – σ2 2) is derived based on the mutually independent statistics , S 2 1, and S 2 2, which are distributed as follows: has a normal distribution with mean θ and variance σ2 1/n, n 1 S 2 1/σ2 1 is chi-squared with n 1, df, and n 2 S 2 1/σ2 2 is chi-squared with n 2, df. Application to the construction of tolerance intervals for the distribution of true values when observations are contaminated with measurement errors is explained. This includes the problem of constructing tolerance intervals in some growth-curve models. Numerical examples are given to illustrate the procedures.

Differentially expressed genes during malting and correlation with malting quality phenotypes in barley (Hordeum vulgare L.)

Breeding for malting quality is an important goal of malting barley breeding programs. Malting quality is a complex phenotype that combines a large number of interrelated components, each of which shows complex inheritance. Currently, only a few genes involved in determining malting quality have been characterized. We combined transcript profiling with phenotypic correlations to identify candidate genes for malting quality. The Barley1 GeneChip® array containing 22,792 probe sets was used to conduct transcript profiling of genes expressed in several different stages of malting of four malting cultivars. Genes that were differentially expressed in comparisons between different malting stages relative to ungerminated seed, as well as in comparisons between malting cultivars in the same malting stage were identified. Correlation analysis of 723 differentially expressed genes with malting quality phenotypes showed that 11–102 of these genes correlated with six malting quality phenotypes. Genes involved in carbohydrate metabolism were among the positively correlated genes. Genes for protein and lipid metabolism, cell wall organization and biogenesis, and genes involved in stress and defense response also correlated with malting quality phenotypes. Expressed sequence tags (ESTs) were generated from a ‘malting-gene enriched’ cDNA library made by suppression subtractive hybridization between malted and ungerminated seeds of ‘Morex’. Eleven percent of the ESTs had no significant homology with sequences in the databases, suggesting that there may be other malting-related genes not represented in the barley gene chip array. The results provide candidate genes for malting quality phenotypes that need to be functionally validated.