Gatekeeping testing via adaptive alpha allocation

In a typical clinical trial, there are one or two primary endpoints, and a few secondary endpoints. When at least one primary endpoint achieves statistical significance, there is considerable interest in using results for the secondary endpoints to enhance characterization of the treatment effect. Because multiple endpoints are involved, regulators may require that the familywise type I error rate be controlled at a pre-set level. This requirement can be achieved by using "gatekeeping" methods. However, existing methods suffer from logical oddities such as allowing results for secondary endpoint(s) to impact the likelihood of success for the primary endpoint(s). We propose a novel and easy-to-implement gatekeeping procedure that is devoid of such deficiencies. A real data example and simulation results are used to illustrate efficiency gains of our method relative to existing methods.     

Bonferroni parallel gatekeeping - transparent generalizations, adjusted p -values, and short direct proofs

In the two-step version (Dmitrienko, Tamhane, Wang and Chen, 2006) of the Bonferroni parallel-gatekeeping multiple-testing procedure (MTP): (a) a family F1 of null hypotheses H is used as a gatekeeper for another family F2 in that no H in F2 can be rejected unless at least one H is rejected in F1; (b) a Bonferroni MTP is used for F1 at local multiple-level alpha in the first step; and (c) Holm's (1979) step-down MTP is used in the second step for F2 at a local multiple level that depends on the rejections made in the first step. It is shown in this article that this two-step procedure can be generalized in that any MTP with multiple-level control and available multiplicity-adjusted p -values can be used instead of Holm's MTP in the second step. A further generalization related to what Dmitrienko, Molenberghs, Chuang-Stein and Offen (2005) called modified Bonferroni parallel gatekeeping is also given where in case all H s in F2 are rejected, additional rejections in F1 can be made in a third step at local multiple-level alpha through any MTP that is more powerful than the initial Bonferroni MTP, e.g. Holm's MTP. The proofs that these two generalized Bonferroni parallel-gatekeeping MTPs have multiple-level alpha are short and direct, without closed-testing arguments. Multiplicity-adjusted p -values can easily be calculated for these MTPs. The extensions to several successive gatekeeper families are straightforward. An illustration is given.     

A renewed approach to the nonparametric analysis of replicated microarray experiments

DNA-microarrays find broad employment in biochemical research. This technology allows the monitoring of the expression levels of thousands of genes at the same time. Often, the goal of a microarray study is to find differentially expressed genes in two different types of tissue, for example normal and cancerous. Multiple hypothesis testing is a useful statistical tool for such studies. One approach using multiple hypothesis testing is nonparametric analysis for replicated microarray experiments. In this paper we present an improved version of this method. We also show how p-values are calculated for all significant genes detected with this testing procedure. All algorithms were implemented in an R-package, and instructions on it's use are included. The package can be downloaded at http://www.statistik.unidortmund.de/de/content/einrichtungen/lehrstuehle/personen/jung.html     

A simple,flexible and high‐throughput cloning system for plant genome editing via CRISPR‐Cas system

CRISPR-Cas9 system is now widely used to edit a target genome in animals and plants. Cas9 protein derived from Streptococcus pyogenes(Sp Cas9) cleaves double-stranded DNA targeted by a chimeric single-guide RNA(sg RNA). For plant genome editing, Agrobacterium-mediated T-DNA transformation has been broadly used to express Cas9 proteins and sg RNAs under the control of Ca MV 35 S and U6/U3 promoter, respectively. We here developed a simple and high-throughput binary vector system to clone a 19 20 bp of sg RNA, which binds to the reverse complement of a target locus, in a large T-DNA binary vector containing an Sp Cas9 expressing cassette. Twostep cloning procedures:(1) annealing two target-specific oligonucleotides with overhangs specific to the Aar I restriction enzyme site of the binary vector; and(2) ligating the annealed oligonucleotides into the two Aar I sites of the vector, facilitate the high-throughput production of the positive clones. In addition, Cas9-coding sequence and U6/U3 promoter can be easily exchanged via the GatewayTMsystem and unique Eco RI/Xho I sites on the vector, respectively. We examined the mutation ratio and patterns when we transformed these constructs into Arabidopsis thaliana and a wild tobacco, Nicotiana attenuata. Our vector system will be useful to generate targeted large-scale knock-out lines of model as well as non-model plant.     

A simple procedure for joint estimation of the long‐term rates of sexuality and mutation in predominantly clonal populations,for use with dominant molecular markers

In highly clonal populations, mutation can contribute to the rate of apparent sexuality. To remove this bias, a method is presented that jointly estimates the rates of sexuality (Ns) and mutation (Nµ) for populations, based upon levels of single‐locus vs. multilocus clonal identity. This effectively haploid model assumes equilibrium, and can be used with dominant molecular data and in conjunction with organisms of various ploidies. Simulations indicate that while equilibrium can take thousands of generations to attain, and afterwards have a large evolutionary variance, the method gives approximate estimates of Ns.     

A flexible cure rate model for spatially correlated survival data based on generalized extreme value distribution and Gaussian process priors

Our present work proposes a new survival model in a Bayesian context to analyze right‐censored survival data for populations with a surviving fraction, assuming that the log failure time follows a generalized extreme value distribution. Many applications require a more flexible modeling of covariate information than a simple linear or parametric form for all covariate effects. It is also necessary to include the spatial variation in the model, since it is sometimes unexplained by the covariates considered in the analysis. Therefore, the nonlinear covariate effects and the spatial effects are incorporated into the systematic component of our model. Gaussian processes (GPs) provide a natural framework for modeling potentially nonlinear relationship and have recently become extremely powerful in nonlinear regression. Our proposed model adopts a semiparametric Bayesian approach by imposing a GP prior on the nonlinear structure of continuous covariate. With the consideration of data availability and computational complexity, the conditionally autoregressive distribution is placed on the region‐specific frailties to handle spatial correlation. The flexibility and gains of our proposed model are illustrated through analyses of simulated data examples as well as a dataset involving a colon cancer clinical trial from the state of Iowa.     

A rapid and simple procedure for the depletion of abundant storage proteins from legume seeds to advance proteome analysis: A case study using Glycine max

2‐D analysis of plant proteomes containing thousands of proteins has limited dynamic resolution because only abundant proteins can be detected. Proteomic assessment of the non‐abundant proteins within seeds is difficult when 60–80% is storage proteins. Resolution can be improved through sample fractionation using separation techniques based upon different physiological or biochemical principles. We have developed a fast and simple fractionation technique using 10 mM Ca²⁺ to precipitate soybean (Glycine max) seed storage globulins, glycinin and β‐conglycinin. This method removes 87±4% of the highly abundant seed proteins from the extract, allowing for 541 previously inconspicuous proteins present in soybean seed to be more detectable (volume increase of ≥50%) using fluorescent detection. Of those 541 enhanced spots, 197 increased more than 2.5‐fold when visualized with Coomassie. The majority of those spots were isolated and identified using peptide mass fingerprinting. Fractionation also provided detection of 63 new phosphorylated protein spots and enhanced the visibility of 15 phosphorylated protein spots, using 2‐D electrophoretic separation and an in‐gel phosphoprotein stain. Application of this methodology toward other legumes, such as peanut, bean, pea, alfalfa and others, also containing high amounts of storage proteins, was examined, and is reported here.     

Resampling-based empirical Bayes multiple testing procedures for controlling generalized tail probability and expected value error rates: focus on the false discovery rate and simulation study

This article proposes resampling-based empirical Bayes multiple testing procedures for controlling a broad class of Type I error rates, defined as generalized tail probability (gTP) error rates, gTP (q,g) = Pr(g (V(n),S(n)) > q), and generalized expected value (gEV) error rates, gEV (g) = E [g (V(n),S(n))], for arbitrary functions g (V(n),S(n)) of the numbers of false positives V(n) and true positives S(n). Of particular interest are error rates based on the proportion g (V(n),S(n)) = V(n) /(V(n) + S(n)) of Type I errors among the rejected hypotheses, such as the false discovery rate (FDR), FDR = E [V(n) /(V(n) + S(n))]. The proposed procedures offer several advantages over existing methods. They provide Type I error control for general data generating distributions, with arbitrary dependence structures among variables. Gains in power are achieved by deriving rejection regions based on guessed sets of true null hypotheses and null test statistics randomly sampled from joint distributions that account for the dependence structure of the data. The Type I error and power properties of an FDR-controlling version of the resampling-based empirical Bayes approach are investigated and compared to those of widely-used FDR-controlling linear step-up procedures in a simulation study. The Type I error and power trade-off achieved by the empirical Bayes procedures under a variety of testing scenarios allows this approach to be competitive with or outperform the Storey and Tibshirani (2003) linear step-up procedure, as an alternative to the classical Benjamini and Hochberg (1995) procedure.     

A simple shoot multiplication procedure using internode explants,and its application for particle bombardment and <Emphasis Type="Italic">Agrobacterium</Emphasis>-mediated transformation in watercress

A shoot multiplication system derived from internode explants was investigated with the aim of improving genetic characteristics of watercress (Nasturtium officinale R. Br.). Internodes of ca. 1 cm excised from in vitro stock shoot culture were placed on half-strength Murashige and Skoog (MS) medium supplemented with 3 μM 2,4-dichlorophenoxyacetic acid as a pre-treatment. Laser scanning microscopy indicated clearly that the first sign of meristematic cell division could be seen after 1–2 days of pre-culture, and meristematic tissues multiplied along the vascular cambium of the internode segment during 7 days of culture. Multiple shoots could be obtained from more than 90% of the pre-treated explants when they were subsequently transferred to MS medium supplemented with 1 μM thidiazuron for 3 weeks. These findings indicate that pre-treatment of the internodes for 7 days promoted their capacity for organogenesis. Using this pre-treatment, frequent generation of transgenic watercress plants was achieved by adapting particle bombardment and Agrobacterium-mediated transformation techniques with a construct expressing a synthetic green florescent protein gene.     

A Survey of Sample Size Formulas for Pairwise and Many‐one Multiple Comparisons in the Parametric,Nonparametric and Binomial Case

We present a survey of sample size formulas derived in other papers for pairwise comparisons of k treatments and for comparisons of k treatments with a control. We consider the calculation of sample sizes with preassigned per‐pair, any‐pair and all‐pairs power for tests that control either the comparisonwise or the experimentwise type I error rate. A comparison exhibits interesting similarities between the parametric, nonparametric and binomial case.